Search

Erectile dysfunction (ED) is a major threat to male fertility and quality of life, and mesenchymal stromal cells (MSCs) are a promising therapeutic option. However, therapeutic outcomes are compromised by low MSC retention and survival rates in corpus cavernosum tissue. Here, we developed an innovative magnetic soft microrobot comprising an ultrasoft hydrogel microsphere embedded with a magnetic nanoparticle chain for MSC delivery. This design also features phenylboronic acid groups for scavenging reactive oxygen species (ROS). With a Young's modulus of less than 1 kPa, the ultrasoft microrobot adapts its shape within narrow blood vessels, ensuring a uniform distribution of MSCs within the corpus cavernosum. Our findings showed that compared with traditional MSC injections, the MSC delivery microrobot (MSC-Rob) significantly enhanced MSC retention and survival. In both rat and beagle ED models, MSC-Rob treatment accelerated the repair of corpus cavernosum tissue and restored erectile function. Single-cell RNA sequencing (scRNA-seq) revealed that MSC-Rob treatment facilitates nerve and blood vessel regeneration in the corpus cavernosum by increasing the presence of regenerative macrophages. Overall, our MSC-Rob not only advances the clinical application of MSCs for ED therapy but also broadens the scope of microrobots for other cell therapies., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: Technology has the potential to prevent patient falls in healthcare settings and to reduce work-related injuries among healthcare providers. However, the usefulness and acceptability of each technology requires careful evaluation. Framed by the Technology Acceptance Model (TAM) and using the Adaptive Robotic Nursing Assistant (ARNA) to assist with patient ambulation, the present study examined the perceived usefulness of robots in patients' fall prevention with implications for preventing associated work-related injuries among healthcare providers., Methods: Employing an experimental design, subjects were undergraduate nursing students ( N = 38) and one external subject (not a nursing student) who played the role of the patient. Procedures included subjects ambulating a simulated patient in three ways: (a) following the practice of a nurse assisting a patient to walk with the patient wearing a gait belt; (b) an ARNA-assisted process with the gait belt attached to ARNA; (c) an ARNA-assisted process with a subject walking a patient wearing a harness that is attached to ARNA. Block randomization was used with the following experimental scenarios: Gait Belt (human with a gait belt), "ARNA + Gait Belt" (a robot with a gait belt), and "ARNA + Harness" (a robot with a harness). Descriptive statistics and a multiple regression model were used to analyze the data and compare the outcome described as the Perceived Usefulness (PU) of a robot for patient walking versus a human "nurse assistant" without a robot. The independent variables included the experimental conditions of "Gait Belt," "ARNA + Gait Belt," and "ARNA + Harness," the subject's age, race, and previous videogame playing experience., Findings: Results indicated that PU was significantly higher in the Gait Belt + ARNA and Harness + ARNA conditions than in the Gait Belt condition ( p -value <.01 for both variables). In examining potential influencing factors, the effects of race (White, African American, and Asian), age, and previous video-playing experience were not statistically significant ( p -value >.05)., Discussion: Results demonstrated that using robot technology to assist in walking patients was perceived by subjects as more useful in preventing falls than the gait belt. Patient fall prevention also has implications for preventing associated work-related injuries among healthcare providers., Implications: Understanding the effects of a subject's perceptions can guide further development of assistive robots in patient care. Robotic engineers and interdisciplinary teams can design robots to accommodate worker characteristics and individual differences to improve worker safety and reduce work injuries., Competing Interests: Conflict of InterestThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

This scoping review synthesises the current research into robotics technologies for promoting social-emotional learning in children with autism spectrum disorder. It examines the types of robotics technologies employed, their applications, and the gaps in the existing literature. Our scoping review adhered to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines. The systematic search of relevant databases allowed us to identify studies that use robotics technologies for fostering social, emotional, and cognitive skills in young children with autism. Our review has revealed that various robots, such as Nao, Kaspar, and Zeno, have been used to support the development of social and emotional skills through imitation games, turn-taking, joint attention, emotional recognition, and conversation. As most of these studies were conducted in clinical settings, there is a need for further research in classroom and community-based environments. Additionally, the literature calls for more high-quality longitudinal studies to assess the long-term effectiveness and sustainability of robot-assisted therapy and to assess adaptive and personalised interventions tailored to individual needs. More emphasis is recommended on professional development for educators, parents, and health professionals to incorporate robotics technologies as evidence-based interventions as a pathway for creating inclusive learning environments for children with autism., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Since 1960, Williams and Trotman had dreamed of automating all technical manipulations in bacteriology. However, this switch to automation took several decades to realize. The high cost of instruments and the attachment to classical bacteriology were the main obstacles. Automation began with blood culture incubators, and paved the way for automation in other areas of bacteriology, notably cytology, culture, identification and antibiotic susceptibility testing. Medical laboratories have been quick to recognize the efficiency of these systems and their many advantages. The reduction in turnaround times for bacteriological examinations is one of the changes that have revolutionized laboratory practice. In addition, sensitivity, safety, traceability and quality are more assured with automation. The second revolution is the integration of artificial intelligence into the processing and interpretation of bacteriological analyses. We are currently witnessing the total automation of laboratories and a reduction in human intervention. In this article, we have attempted to address all aspects of bacteriology affected by automation, and the impact of this change on current laboratory practice and quality of healthcare.

This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to create a dialogue robot that offers pleasant experiences for users, so they are willing to engage in repeated interactions in daily lives. While there has been dramatic progress in the performance of dialogue robots, there has been less attention to the importance of users' interactional experience compared to the "specs" of the dialogue system. Employing Goffmanian insights and using research in Conversation Analysis (CA), the present study develops a dialogue closing system to exit the interaction. We then experimentally verified that the robot with the dialogue closing system performs better in the user's perception of the robot (i.e. likeability, politeness, and dialogue satisfaction) than the control group. Further, by analyzing the dialogue between the human and the robot through CA, we propose to build a recursive, reflective model to improve the dialogue model design. A constructive approach urges us to reproduce complicated social phenomena in human-robot interaction so that we can investigate the underlying cognitive mechanisms of humans and create robots that can convey human-like cognition functions and coexist with humans. Taking such a constructive approach, we posit that our recursive model for dialogue systems that uses CA insights and then qualitatively analyzes conversational data can enhance the quality of dialogue systems because the model elucidates which properties of a conversation humans need to experience a conversational robot as human-like. Our study suggests that interactional morality - particularly conversational closings - is one property of human interactions that humans likely require social robots to adhere to., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Background: We evaluated the feasibility, safety, and efficacy of a 2D-planar robot for minimally supervised home-based upper-limb therapy for post-stroke hemiparesis., Methods: The H-Man, end effector robot, combined with web-based software application for remote tele-monitoring were evaluated at homes of participants. Inclusion criteria were: strokes > 28 days, Fugl-Meyer Motor Assessment (FMA) > 10-60/66, presence of a carer and absence of medical contraindications. Participants performed self-directed, minimally supervised robotics-assisted therapy (RAT) at home for 30 consecutive days, after 2 therapist-supervised clinic on-boarding sessions. Web-based compliance measures were: accessed sessions of > 20 min/day, training minutes/day and active training hours/30 days. Clinical outcomes at weeks 0, 5 (post-training), 12 and 24 (follow-up) consisted of FMA, Action Research Arm Test (ARAT) and WHO-Stroke Specific Quality of Life (SSQOL). To estimate immediate economic benefits of the home-based robotic therapy, we performed cost-effectiveness analysis (CEA), followed by budget impact analysis (BIA)., Results: Altogether, all 12 participants completed Home-RAT without adverse events; 9 (75.0%) were males, mean (SD) age, 59.4 years (9.5), median (IQR) stroke duration 38.6 weeks (25.4, 79.6) baseline FMA (0-66) 42.1 ± 13.2, ARAT (0-57) 25.4 ± 19.5, SSQOL (0-245) 185.3 ± 32.8. At week 5 follow-up, mean (SD) accessed days were 26.3 days ± 6.4, active training hours of 35.3 h ± 14.7/30 days, or ~ 6 days/week and 77 training minutes ± 20.9/day were observed. Significant gains were observed from baseline across time; ΔFMA 2.4 at week 5 (FMA 44.5, CI 95% 39.7-49.3, p < 0.05) and ΔFMA 3.7 at week 24 (FMA 45.8, CI 95% 40.5-51, p < 0.05); ΔARAT 2.6 at week 5 (ARAT 28.0, CI 95% 19.3-36.7, p < 0.05), and ΔARAT 4.8 at week 24 (ARAT 30.2, CI 95% 21.2-39.1, p < 0.05). At week 5 follow-up, 91% of participants rated their overall experience as satisfied or very satisfied. Incremental CEA observed savings of -S$144/per cure over 24 weeks, BIA-potentially 12% impact reduction over five years., Conclusions: This study demonstrates the feasibility, acceptability, safety, clinical efficacy, and cost-effectiveness of a home-based, web-enabled telemonitored carer-supervised robotics-aided therapy., Trial Registration: NCT05212181  ( https://clinicaltrials.gov )., Competing Interests: Declarations. Ethics approval and consent to participate: Institutional review board approvals were obtained from National Healthcare Group (NHG 2021/00156). All participants gave written informed consent. Consent for publication: Participant gave written consent for image in Fig. 1B. Competing interests: KSGC and CWKK both declare interests that they were not involved in participant screening, informed consent, participant recruitment, data analysis nor data safety monitoring due to their co-sharing royalties from H-Man co-development. AH declares interest as co-founder and CEO of the company leading the development of H-Man and was not involved in the concept, conduct of trial, data monitoring or analysis of results. The following authors have no competing interests: GAO, PLO, TKP, CYN, LWK, KHG, WBC, JAML, MM, TS, SK, HJC., (© 2024. The Author(s).)

Background: Robot-assisted rehabilitation is considered beneficial for functional recovery in patients with stroke, but the therapeutic effect remains inconclusive. The present study investigated the therapeutic effects of gait training assisted by a user-initiated powered exoskeletal robot (UIPER) in patients in the early stage after stroke. We also characterized patients' improvement by analyzing chronological changes in clinical measurements together with gait parameters obtained from internal sensors in the exoskeletal robot., Methods: In this pilot case-controlled study, 17 and 81 patients with stroke onset durations of < 3 months were included in the robot-assisted combined with conventional treatment (RT + CT) group and conventional treatment only (CT) group, respectively. The UIPER, which provides knee flexion and extension support and has hip and knee sensors, was applied to guide gait performance in the RT + CT group. The patients in the RT + CT group received robot-assisted gait training for 40 min/ session, 1 session a day, and 2-3 sessions a week (6 sessions in total). The primary outcome was the proportion of patients reaching the minimum clinically important difference (MCID) in the 5-meter walking speed (5MWS) assessment, and the secondary outcomes were the MCID for the six-minute walking test, the Berg Balance Scale, the Barthel Index, the Fugl-Meyer assessment, and the timed up and go test before, during, and after the interventions. Gait parameters of the hip and knee were evaluated at baseline, midterm, and final sessions., Results: Gait function improved in both groups after the intervention (both P < 0.05). The primary outcome showed that a greater proportion of patients reached the MCID for the 5MWS in the RT + CT group than in the CT group (70.6 vs. 43.2%, P = 0.040; [Formula: see text] = 0.208). Similarly, in terms of the secondary outcomes, more patients in the RT + CT group reached the MCID for the Barthel index as compared with the CT group (41.2 vs. 17.3%, P = 0.047, [Formula: see text] = 0.220). Gait analysis revealed improvements in gait in the RT + CT group, as indicated by increases in the perimeter and area of the hip-knee cyclogram, as well as the range of motion in the hip joint., Conclusions: Gait training assisted by UIPER facilitates the recovery of walking speed and activities of daily living in patients with stroke, and these improvements may be related to improvements in gait parameters. Randomized controlled studies with larger sample sizes are needed to confirm these findings., Trial Registration: This trial was approved by the Institutional Review Board of Chang Gung Medical Foundation (No. 202200822B0)., Competing Interests: Declarations. Ethics approval and consent to participate: This study was approved by the Institutional Review Board of Chang Gung Medical Foundation (No. 202200822B0) on 11th July 2022 and all participants in the present study provided informed consent. Consent for publication: All participants in the present study received instruction about the experimental procedures and content and the experiments were conducted after participants had completed the consent forms. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The robotic fish utilizes a bio-inspired undulatory propulsion system to achieve high swimming performance. However, significant roll motion has been observed at the head when the tail oscillates at certain frequencies, adversely affecting both perception accuracy and propulsion efficiency. In this paper, the roll torque acting on the robotic fish is theoretically analyzed and decomposed into gravitational, inertial, and hydrodynamic components. Resonance is identified as a key factor amplifying the roll response. To mitigate this roll and enhance stability, a passive roll absorber based on tuned mass damper is designed. A simplified rolling structure is dynamically modeled to optimize absorber parameters. Experiments are conducted to quantify the roll torque experienced by the robotic fish, with the effectiveness of the absorber verified on both the simplified model and the robotic fish. Results show that the maximum roll angle of the simplified system under harmonic load decreases from 98° to29∘, representing a reduction of over 70%, while a 25.1% reduction is achieved on the robotic fish., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Endoluminal and endocavitary intervention via natural orifices of the body is an emerging trend in medicine, further underpinning the future of early intervention and precision surgery. This motivates the development of small continuum robots to navigate freely in confined and tortuous environment. The trade-off between a large range of motion and high precision with concomitant actuation cross-talk poses a major challenge. Here, we present a submillimeter-scale fiber robot (~1 mm) capable of decoupled macro and micro manipulations for intervention and operation. The thin optical fibers, working both as mechanical tendons and light waveguides, can be pulled/pushed to actuate the macro tendon-driven continuum robot and transmit light to actuate the liquid crystal elastomer-based micro built-in light-driven parallel robot. The combination of the decoupled macro and micro motions can accomplish accurate cross-scale motion from several millimeters down to tens of micrometers. In vivo animal studies are performed to demonstrate its positioning accuracy of precise micro operations in endoluminal or endocavitary intervention.

Boron Neutron Capture Therapy (BNCT) represents a revolutionary approach in targeted radiation treatment for cancer. While the therapy's potential in precise targeting is well-recognized, a critical bottleneck remains in the accurate positioning of patients for treatment delivery. This study proposes a novel automated robotic-assisted patient positioning system specifically engineered for BNCT applications. The system utilizes high-precision industrial robotics and is fully integrated with NeuMANTA, a proprietary treatment planning system designed for BNCT. Through a systematic workflow, the robotic arm algorithmically calculates and executes the patient's positioning based on the treatment plan, thus enhancing the accuracy and efficacy of the treatment. We validate the positioning system using an anthropomorphic phantom and evaluate the dosimetric impact of positional deviations. The results indicate that the system achieves high accuracy, with a maximum observational deviation of 3 mm in Source-to-Skin Distance (SSD) and 2 mm along the surface. Dosimetric analysis reveals that the resulting dose changes are less than 1% in surface orientation deviations and less than 5% in SSD orientation deviations. The study concludes that the robotic patient positioning system substantially advances in BNCT treatment delivery. This work not only sets a new benchmark for patient positioning in BNCT, but also provides a comprehensive framework for integrating advanced robotics into radiotherapy, paving the way for more precise and effective cancer treatments., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Currently, teleoperated robots, with the operator's input, can fully perceive unknown factors in a complex environment and have strong environmental interaction and perception abilities. However, physiological tremors in the human hand can seriously affect the accuracy of processes that require high-precision control. Therefore, this paper proposes an EEMD-IWOA-LSTM model, which can decompose the physiological tremor of the hand into several intrinsic modal components (IMF) by using the EEMD decomposition strategy and convert the complex nonlinear and non-stationary physiological tremor curve of the human hand into multiple simple sequences. An LSTM neural network is used to build a prediction model for each (IMF) component, and an IWOA is proposed to optimize the model, thereby improving the prediction accuracy of the physiological tremor and eliminating it. At the same time, the prediction results of this model are compared with those of different models, and the results of EEMD-IWOA-LSTM presented in this study show obvious superior performance. In the two examples, the MSE of the prediction model proposed are 0.1148 and 0.00623, respectively. The defibrillation model proposed in this study can effectively eliminate the physiological tremor of the human hand during teleoperation and improve the control accuracy of the robot during teleoperation.

Crawling robots are becoming increasingly prevalent in both industrial and private applications. Despite their many advantages over other robot types, they have complex movement mechanics. Artificial intelligence can simplify this by reinforcement learning. This process requires configuring the training environment and defining input parameters, including a robot model for movement training. To translate the virtual results into real-world scenarios, a 3D model with appropriate mechanical parameters must be developed.These parameters can vary significantly between multiple mechanical configurations, which will further impact the reinforcement learning process of such a robot. For this reason, it was decided to test which limb configurations would work best in this process. Initially, various kinematic types of walking robots were analysed, drawing on the anatomy of mammals, reptiles, and insects for the biological model. The reptilian model was chosen for its balance of stability, dynamics, and energy efficiency. The article reviews the preparation of robot models and the configuration of the Unity3D development environment using the ML-Agents toolkit. The experiment examined how different limb lengths affect training, resulting in movement algorithms for various quadruped robot configurations using artificial neural networks. Based on the numerical results, the best configuration was the default, with the same length of the tibia as the thigh, achieving a reward function value of 883.9 and an episode length of 245.5. Taking into account the same criteria, the least efficient configuration was definitely the one characterised by the shortest thigh and the longest tibia among those considered. In its case, the reward function reached a value of only 526.2 with an episode lasting 999.0, which means that it never achieved the intended goal., Competing Interests: Declarations Competing interest The authors do not have any conflict of interest to declare. Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Code availability The code that supports the findings of this study is available from the corresponding author upon request., (© 2024. The Author(s).)

The capacity to discern and locate positions in three-dimensional space is crucial for human-machine interfaces and robotic perception. However, current soft electronics can only obtain two-dimensional spatial locations through physical contact. In this study, we report a non-contact position targeting concept enabled by transparent and thin soft electronic skin (E-skin) with three-dimensional sensing capabilities. Inspired by the active electrosensation of mormyroidea fish, this E-skin actively ascertains the 3D positions of targeted objects in a contactless manner and can wirelessly convey the corresponding positions to other devices in real-time. Consequently, this E-skin readily enables interaction with machines, i.e., manipulating virtual objects, controlling robotic arms, and drones in either virtual or actual 3D space. Additionally, it can be integrated with robots to provide them with 3D situational awareness for perceiving their surroundings, avoiding obstacles, or tracking targets., Competing Interests: Competing interests The authors declare no conflict of interest., (© 2024. The Author(s).)

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the acute angle test (AAT) is proposed, which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighbor selection mechanism can reduce the redundant communication burden between large-scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Over the last two decades, robotics engineering has witnessed rapid growth in the exploration and development of soft robots. Soft robots are made of deformable materials with mechanical properties or other features that resemble biological structures. These robots are often inspired by living organisms or mimic their locomotion, such as crawling and swimming. This paper aims to assist researchers in robotics and engineering to design soft robots incorporating or inspired by biological systems with a more informed perspective on biological models and functions. We address the characteristics of fluidic soft robots inspired by or mimicking biological examples, establish a method to categorize soft robots from a functional biological perspective, and provide a wider range of organisms to inspire the development of soft robotics. The actuation mechanisms in bioinspired and biomimetic soft robotics would benefit from a clearer understanding of the underlying principles, organization, and function of biological structures., (Creative Commons Attribution license.)

This paper mainly explores the computational model that connects a robot's emotional body movements with human emotion to propose an emotion recognition method for humanoid robot body movements. There is sparse research directly carried out from this perspective to recognize robot bodily expression. A robot's body movements are designed by imitating human emotional body movements. Subjective questionnaires and statistical methods are used to analyze the characteristics of a user's perceptions and select appropriate designs. An emotional body movement recognition model using a BP neural network (EBMR-BP model) is proposed, in which the selected robot's body movements and corresponding emotions are used as inputs and outputs. The corresponding topological architecture, encoding rules, and training process are illustrated in detail. Then, the PSO method and the RMSProp algorithm are introduced to optimize the EBMR-BP method, and the PSO-BP-RMSProp model is developed. Through experiments and comparisons for emotion recognition of a robot's body movements, the feasibility and effectiveness of the EBMR-BP model, with a recognition rate of 66.67%, and the PSO-BP-RMSProp model, with a recognition rate of 88.89%, are verified. This indicates that the proposed method can be used for emotion recognition of a robot's body movements, and optimization can improve emotion recognition. The contributions are beneficial for emotional interaction design in HRI.

Minimally invasive procedures for endovascular interventions involve manual navigation of a guidewire. Endovascular interventions encompassing highly tortuous vessels would benefit from guidewires which exhibit higher dexterity. This paper introduces a version of the COAST (COaxially Aligned STeerable) guidewire system capable of exhibiting higher dexterity. The system presented in this paper consists of three coaxially aligned tubes with a tendon to actuate the middle tube. Furthermore, it is possible to independently rotate the middle tube with respect to the outer tube. This variation enables the guidewire to achieve curvature in different planes while avoiding rotation of the entire structure. We also present the simulated stability of the guidewire with different outer tube geometries and experimentally validate the model. Experimental analysis and modeling of the kinematic behavior of the system is presented. A model to calculate the curvature vs. tendon stroke relationship for the optimal notch geometry is presented with an average RMSE of 0.16 mm. A control strategy addressing the snapping instabilities to ensure reliable operation is discussed. A custom phantom vessel and an aortic arch phantom model were used to demonstrate the ability of the system to safely navigate through tortuous pathways without exhibiting these elastic instabilities., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

This paper presents the development and control of a dynamic model for a plant-inspired growing robot, termed the 'vine-robot', using the Euler-Lagrangian method. The unique growth mechanism of the vine-robot enables it to navigate complex environments by extending its body. We derive the dynamic equations of motion and employ model predictive control to regulate the task space position, orientation, and interaction forces. Simulation experiments are conducted to evaluate the performance of the proposed model and control strategy. The results demonstrate that the model effectively achieves sub-millimeter precision in the position control in both static and time varying refrence trajectroies, and sub micronewton in force control., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

This article presents a novel bioinspired technology for the cooperation and coordination of heterogeneous robot swarms in uncontrolled environments, utilizing an artificial pheromone composed of magnetized ferrofluids. Communication between different types of robots is achieved indirectly through stigmergy, where messages are inherently associated with specific locations. This approach is advantageous for swarm experimentation outside controlled laboratory spaces, where localization is typically managed through centralized camera systems (e.g. infrared, RGB). Applying pheromone principles has also proven beneficial for various swarm behaviors. We introduce a detection methodology for the artificial ferrofluid pheromone using low-cost magnetic sensors, along with signal processing and parameter characterization. Experiments involved a heterogeneous swarm consisting of two types of robots: one equipped with camera and image processing capabilities and the other with basic sensor technologies. Validation in multiple uncontrolled environments (with varying floor surfaces, wind, and light conditions) demonstrated successful cooperation among robots with differing technological complexities using the proposed technology., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Introduction : In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods : In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot's usability for this demographic. Results : The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot's speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the "good" category. Conclusions : Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.

A central pattern generator (CPG)-based controller enhances the adaptability of quadrupedal locomotion, for example, by controlling the trunk posture. The conventional CPG-based controllers with attitude control often utilized the posture angle as feedback information. However, if the robot's body is as soft as a musculoskeletal structure, it can detect the over-tilting of the trunk based on proprioceptive information of the muscles. In general, proprioceptive information such as muscle tension changes more rapidly than posture angle information. Therefore, a feedback loop based on proprioceptive information has great potential to respond to sudden disturbances that occur during locomotion over uneven terrain. In this research, we proposed a CPG-based controller utilizing the tension of soft pneumatic artificial muscles (PAMs). Musculoskeletal quadruped robots driven by PAMs are so soft, which prevents over-tilting of the trunk because the soft leg acts like a suspension. In addition, tension, one of the proprioceptive information of PAMs, exhibits high sensitivity to changes in trunk posture because the soft body's motion easily is affected by over-tilting of the trunk. To validate the efficacy of the proposed controller, we conducted numerical simulations with a simple quadruped model and experiments with a musculoskeletal quadruped robot. As a result, the tension feedback is not effective for posture stabilization on flat terrain whereas it is effective on uneven terrain. Moreover, the tension feedback improved the running velocity over uneven terrain. These results will enhance the locomotion capability of musculoskeletal quadruped robots, advancing their practical application., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Aim was to compare image-guided navigation with a robot-assisted solution for performing MISS regarding precision, required time and subjective aspects. 90 pedicles were instrumented on two torsos, half with navigation, half robot-assisted. Precision analysis between both solutions didn't show a significant difference. Time measurement showed a significantly longer duration per wire for the robot-arm on the first torso and a not significant longer duration on the second torso, where a significant reduction in the mean duration was shown. There was no significant difference in the subjective impressions comparing navigation and robot except the possibility to change the procedure. Precision of both methods is suitable for clinical use. A time advantage using the robot-arm couldn't be demonstrated in the present study. A significant learning curve was shown, so a reduction in the longer duration on the robot can be expected. Further studies in clinical use are necessary., (© 2024. The Author(s).)

Background: Upper limb rehabilitation robot is a relatively new technology, but its effectiveness remains debatable due to the inconsistent results of clinical trials. This article intends to assess how upper limb rehabilitation robots help the functional recovery of stroke patients., Methods: PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible studies to explore the effect of upper limb rehabilitation robots on upper limb motor function, muscle tone, and daily living activities., Results: Eighteen trials with 573 stroke patients met the inclusion criteria. The results showed that compared to conventional rehabilitation training, patients who received upper limb robotic therapy (RT) had significantly improved Fugl-Meyer Upper Extremity Motor Assessment (FMA-UE) scores (weighted mean differences [WMD]: 5.27, 95% confidence intervals [CI]: 3.36, 7.17), Action Research Arm Test (ARAT) scores (WMD: 4.07, 95% CI: -4.14, 12.28), Modified Barthel Index (MBI) scores (WMD: 9.55, 95% CI: 6.37, 12.73), and modified Ashworth Scale (MAS) scores (WMD: -0.28, 95% CI: -0.50, 0.06), with no significant heterogeneity., Conclusions: Upper limb robot-assisted training is superior to conventional training in terms of improving upper limb motor impairment, ability to perform daily living activities, and muscle tone recovery, which supports the application of robots in clinical practice., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Technologies that can record neural activity at cellular resolution at multiple spatial and temporal scales are typically much larger than the animals that are being recorded from and are thus limited to recording from head-fixed subjects. Here we have engineered robotic neural recording devices-'cranial exoskeletons'-that assist mice in maneuvering recording headstages that are orders of magnitude larger and heavier than the mice, while they navigate physical behavioral environments. We discovered optimal controller parameters that enable mice to locomote at physiologically realistic velocities while maintaining natural walking gaits. We show that mice learn to work with the robot to make turns and perform decision-making tasks. Robotic imaging and electrophysiology headstages were used to record recordings of Ca 2+ activity of thousands of neurons distributed across the dorsal cortex and spiking activity of hundreds of neurons across multiple brain regions and multiple days, respectively., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The development of environmentally adaptive solutions for magnetically actuated microrobots to enable targeted delivery in complex and confined fluid environments presents a significant challenge. Inspired by the natural locomotion of crucian carp, a barbell-shaped soft microrobot (MBS 2 M) is proposed. A mechano-electromagnetic hybrid actuation system is developed to generate oscillating magnetic fields to manipulate the microrobot. The MBS 2 M can seamlessly transition between three fundamental locomotion modes: fast navigation (FN), high-precision navigation (HPN), and fixed-point rotation (FPR). Moreover, the MBS 2 M can move in reverse without turning. The multimodal locomotion endows the MBS 2 M's adaptability in diverse environments. It can smoothly pass through confined channels, climb over obstacles, overcome gravity for vertical motion, track complex pathways, traverse viscous environments, overcome low fluid resistance, and navigate complex spaces mimicking in vivo environments. Additionally, the MBS 2 M is capable of drug loading and release in response to ultrasound excitation. In an ex vivo porcine liver vein, the microrobot demonstrated targeted navigation under ultrasound guidance, showcasing its potential for specialized in vivo tasks., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Soft robots spark a revolution in human-machine interaction. However, developing high-performance soft actuators remains challenging due to trade-offs among output force, driving distance, control precision, safety, and compliance. Here, addressing the lack of long-distance, high-precision flexible linear actuators, an innovative pneumatic flexible linear actuator (PFLA) is introduced, inspired by the smooth and controlled process observed in snakes ingesting sizable food, such as eggs. This PFLA combines a soft tube, emulating the snake's body cavity, with a pneumatically driven piston. Through the joint modulation of moving resistance and driving force by pneumatic pressure, the PFLA exhibits exceptional motion control capabilities, including self-holding without pressure supply, smooth low-speed motion (down to 0.004 m s -1 ), high-speed motion (up to 5.6 m s -1 ) with low air pressure demand, and a self-protection mechanism. Highlighting its adaptability and versatility, the PFLA finds applications in various settings, including a wearable assistive devices, a manipulator capable of precise path tracking and positioning, and rapid transportation in diverse environments for pipeline inspection and firefighting. This PFLA combines biomimetic principles with sophisticated fluidic actuation to achieve long-distance, flexible, precise, and safe actuation, offering a more adaptive solution for force/motion transmission, particularly in challenging environments., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Humans recognize and manipulate objects relying on the multidimensional force features captured by the tactile sense of skin during the manipulation. Since the current sensors integrated in robots cannot support the robots to sense the multiple interaction states between manipulator and objects, achieving human-like perception and analytical capabilities remains a major challenge for service robots. Prompted by the tactile perception involved in robots performing complex tasks, a multimodal tactile sensory system is presented to provide in situ simultaneous sensing for robots when approaching, touching, and manipulating objects. The system comprises a capacitive sensor owning the high sensitivity of 1.11E-2 pF mm -1 , a triboelectricity nanogenerator with the fast response speed of 30 ms, and a pressure sensor array capable of 3D force detection. By Combining transfer learning models, which fuses multimodal tactile information to achieve high-precision (up to 95%) recognition of the multi-featured targets such as random hardness and texture information under random sampling conditions, including random grasp force and velocity. This sensory system is expected to enhance the intelligent recognition and behavior-planning capabilities of autonomous robots when performing complex tasks in undefined surrounding environments., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Background and Objective: As the presentation of pulmonary nodules increases, the importance of a safe and accurate method of sampling peripheral pulmonary nodules is highlighted. First-generation robotic bronchoscopy has successfully assisted navigation and improved peripheral reach during bronchoscopy. Integrating tool-in-lesion tomosynthesis (TiLT) may further improve yield., Methods: We performed a first-in-human clinical trial of a new robotic electromagnetic navigation bronchoscopy system with integrated digital tomosynthesis technology (Galaxy System, Noah Medical). Patients with moderate-risk peripheral pulmonary nodules were enrolled in the study. Robotic bronchoscopy was performed using electromagnetic navigation with TiLT-assisted lesion guidance. Non-specific results were followed up until either a clear diagnosis was achieved or repeat radiology at 6 months demonstrated stability., Results: Eighteen patients (19 nodules) were enrolled. The average lesion size was 20 mm, and the average distance from the pleura was 11.6 mm. The target was successfully reached in 100% of nodules, and the biopsy tool was visualized inside the target lesion in all cases. A confirmed specific diagnosis was achieved in 17 nodules, 13 of which were malignant. In one patient, radiological monitoring confirmed a true non-malignant result. This translates to a yield of 89.5% (strict) to 94.7% (intermediate). Complications included one pneumothorax requiring observation only and another requiring an overnight chest drain. There was one case of severe pneumonia following the procedure., Conclusion: In this first-in-human study, second-generation robotic bronchoscopy using electromagnetic navigation combined with integrated digital tomosynthesis was feasible with an acceptable safety profile and demonstrated a high diagnostic yield for small peripheral lung nodules., (© 2024 The Author(s). Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology.)

Differential game is an effective technique to describe the negotiation between the humans and robots, which is widely used to realize the trajectory tracking tasks in the human-robot interaction (HRI). However, most existing works consider the control-affine HRI systems and assume the desired trajectory is available to both the human and the robot, which limit the scope of applications. To overcome these difficulties, this work focuses on the nonaffine HRI system and supposes that the desired trajectory is not available to the robot. A novel differential game framework encoding the desired trajectory estimator is proposed, where the desired trajectory is estimated via the Gaussian process regression (GPR) technique. To address the challenge arising from the nonlinearity of the HRI system, we equivalently transform the original problem into the one in a differentially flat space, and seek the equilibrium strategies for the transformed problem substitutionally. We further prove that the trajectory tracking error satisfies a probabilistic bound, whose confidence interval tightens as the decrease of noise variance during the interaction. Comparative simulation results show that our method outperforms the learning-based method in terms of robustness, parameters setting, and time consumption. Experiment results further show that the tracking error under the proposed human-robot cooperative algorithm is reduced by 55% compared to the human direct control.

Recent successes in robot learning have significantly enhanced autonomous systems across a wide range of tasks. However, they are prone to generate similar or the same solutions, limiting the controllability of the robot to behave according to user intentions. These limited robot behaviors may lead to collisions and potential harm to humans. To resolve these limitations, we introduce a semi-autonomous teleoperation framework that enables users to operate a robot by selecting a high-level command, referred to as option. Our approach aims to provide effective and diverse options by a learned policy, thereby enhancing the efficiency of the proposed framework. In this work, we propose a quality-diversity (QD) based sampling method that simultaneously optimizes both the quality and diversity of options using reinforcement learning (RL). Additionally, we present a mixture of latent variable models to learn multiple policy distributions defined as options. In experiments, we show that the proposed method achieves superior performance in terms of the success rate and diversity of the options in simulation environments. We further demonstrate that our method outperforms manual keyboard control for time duration over cluttered real-world environments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The intersection of mathematical cognition, metacognition, and advanced technologies presents a frontier with profound implications for human learning and artificial intelligence. This paper traces the historical roots of these concepts from the Pythagoreans and Aristotle to modern cognitive science and explores their relevance to contemporary technological applications. We examine how the Pythagoreans' view of mathematics as fundamental to understanding the universe and Aristotle's contributions to logic and categorization have shaped our current understanding of mathematical cognition and metacognition. The paper investigates the role of Boolean logic in computational processes and its relationship to human logical reasoning, as well as the significance of Bayesian inference and fuzzy logic in modelling uncertainty in human cognition and decision-making. We also explore the emerging field of Chemical Artificial Intelligence and its potential applications. We argue for unifying mathematical metacognition with advanced technologies, including artificial intelligence and robotics, while identifying the multifaceted benefits and challenges of such unification. The present paper examines essential research directions for integrating cognitive sciences and advanced technologies, discussing applications in education, healthcare, and business management. We provide suggestions for developing cognitive robots using specific cognitive tasks and explore the ethical implications of these advancements. Our analysis underscores the need for interdisciplinary collaboration to realize the full potential of this integration while mitigating potential risks., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Robotic pills leverage the advantages of oral pharmaceutical formulations-in particular, convenient encapsulation, high loading capacity, ease of manufacturing and high patient compliance-as well as the multifunctionality, increasing miniaturization and sophistication of microrobotic systems. In this Perspective, we provide an overview of major innovations in the development of robotic pills-specifically, oral pills embedded with robotic capabilities based on microneedles, microinjectors, microstirrers or microrockets-summarize current progress and applicational gaps of the technology, and discuss its prospects. We argue that the integration of multiple microrobotic functions within oral delivery systems alongside accurate control of the release characteristics of their payload provides a basis for realizing sophisticated multifunctional robotic pills that operate as closed-loop systems., Competing Interests: Competing interests: The authors declare no competing interests., (© 2023. Springer Nature Limited.)

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human-robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

Background: Amyotrophic lateral sclerosis (ALS) is a lethal progressive neurodegenerative disease characterized by upper motor neuron (UMN) and lower motor neuron (LMN) involvement. Their varying degree of involvement results in a clinical heterogenous picture, making clinical assessments of UMN signs in patients with ALS often challenging. We therefore explored whether instrumented assessment using robotic manipulation could potentially be a valuable tool to study signs of UMN involvement., Methods: We examined the dynamics of the wrist joint of 15 patients with ALS and 15 healthy controls using a Wristalyzer single-axis robotic manipulator and electromyography (EMG) recordings in the flexor and extensor muscles in the forearm. Multi-sinusoidal torque perturbations were applied, during which participants were asked to either relax, comply or resist. A neuromuscular model was used to study muscle viscoelasticity, e.g. stiffness (k) and viscosity (b), and reflexive properties, such as velocity, position and force feedback gains (kv, kp and kf, respectively) that dominated the responses. We further obtained clinical signs of LMN (muscle strength) and UMN (e.g. reflexes, spasticity) dysfunction, and evaluated their relation with the estimated neuromuscular model parameters., Results: Only force feedback gains (kf) were elevated in patients (p = 0.033) compared to controls. Higher kf, as well as the resulting reflexive torque (Tref), were both associated with more severe UMN dysfunction in the examined arm (p = 0.040 and p < 0.001). Patients with UMN symptoms in the examined arm had increased kf and Tref compared to controls (both p = 0.037). Neither of these measures was related to muscle strength, but muscle stiffness (k) was lower in weaker patients (p = 0.012). All these findings were obtained from the relaxed test. No differences were observed during the instructions comply and resist., Conclusions: This findings are proof-of-concept that instrumented assessment using robotic manipulation is a feasible technique in ALS, which may provide quantitative, operator-independent measures relating to UMN symptoms. Elevated force feedback gains, driving larger reflexive muscle torques, appear to be particularly indicative of clinically established levels of UMN dysfunction in the examined arm., (© 2024. The Author(s).)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258 ∘ C and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking., (© 2024. The Author(s).)

Stroke-induced upper-extremity paralysis affects a substantial portion of the population, necessitating effective rehabilitation strategies. This study aimed to develop an automated program, incorporating the item response theory, for rehabilitation in patients with post-stroke upper-extremity paralysis, focusing on the ReoGo-J device, and to identify suitable robot parameters for robotic rehabilitation. ReoGo-J, a training device for upper-extremity disorders including 71 items, was administered to over 300 patients with varying degrees of post-stroke upper-extremity paralysis. Each item was rated on a three-point scale (0, very difficult; 1, adequate; 2, very easy). The results were analyzed using the graded response model, an extension of the two-parameter logistic model within the framework of the item response theory, to grade the training items based on ability of the patients. The relationship between the predicted ability, an indicator of the predicted ability the paralyzed upper-extremity to perform an item in the item response theory analysis (higher numbers indicate higher ability, lower numbers indicate lower ability), and the items in the Fugl-Meyer assessment (FMA), which indicate the degree of paralysis, was analyzed using Pearson's correlation coefficient. This study included 312 patients with post-stroke upper-extremity paralysis. The predicted ability (θ) of the tasks included in the original ReoGo-J test for forward reaching, reaching for mouth, rotational reaching, radial reaching (2D), abduction reaching, reaching in eight directions, radial reach (3D), and reaching for head ranged from - 2.0 to - 0.8, - 1.3 to - 0.8, - 1.0 to - 0.1, - 0.7 to 0.3, - 0.2 to 0.4, - 0.4 to 0.6, - 0.1 to 0.6, and 0.5 to 0.6, respectively. Significantly high correlations (r = 0.80) were observed between the predicted ability of all patients and the upper-extremity items of shoulder-elbow-forearm in the FMA. We have introduced an automated program based on item response theory and determined the order of difficulty of the 71 training items in ReoGo-J. The strong correlation between the predicted ability and the shoulder-elbow-forearm items in FMA may be used to ameliorate post-stroke upper-extremity paralysis. Notably, the program allows for estimation of appropriate ReoGo-J tasks, enhancing clinical efficiency.Trial registration: https://www.umin.ac.jp/ctr/index-j.htm (UMIN000040127)., (© 2024. The Author(s).)

The inverse kinematics problem of exoskeleton rehabilitation robots is challenging due to the lack of a standard analytical model, resulting in a complex and varied solution process. This complexity is especially pronounced in redundant upper limb exoskeleton robots, where inefficient solutions hinder the robot's ability to adapt to the kinematic shape of the upper limb. This paper proposes a modeling and solution method based on multi-objective optimization to address the inverse kinematics of upper limb exoskeleton robots. We analyzed and validated this method using a redundant upper limb exoskeleton rehabilitation robot system developed by ourselves. First, we established a multi-objective inverse kinematics solution model by defining the end-position function, joint motion comfort function, system energy consumption function, motion safety, and human-like constraints. Then, the solution was designed based on the Improved Equilibrium Optimization (IEO) algorithm and validated its computational performance in terms of optimization ability, accuracy, and robustness. Finally, we experimentally tested the inverse kinematics solution model with the IEO algorithm on discrete objectives and continuous training trajectories using a redundant upper limb exoskeleton rehabilitation robot system. The results show that by incorporating the joint comfort function, system energy consumption function, and human-like constraints into the inverse kinematics model, we can not only quickly solve the inverse kinematics of redundant upper limb exoskeleton robots but also significantly improve the robot's motion shape. Furthermore, it has better solution accuracy and stronger robustness than other algorithms when solving this inverse kinematics model based on the IEO algorithm., (© 2024. The Author(s).)

Inspired by animals that co-adapt their brain and body to interact with the environment, we present a tendon-driven and over-actuated (i.e. n joint, n +1 actuators) bipedal robot that (i) exploits its backdrivable mechanical properties to manage body-environment interactions without explicit control, and (ii) uses a simple 3-layer neural network to learn to walk after only 2 min of 'natural' motor babbling (i.e. an exploration strategy that is compatible with leg and task dynamics; akin to childsplay). This brain-body collaboration first learns to produce feet cyclical movements 'in air' and, without further tuning, can produce locomotion when the biped is lowered to be in slight contact with the ground. In contrast, training with 2 min of 'naïve' motor babbling (i.e. an exploration strategy that ignores leg task dynamics), does not produce consistent cyclical movements 'in air', and produces erratic movements and no locomotion when in slight contact with the ground. When further lowering the biped and making the desired leg trajectories reach 1 cm below ground (causing the desired-vs-obtained trajectories error to be unavoidable), cyclical movements based on either natural or naïve babbling presented almost equally persistent trends, and locomotion emerged with naïve babbling. Therefore, we show how continual learning of walking in unforeseen circumstances can be driven by continual physical adaptation rooted in the backdrivable properties of the plant and enhanced by exploration strategies that exploit plant dynamics. Our studies also demonstrate that the bio-inspired co-design and co-adaptations of limbs and control strategies can produce locomotion without explicit control of trajectory errors., (Creative Commons Attribution license.)

Rapid and reliable detection of human survivors trapped under debris is crucial for effective post-earthquake search and rescue (SAR) operations. This paper presents a novel approach to survivor detection using a snake robot equipped with deep learning (DL) based object identification algorithms. We evaluated the performance of three main algorithms: Faster R-CNN, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO). While these algorithms are initially trained on the PASCAL VOC 2012 dataset for human identification, we address the lack of a dedicated dataset for trapped individuals by compiling a new dataset of 200 images that specifically depicts this scenario, featuring cluttered environment and occlusion. Our evaluation takes into account detection accuracy, confidence interval, and running time. The results demonstrate that the YOLOv10 algorithm achieves the 98.4 mAP@0.5, accuracy of 98.5% for inference time of 15 ms. We validate the performance of these algorithms using images of human survivors trapped under debris and subjected to various occlusions., (© 2024. The Author(s).)

Human-robot collaboration (HRC) is a novel manufacturing paradigm designed to fully leverage the advantage of humans and robots, efficiently and flexibly accomplishing customized manufacturing tasks. However, existing HRC systems lack the transfer and generalization capability for environment perception and task reasoning. These limitations manifest in: (1) current methods rely on specialized models to perceive scenes; and need retraining the model when facing unseen objects. (2) current methods only address predefined tasks, and cannot support undefined task reasoning. To avoid these limitations, this paper proposes a novel HRC approach based on Foundation Models (FMs), including Large Language models (LLMs) and Vision Foundation Models (VFMs). Specifically, a LLMs-based task reasoning method is introduced, utilizing prompt learning to transfer LLMs into the domain of HRC tasks, supporting undefined task reasoning. A VFMs-based scene semantic perception method is proposed, integrating various VFMs to achieve scene perception without training. Finally, a FMs-based HRC system is developed, comprising perception, reasoning, and execution modules for more flexible and generalized HRC. The superior performances of FMs in perception and reasoning are demonstrated by extensive experiments. Furthermore, the feasibility and effectiveness of the FMs-based HRC system are validated through an part assembly case involving a satellite component model., (© 2024. The Author(s).)

As gestures play an important role in human communication, there have been a number of service robots equipped with a pair of human-like arms for gesture-based human-robot interactions. However, the arms of most human companion robots are limited to slow and simple gestures due to the low maximum velocity of the arm actuators. In this work, we present the JF-2 robot, a mobile home service robot equipped with a pair of torque-controlled anthropomorphic arms. Thanks to the low inertia design of the arm, responsive Quasi-Direct Drive (QDD) actuators, and active compliant control of the joints, the robot can replicate fast human dance motions while being safe in the environment. In addition to the JF-2 robot, we also present the JF-mini robot, a scaled-down, low-cost version of the JF-2 robot mainly targeted for commercial use at kindergarten and childcare facilities. The suggested system is validated by performing three experiments, a safety test, teaching children how to dance along to the music, and bringing a requested item to a human subject.

Background: Walking impairment after stroke is associated with substantial limitations in functional independence, quality of life, and long-term survival. People in the subacute phase after stroke who are unable to walk are most likely to benefit the greatest from use of overground robotic gait training (RGT). This study will provide preliminary evidence regarding the clinical use and efficacy of RGT during the subacute phase of stroke recovery as well as observational findings associated with the safety, tolerability, feasibility, and cost of delivering RGT during inpatient stroke rehabilitation., Methods: This prospectively registered randomized controlled trial will enroll 54 patients admitted to inpatient rehabilitation within six months of stroke. Admitted patients will be screened at admission to inpatient rehabilitation for eligibility. Consented patients will be randomized based on stroke severity to receive either RGT or usual care for 90 minutes per week of gait training intervention during inpatient rehabilitation length of stay. Patients will complete assessments on walking and health outcomes at admission and discharge from inpatient rehabilitation and at 1- and 3-month follow-up. Intent-to-treat and per protocol analysis will be performed to evaluate safety [rate of adverse events, visual analog scale, and treatment completion rate], walking function [gait speed via 10-Meter Walk Test, Functional Ambulation Category, gait endurance via 6-Minute Walk Test] and health outcomes [Modified Rankin Scale, Stroke Rehabilitation Assessment of Movement, Continuity Assessment Record and Evaluation Tool, 5 Times Sit-to-Stand Test, Berg Balance Scale, and Stroke Impact Scale-16], and cost-analysis., Discussion: This study will provide foundational evidence regarding the clinical use and efficacy of a RGT program during the subacute phase of stroke recovery with specific findings associated with the safety, tolerability, feasibility, and cost-analysis of delivering RGT during inpatient stroke rehabilitation., Trial Registration: NCT06430632., (© 2024. The Author(s).)

An exoskeleton is a wearable device with human-machine interaction characteristics. An ideal exoskeleton should have kinematic and kinetic characteristics similar to those of the wearer. Most traditional exoskeletons are driven by rigid actuators based on joint torque or position control algorithms. In order to achieve better human-robot interaction, flexible actuators have been introduced into exoskeletons. However, exoskeletons with fixed stiffness cannot adapt to changing stiffness requirements during assistance. In order to achieve collaborative control of stiffness and torque, a bionic variable stiffness hip joint exoskeleton (BVS-HJE) is designed in this article. The exoskeleton proposed in this article is inspired by the muscles that come in agonist-antagonist pairs, whose actuators are arranged in an antagonistic form on both sides of the hip joint. Compared with other exoskeletons, it has antagonistic actuators with variable stiffness mechanisms, which allow the stiffness control of the exoskeleton joint independent of force (or position) control. A BVS-HJE model was established to study its variable stiffness and static characteristics. Based on the characteristics of the BVS-HJE, a control strategy is proposed that can achieve independent adjustment of joint torque and joint stiffness. In addition, the variable stiffness mechanism can estimate the output force based on the established mathematical model through an encoder, thus eliminating the additional force sensors in the control process. Finally, the variable stiffness properties of the actuator and the controllability of joint stiffness and joint torque were verified through experiments.

Recognizing penetration events in multilayer tissue is critical for many biomedical engineering applications, including surgical procedures and medical diagnostics. This paper presents a unique method for detecting penetration events in multilayer tissue using Long Short-Term Memory (LSTM) networks. LSTM networks, a form of recurrent neural network (RNN), excel at analyzing sequential data because of their ability to hold long-term dependencies. The suggested method collects time-series insertion force data from sensors integrated from a 1-DOF prismatic robot as it penetrates tissue. This data is then processed by the LSTM network, which has been trained to recognize patterns indicating penetration events through various tissue layers. The effectiveness of this approach is validated through experimental setups, demonstrating high accuracy and reliability in detecting penetration events. This technique offers significant improvements over traditional methods, providing a non-invasive, real-time solution that enhances the precision and safety of medical procedures involving multilayer tissue interaction., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Human-robot collaboration will play an important role in the fourth industrial revolution in applications related to hostile environments, mining, industry, forestry, education, natural disaster and defense. Effective collaboration requires robots to understand human intentions and tasks, which involves advanced user profiling. Voice-based communication, rich in complex information, is key to this. Beamforming, a technology that enhances speech signals, can help robots extract semantic, emotional, or health-related information from speech. This paper describes the implementation of a system that provides substantially improved signal-to-noise ratio (SNR) and speech recognition accuracy to a moving robotic platform for use in human-robot interaction (HRI) applications in static and dynamic contexts. This study focuses on training deep learning-based beamformers using acoustic model-based multi-style training with measured room impulse responses (RIRs). The results show that this approach outperforms training with simulated RIRs or matched measured RIRs, especially in dynamic conditions involving robot motion. The findings suggest that training with a broad range of measured RIRs is sufficient for effective HRI in various environments, making additional data recording or augmentation unnecessary. This research demonstrates that deep learning-based beamforming can significantly improve HRI performance, particularly in challenging acoustic environments, surpassing traditional beamforming methods.

Robot-assisted neurosurgery has revolutionized intricate brain and spinal procedures. From PUMA 560 to NeuroArm and ROSA, robotic systems have enhanced precision and enabled minimally invasive techniques. Despite challenges in LMICs, collaborative efforts in infrastructure, education, and international partnerships can foster equitable adoption of these transformative technologies., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper presents the design of a strawberry fruit head gripper unit, together with the concept of a control system for the operation of its mechanisms and vision system. The developed design consists of three specialised mechanisms: positioning, grasping, and cutting off of the fruit. A Finite Element Method (FEM) model was developed for the described design. Next, calculations were carried out, based on which the construction materials were selected. The key performance parameters of the functional model, built on the basis of the developed design concept, were verified under laboratory conditions. In tests carried out on the possible hematoma caused by exceeding the breaking stress induced by the pressure of the encompassing jaws on the fruit, it was found that none of the fruit tested suffered mechanical damage as a result of the sensor triggering force, and the average length of the trimmed stalk was approximately 14 mm. The designed head gripper, together with the proposed automation system, will contribute to improving harvesting precision, and this will favour a reduction in the quantitative and qualitative losses of the harvested crop. The experimental tests conducted under harvesting conditions showed a high efficiency of 95% in identifying ripe fruit, and the harvesting efficiency of the robotic arm was 90%.

The nematode Caenorhabditis elegans is widely employed as a model organism to study basic biological mechanisms. However, transgenic C. elegans are generated by manual injection, which remains low-throughput and labor-intensive, limiting the scope of approaches benefitting from large-scale transgenesis. Here, we report a robotic microinjection system, integrating a microfluidic device capable of reliable worm immobilization, transfer, and rotation, for high-speed injection of C. elegans. The robotic system provides an injection speed 2-3 times faster than that of experts with 7-22 years of experience while maintaining comparable injection quality and only limited trials needed by users to become proficient. We further employ our system in a large-scale reverse genetic screen using multiplexed alternative splicing reporters, and find that the TDP-1 RNA-binding protein regulates alternative splicing of zoo-1 mRNA, which encodes variants of the zonula occludens tight junction proteins. With its high speed, high accuracy, and high efficiency in worm injection, this robotic system shows great potential for high-throughput transgenic studies of C. elegans., (© 2024. The Author(s).)

Background: Periprocedural ischemic stroke remains a serious complication in patients undergoing transcatheter aortic valve replacement (TAVR). We used a novel robotic transcranial Doppler (TCD) system equipped with artificial intelligence (AI) for real-time continuous intraoperative neuromonitoring during TAVR to establish the safety and potential validity of this tool in detecting cerebral emboli, report the quantity and distribution of high intensity transient signals (HITS) with and without cerebral protection, and correlate HITS occurrence with various procedural steps., Methods: Consecutive patients undergoing TAVR procedures during which the robotic system was used between October 2021 and May 2022 were prospectively enrolled in this pilot study. The robotic TCD system included autonomous adjustment of the TCD probes and AI-assisted post-processing of HITS and other cerebral flow parameters. Basic demographics and procedural details were recorded. Continuous variables were analyzed by a two-sample Mann-Whitney t-test and categorical variables by a χ 2 or Fisher test., Results: Thirty-one patients were prospectively enrolled (mean age 79.9±7.6 years; 16 men (51.6%)). Mean aortic valve stenotic area was 0.7 cm 2 and mean aortic-ventricular gradient was 43 mmHg (IQR 31.5-50 mmHg). Cerebral protection was used in 16 cases (51.6%). Significantly fewer emboli were observed in the protection group than in the non-protection group (mean 470.38 vs 693.33; p=0.01). Emboli counts during valve positioning and implantation were significantly different in the protection and non-protection groups (mean 249.92 and 387.5, respectively; p=0.01). One (4%) transient ischemic attack occurred post-procedurally in the non-protection group., Conclusion: We describe a novel real-time intraoperative neuromonitoring tool used in patients undergoing TAVR. Significantly fewer HITS were detected with protection. Valve positioning-implantation was the most significant stage for intraprocedural HITS., Competing Interests: Competing interests: LNH: Grant/research support: Canon Medical; major stock shareholder/equity: Boston Scientific, Imperative Care, Ostial, Synchron, Spinnaker, Serenity, Cerebrotech, Reist. JMD: Consulting fees, payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing, or educational events: Medtronic, Rapid Medical; support for attending meetings and/or travel: Medtronic, Rapid Medical; patents planned, issued, or pending: QAS.ai; participation on a Data Safety Monitoring Board or Advisory Board: NIH NIHDS Strokenet; stock or stock options: Synchron, Cerebrotech, QAS.ai. EIL: Shareholder/ownership interest: NeXtGen Biologics, Rapid Medical, Claret Medical, Cognition Medical, Imperative Care, Rebound Therapeutics, StimMed, Three Rivers Medical; patent: Bone Scalpel; honorarium: Medtronic, Penumbra, MicroVention, Integra; consultant: Clarion, GLG Consulting, Guidepoint Global, Imperative Care, Medtronic, StimMed, Misionix, Mosiac; Chief Medical Officer: Haniva Technology; Advisory Board: Stryker, NeXtGen Biologics, MEDX, Cognition Medical, Endostream Medical, IRRAS AB. AHS: Grants or contracts: Co-investigator for NIH - 1R01EB030092-01, Project Title: High Speed Angiography at 1000 frames per second; Mentor for Brain Aneurysm Foundation Carol W Harvey Chair of Research, Sharon Epperson Chair of Research, Project Title: A Whole Blood RNA Diagnostic for Unruptured Brain Aneurysm: Risk Assessment Prototype Development and Testing; consulting fees: Amnis Therapeutics, Apellis Pharmaceuticals, Boston Scientific, Canon Medical Systems USA, Cardinal Health 200, Cerebrotech Medical Systems, Cerenovus, Cerevatech Medical, Cordis, Corindus, Inc., Endostream Medical, Imperative Care, InspireMD, Integra, IRRAS AB, Medtronic, MicroVention, Minnetronix Neuro, Peijia Medical, Penumbra, Q’Apel Medical, Rapid Medical, Serenity Medical, Silk Road Medical, StimMed, Stryker Neurovascular, Three Rivers Medical, VasSol, Viz.ai; leadership or fiduciary role in other board, society, committee or advocacy group: Secretary – Board of the Society of NeuroInterventional Surgery 2020-2021, Chair – Cerebrovascular Section of the AANS/CNS 2020-2021; stock or stock options: Adona Medical, Amnis Therapeutics, Bend IT Technologies, BlinkTBI, Borvo Medical, Bridgewater Ventures, Cerebrotech Medical Systems, Cerevatech Medical, Code Zero Medical, Cognition Medical, Collavidence, CVAID, E8, Endostream Medical, Galaxy Therapeutics, Imperative Care, InspireMD, Instylla, International Medical Distribution Partners, Launch NY, Neurolutions, NeuroRadial Technologies, NeuroTechnology Investors, Neurovascular Diagnostics, Peijia Medical, PerFlow Medical, Piraeus Medical, Q’Apel Medical, QAS.ai, Radical Catheter Technologies, Rebound Therapeutics Corp (purchased 2019 by Integra Lifesciences Corp), Rist Neurovascular, (purchased 2020 by Medtronic), Sense Diagnostics, Serenity Medical, Silk Road Medical, Sim & Cure, SongBird Therapy, Spinnaker Medical, StimMed, Synchron, Three Rivers Medical, Truvic Medical, Tulavi Therapeutics, Vastrax, VICIS, Viseon, Whisper Medical, Willow Medtech; other financial or non-financial interests: National PI/Steering Committees: Cerenovus EXCELLENT and ARISE II Trial; Medtronic SWIFT PRIME, VANTAGE, EMBOLISE and SWIFT DIRECT Trials; MicroVention FRED Trial and CONFIDENCE Study; MUSC POSITIVE Trial; Penumbra 3D Separator Trial, COMPASS Trial, INVEST Trial, MIVI neuroscience EVAQ Trial; Rapid Medical SUCCESS Trial; InspireMD C-GUARDIANS IDE Pivotal Trial., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings.