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The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Background Context: Robotic spine surgery, utilizing 3D imaging and robotic arms, has been shown to improve the accuracy of pedicle screw placement compared to conventional methods, although its superiority remains under debate. There are few studies evaluating the accuracy of 3D navigated versus robotic-guided screw placement across lumbar levels, addressing anatomical challenges to refine surgical strategies and patient safety., Purpose: This study aims to investigate the pedicle screw placement accuracy between 3D navigation and robotic arm-guided systems across distinct lumbar levels., Study Design: A retrospective review of a prospectively collected registry., Patient Sample: Patients undergoing fusion surgery with pedicle screw placement in the prone position, using either via 3D image navigation only or robotic arm guidance., Outcome Measure: Radiographical screw accuracy was assessed by the postoperative computed tomography (CT) according to the Gertzbein-Robbins classification, particularly focused on accuracy at different lumbar levels., Methods: Accuracy of screw placement in the 3D navigation (Nav group) and robotic arm guidance (Robo group) was compared using Chi-squared test/Fisher's exact test with effect size measured by Cramer's V, both overall and at each specific lumbosacral spinal level., Results: A total of 321 patients were included (Nav, 157; Robo, 189) and evaluated 1210 screws (Nav, 651; Robo 559). The Robo group demonstrated significantly higher overall accuracy (98.6 vs 93.9%; p<.001, V=0.25). This difference of no breach screw rate was signified the most at the L3 level (No breach screw: Robo 91.3 vs 57.8%, p<.001, V=0.35) followed by L4 (89.6 vs 64.7%, p<.001, V=0.28), and L5 (92.0 vs 74.5%, p<.001, V=0.22). However, screw accuracy at S1 was not significant between the groups (81.1 vs 72.0%, V=0.10)., Conclusion: This study highlights the enhanced accuracy of robotic arm-guided systems compared to 3D navigation for pedicle screw placement in lumbar fusion surgeries, especially at the L3, L4, and L5 levels. However, at the S1 level, both systems exhibit similar effectiveness, underscoring the importance of understanding each system's specific advantages for optimization of surgical complications., Competing Interests: Declaration of competing interest One or more of the authors declare financial or professional relationships on ICMJE-TSJ disclosure forms., (Copyright © 2024. Published by Elsevier Inc.)

Background: In modern Hybrid ORs, the synergies of navigation and robotics are assumed to contribute to the optimisation of the treatment in trauma, orthopaedic and spine surgery. Despite promising evidence in the area of navigation and robotics, previous publications have not definitively proven the potential benefits. Therefore, the aim of this retrospective study was to evaluate the potential benefit and clinical outcome of patients treated in a fully equipped 3D-Navigation Hybrid OR., Methods: Prospective data was collected (March 2022- March 2024) after implementation of a fully equipped 3D-Navigation Hybrid OR ("Robotic Suite") in the authors level 1 trauma centre. The OR includes a navigation unit, a cone beam CT (CBCT), a robotic arm and mixed reality glasses. Surgeries with different indications of the spine, the pelvis (pelvic ring and acetabulum) and the extremities were performed. Spinal and non-spinal screws were inserted. The collected data was analysed retrospectively. Pedicle screw accuracy was graded according to the Gertzbein and Robbins (GR) classification., Results: A total of n = 210 patients (118 m:92f) were treated in our 3D-Navigation Hybrid OR, with 1171 screws inserted. Among these patients, 23 patients (11.0%) arrived at the hospital via the trauma room with an average Injury Severity Score (ISS) of 25.7. There were 1035 (88.4%) spinal screws inserted at an accuracy rate of 98.7% (CI95%: 98.1-99.4%; 911 GR-A & 111 GR-B screws). The number of non-spinal screws were 136 (11.6%) with an accuracy rate of 99.3% (CI95%: 97.8-100.0%; 135 correctly placed screws). This resulted in an overall accuracy rate of 98.8% (CI95%: 98.2-99.4%). The robotic arm was used in 152 cases (72.4%), minimally invasive surgery (MIS) was performed in 139 cases (66.2%) and wound infection occurred in 4 cases (1,9%). Overall, no revisions were needed., Conclusion: By extending the scope of application, this study showed that interventions in a fully equipped 3D-Navigation Hybrid OR can be successfully performed not only on the spine, but also on the pelvis and extremities. In trauma, orthopaedics and spinal surgery, navigation and robotics can be used to perform operations with a high degree of precision, increased safety, reduced radiation exposure for the OR-team and a very low complication rate., (© 2024. The Author(s).)

In this study, crack-free TiC/Ti composites with TiC content ranging from 0 to 15 wt.% were successfully fabricated using Direct Energy Deposition with a dual-feeder system that concomitantly delivered different amounts of both constituents into a high-power laser beam. The samples were investigated to evaluate the morphologies and distribution behavior of TiC. The microhardness values of the samples obtained under optimal processing conditions increased from 192 ± 5.3 HV 0.2 (pure Ti) to 300 ± 14.2 HV 0.2 (Ti + wt.% 15 TiC). Also, TiC has a significant impact on the Ti matrix, increasing the strength of TMCs up to 725 ± 5.4 MPa, while the elongation drastically decreased to 0.62 ± 0.04%. The wear rate is not proportionally affected by the rise content of TiC reinforcement; the hypoeutectic region of TMCs exhibited a wear rate of 2.45 mm 3 /N·m (Ti + wt.% 3 TiC) and a friction coefficient of 0.48 compared to the ones from the hypereutectic region, which measured a wear rate of 3.02 mm 3 /N·m (Ti + wt.% 15 TiC) and a friction coefficient of 0.63. The improved values of mechanical properties in the case of TMCs as compared to pure Ti are provided due to the solid solution strengthening of carbon and the fine grain strengthening. This work outlines a method for changing TiC morphologies to improve the hardness and tensile strength of TMCs fabricated starting from micro-scale powder.

Background: Robotic systems for total knee arthroplasty (TKA) may utilize computed tomography three-dimensional modeling and intraoperative ligamentous balancing data to assist surgeons with implant size and position. This study evaluated the effect of such robotic systems on implant selection., Methods: We reviewed 645 TKAs performed with a single prosthetic design at 2 academic medical centers between 2016 and 2022. A robotic system was utilized in 304 TKAs, 341 were conventionally instrumented. Implant sizing was compared between cohorts. Multivariate analyses assessed for confounding and effect modification on the basis of demographics., Results: The 2 cohorts exhibited no significant differences in age ( P  = .33), weight ( P  = .29), or race ( P  = .24). The robotic-arm cohort had fewer women (58.9% vs 66.7% P  = .04) and was taller on average (66.3 in vs 65.0 in P < .001). Mean polyethylene liner thickness was larger in the manual cohort (10.3 robotic and 10.6 manual; P < .00). On multivariate analysis, robotic-arm TKAs had larger tibial components ( P < .001) and smaller femoral components ( P  = .017)., Conclusions: Robotic-arm assisted TKA with computed tomography-based three-dimensional planning was associated with a larger mean tibial component size and a smaller mean femoral component size when compared to conventionally instrumented TKAs. Observed differences likely reflect differences in the data informing implant size selection; effects on clinical outcomes warrant further study., (© 2024 The Authors.)

Background: In the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants., Methods: To meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics., Results: The Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity., Conclusion: These application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users., (© 2024. The Author(s).)

Objective . Noninvasive brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. Approach . By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI training involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results . Showed that the hand grasping motor imagery timing significantly affects the BCI accuracy evolution as well as the spatiotemporal brain dynamics. Larger accuracy improvement was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions. Significance . Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces., (© 2024 IOP Publishing Ltd.)

This paper presents a gesture-controlled robotic arm system designed for agricultural harvesting, utilizing a data glove equipped with bending sensors and OptiTrack systems. The system aims to address the challenges of labor-intensive fruit harvesting by providing a user-friendly and efficient solution. The data glove captures hand gestures and movements using bending sensors and reflective markers, while the OptiTrack system ensures high-precision spatial tracking. Machine learning algorithms, specifically a CNN+BiLSTM model, are employed to accurately recognize hand gestures and control the robotic arm. Experimental results demonstrate the system's high precision in replicating hand movements, with a Euclidean Distance of 0.0131 m and a Root Mean Square Error (RMSE) of 0.0095 m, in addition to robust gesture recognition accuracy, with an overall accuracy of 96.43%. This hybrid approach combines the adaptability and speed of semi-automated systems with the precision and usability of fully automated systems, offering a promising solution for sustainable and labor-efficient agricultural practices.

Particle (proton, carbon ion, or others) radiotherapy for ocular tumors is highly dependent on precise dose distribution, and any misalignment can result in severe complications. The proposed eye positioning and tracking system (EPTS) was designed to non-invasively position eyeballs and is reproducible enough to ensure accurate dose distribution by guiding gaze direction and tracking eye motion. Eye positioning was performed by guiding the gaze direction with separately controlled light sources. Eye tracking was performed by a robotic arm with cameras and a mirror. The cameras attached to its end received images through mirror reflection. To maintain a light weight, certain materials, such as carbon fiber, were utilized where possible. The robotic arm was controlled by a robot operating system. The robotic arm, turntables, and light source were actively and remotely controlled in real time. The videos captured by the cameras could be annotated, saved, and loaded into software. The available range of gaze guidance is 360° (azimuth). Weighing a total of 18.55 kg, the EPTS could be installed or uninstalled in 10 s. The structure, motion, and electromagnetic compatibility were verified via experiments. The EPTS shows some potential due to its non-invasive wide-range flexible eye positioning and tracking, light weight, non-collision with other equipment, and compatibility with CT imaging and dose delivery. The EPTS can also be remotely controlled in real time and offers sufficient reproducibility. This system is expected to have a positive impact on ocular particle radiotherapy., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies.

Waste segregation is an essential aspect of a smoothly functioning waste management system. Usually, various recyclable waste types are disposed of together at the source, and this brings in the necessity to segregate them into their categories. Dry waste needs to be separated into its own categories to ensure that the proper procedures are implemented to treat and process it, which leads to an overall increased recycling rate and reduced landfill impact. Paper, plastics, metals, and glass are just a few examples of the many dry waste materials that can be recycled or recovered to create new goods or energy. Over the past years, much research has been conducted to devise effective and productive ways to achieve proper segregation for the waste that is being produced at an ever-increasing rate. This article introduces a multi-class garbage segregation system employing the YOLOv5 object detection model. Our final prototype demonstrates the capability of classifying dry waste categories and segregating them into their respective bins using a 3D-printed robotic arm. Within our controlled test environment, the system correctly segregated waste classes, mainly paper, plastic, metal, and glass, eight out of 10 times successfully. By integrating the principles of artificial intelligence and robotics, our approach simplifies and optimizes the traditional waste segregation process., Competing Interests: The authors declare there are no competing interests., (©2024 Lahoti et al.)

With the development and the application popularization of artificial intelligence robot technology and 5G technology, a robotic arm is designed and developed for rinsing porcelain bushing in high voltage substation in this paper. Firstly, the components and implementation of robotic arm are presented, subsequently, a circular cleaning structure with a 120-degree split is proposed to rinse the porcelain bushing. Secondly, a two-stage simple and effective method to realize automatic orientation is proposed utilizing photoelectric switches. Moreover, a prototype of robotic arm with control system is developed based on the regime switching function, and the result of edge computing is transmitted by 5G technology. Finally, feasibility and effectiveness of the robotic arm are verified in the Nanjing power grid. The case study manifests that the robotic arm developed by the proposed method in the paper can achieve efficient rinsing and all the corresponding information can be transmitted preciously. The proposed method lays a foundation for wide application of cleaning robot in high voltage substation., (© 2024. The Author(s).)

Introduction: The use of a robotic system for the placement of pedicle screws in spine surgeries is well documented in the literature. However, there is only a single report in the United States describing the use of a robotic system to place two screws in osseous fixation pathways (OFPs) commonly used in the treatment of pelvic and acetabular fractures in a simulated bone model. The purpose of this study was to demonstrate the use of a robotic system to place screws in multiple, clinically relevant OFPs in a cadaveric model and to quantitatively measure accuracy of screw placement relative to the preoperative plan., Methods: A single cadaveric specimen was obtained for the purpose of this study. All surrounding soft tissues were left intact. Screws were placed in OFPs, namely iliosacral (IS), trans-sacral (TS), Lateral Compression-II (LC-II), antegrade anterior column (AC) and antegrade posterior column (PC) of the right hemipelvis using standard, fluoroscopically assisted percutaneous or mini-open technique. Following the placement of screws into the right hemipelvis using standard techniques, screws were planned and placed in the same OFPs of the contralateral hemipelvis using the commercially available ExcelsiusGPS ® robotic system (Globus Medical Inc., Audubon, PA). After robotic-assisted screw placement, a post-procedure CT scan was obtained to evaluate actual screw placement against the pre-procedure plan. A custom-made image analysis program was devised to measure screw tip/tail offset and angular offset on axial and sagittal planes., Results: For different OFPs, the mean tip offset, tail offset and angular offsets were 1.6 ± 0.9 mm (Range 0.0-3.6 mm), 1.4 ± 0.4 mm (Range 0.3-2.5 mm) and 1.1 ± 0.4° (Range 0.5-2.1), respectively., Conclusion: In this feasibility study, surgeons were able to place screws into the clinically relevant fracture pathways of the pelvis using ExcelsiusGPS ® for robotic-assisted surgery. The measured accuracy was encouraging; however, further investigation is needed to demonstrate that robotic-assisted surgery can be used to successfully place the screws in the bony corridors of the pelvis to treat traumatic pelvic injuries., (© 2024. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.)

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand-eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs.

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments.

In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved deformable convolution is introduced to adaptively adjust receptive fields for multiscale feature information extraction. Then, an efficient spatial feature center (SFC) layer is explored to capture the global remote dependencies through a lightweight multilayer perceptron (MLP) architecture. Furthermore, a learnable feature center (LFC) mechanism is reported to gather local regional features and preserve the local corner region. Finally, a lightweight CARAFE operator is developed to upsample the features. Experimental results show that DCSFC-Grasp achieves a high accuracy (99.3% and 96.1% for the Cornell and Jacquard grasp datasets, respectively) and even outperforms the existing state-of-the-art grasp detection models. The results of real-world experiments on the six-DoF Realman RM65 robotic arm further demonstrate that our DCSFC-Grasp is effective and robust for the grasping of unknown targets.

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology.

Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand movement can be significantly increased. In this study, the artificial neuromolecular system (ANM system) developed by our laboratory is used as the core motion control system to learn to control the mechanical arm, produce similar human rehabilitation actions, and assist patients in transiting between different activities. The strength of the ANM system lies in its ability to capture and process spatiotemporal information by exploiting the dynamic information processing inside neurons. Five experiments are conducted in this research: continuous learning, dimensionality reduction, moving problem domains, transfer learning, and fault tolerance. The results show that the ANM system can find out the arm movement trajectory when people perform different rehabilitation actions through the ability of continuous learning and reduce the activation of multiple muscle groups in stroke patients through the learning method of reducing dimensions. Finally, using the ANM system can reduce the learning time and performance required to switch between different actions through transfer learning.

This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images.

Background: The tendon-sheath-system (TSS) is an excellent medium for remote power transmission, which is widely used in laparoscopic surgery robots. Since the operation process requires the robot to move continuously, this time-varying characteristic further aggravates the force and position transmission loss caused by the nonlinear friction of TSS, which affects the control accuracy of the surgical robot., Method: A time-varying tendon-sheath transmission model (RT model) is proposed. A feedforward control system is designed to improve tendon-sheath transmission accuracy. Furthermore, a tendon-sheath transmission model with velocity characteristics (RV model) is established., Result: Force, position, and velocity experiments were carried out on the platform of TSS with a robotic arm. The results show that the R-square values of force and position compensation are at least 96.57% and 99.16%., Conclusion: The proposed RT and RV models are effective in compensating for the TSS transmission loss during the operation of the surgical robot., (© 2023 John Wiley & Sons Ltd.)

With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system's performance and scalability.

Several studies have reported the effects of robotic arms on improving upper limb function in patients with stroke. However, previous studies have reported inconsistent findings that may lead to incorrect applications of robotic arm use. Six databases were searched for relevant randomized controlled trials. Meta-analyses were performed for upper limb performance measures, including subgroup analysis of pooled upper limb rehabilitation data such as stroke stage and intervention delivery dose. Furthermore, the Cochrane risk-of-bias tool for randomized trials version 2 (RoB 2) and sensitivity analysis were used to assess methodology and determine publication bias. The final analysis included 18 studies. Robotic arms improved upper limb and hand function in patients with stroke. Subgroup analysis revealed that robotic arm interventions lasting 30-60 min per session significantly improved upper limb function. However, no significant improvement was observed in shoulder and elbow or wrist and hand movements. This review may help develop applicable rehabilitation robots and collaboration between clinicians., (© 2023 The Authors. The Kaohsiung Journal of Medical Sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.)

In mixed-reality (MR) telecollaboration, the local environment is remotely presented to a remote user wearing a virtual reality (VR) head-mounted display (HMD) via a video capture device. However, remote users frequently face challenges in naturally and actively manipulating their viewpoints. In this paper, we propose a telepresence system with viewpoint control, which involves a robotic arm equipped with a stereo camera in the local environment. This system enables remote users to actively and flexibly observe the local environment by moving their heads to manipulate the robotic arm. Additionally, to solve the problem of the limited field of view of the stereo camera and limited movement range of the robotic arm, we propose a 3D reconstruction method combined with a stereo video field-of-view enhancement technique to guide remote users to move within the movement range of the robotic arm and provide them with a larger range of local environment perception. Finally, a mixed-reality telecollaboration prototype was built, and two user studies were conducted to evaluate the overall system. User study A evaluated the interaction efficiency, system usability, workload, copresence, and user satisfaction of our system from the remote user's perspective, and the results showed that our system can effectively improve the interaction efficiency while achieving a better user experience than two traditional view-sharing techniques based on 360 video and based on the local user's first-person view. User study B evaluated our MR telecollaboration system prototype from both the remote-user side and the local-user side as a whole, providing directions and suggestions for the subsequent design and improvement of our mixed-reality telecollaboration system.

At present, single-modal brain-computer interface (BCI) still has limitations in practical application, such as low flexibility, poor autonomy, and easy fatigue for subjects. This study developed an asynchronous robotic arm control system based on steady-state visual evoked potentials (SSVEP) and eye-tracking in virtual reality (VR) environment, including simultaneous and sequential modes. For simultaneous mode, target classification was realized by decision-level fusion of electroencephalography (EEG) and eye-gaze. The stimulus duration for each subject was non-fixed, which was determined by an adjustable window method. Subjects could autonomously control the start and stop of the system using triple blink and eye closure, respectively. For sequential mode, no calibration was conducted before operation. First, subjects' gaze area was obtained through eye-gaze, and then only few stimulus blocks began to flicker. Next, target classification was determined using EEG. Additionally, subjects could reject false triggering commands using eye closure. In this study, the system effectiveness was verified through offline experiment and online robotic-arm grasping experiment. Twenty subjects participated in offline experiment. For simultaneous mode, average ACC and ITR at the stimulus duration of 0.9 s were 90.50% and 60.02 bits/min, respectively. For sequential mode, average ACC and ITR at the stimulus duration of 1.4 s were 90.47% and 45.38 bits/min, respectively. Fifteen subjects successfully completed the online tasks of grabbing balls in both modes, and most subjects preferred the sequential mode. The proposed hybrid brain-computer interface (h-BCI) system could increase autonomy, reduce visual fatigue, meet individual needs, and improve the efficiency of the system., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Guo, Lin, Luo, Gao and Zhang.)

Kresling pattern origami-inspired structural design has been widely investigated using its bistable property and the single coupling degree of freedom (DOF). In order to obtain new properties or new origami-inspired structures, it needs to innovate the crease lines in the flat sheet of Kresling pattern origami. Here, we present a derivative of Kresling pattern origami-multi-triangles cylindrical origami (MTCO) with tristable property. The truss model is modified based on the switchable active crease lines during the folding motion of the MTCO. Using the energy landscape obtained from the modified truss model, the tristable property is validated and extended to Kresling pattern origami. Simultaneously, the high stiffness property of the third stable state and some special stable states are discussed. In addition, MTCO-inspired metamaterials with deployable property and tunable stiffness, and MTCO-inspired robotic arms with wide movement ranges and rich motion forms are created. These works promote research on Kresling pattern origami, and the design ideas of the metamaterials and robotic arms play a positive role in improving the stiffness of deployable structures and conceiving motion robots., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use.

Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other. In addition, the experience replay mechanism of DDPG is also improved by combining priority sampling and uniform sampling to accelerate the DDPG's convergence. Finally, it is verified in the simulation environment that the improved DDPG algorithm can achieve accurate control of the robot arm motion. The experimental results show that the improved DDPG algorithm can converge in a shorter time, and the average success rate in the robotic arm end-reaching task is as high as 91.27%. Compared with the original DDPG algorithm, it has more robust environmental adaptability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Dong, Du, Liu, Heidari and Chen.)

Magnetic navigation systems (MNSs) have been developed to use in the diagnosis of gastrointestinal problems. However, most conventional magnetic navigation systems are expensive and have structural problems because of their large weights and volumes. Therefore, this paper proposes C-Mag, a novel compact MNS composed of two electromagnets and a robotic arm. The two electromagnets generate a planar magnetic field, and the robotic arm rotates and translates the electromagnets to manipulate the magnetic capsule in a large 3-dimensional (3-D) space. The C-Mag design considers the payload of the robotic arm and the capacity of the power supply unit. Under these limited conditions, the C-Mag was optimized to generate the maximum magnetic field considering several major factors. Finally, the C-Mag was constructed, and the maximum magnetic field that could be generated in one direction was 18.65 mT in the downward direction. Additionally, the maximum rotating magnetic field was 13.21 mT, which was used to manipulate the capsule. The performance was verified by measuring the generated magnetic field, and it matched well with the simulated result. Additionally, the path-following experiment of the magnetic capsule showed that the proposed C-Mag can effectively manipulate the magnetic capsule in 3-D space using the robotic arm. This study is expected to contribute to the further development of magnetic navigation systems to treat gastrointestinal problems.

The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block's pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row.

Robotic-assisted total knee arthroplasty (TKA) has proven higher accuracy, fewer alignment outliers, and improved short-term clinical outcomes when compared to conventional TKA. However, evidence of cost-effectiveness and individual superiority of one system over another is the subject of further research. Despite its growing adoption rate, published results are still limited and comparative studies are scarce. This review compares characteristics and performance of five currently available systems, focusing on the information and feedback each system provides to the surgeon, what the systems allow the surgeon to modify during the operation, and how each system then aids execution of the surgical plan.Cite this article: Bone Jt Open  2023;4(1):13-18.

Preoperative planning and intra-operative system setup are crucial steps to successfully integrate robotically assisted surgical systems (RASS) into the operating room. Efficiency in terms of setup planning directly affects the overall procedural costs and increases acceptance of RASS by surgeons and clinical personnel. Due to the kinematic limitations of RASS, selecting an optimal robot base location and surgery access point for the patient is essential to avoid potentially critical complications due to reachability issues. To this end, this work proposes a novel versatile method for RASS setup and planning based on robot capability maps (CMAPs). CMAPs are a common tool to perform workspace analysis in robotics, as they are in general applicable to any robot kinematics. However, CMAPs have not been completely exploited so far for RASS setup and planning. By adapting global CMAPs to surgical procedure-specific tasks and constraints, a novel RASS capability map (RASSCMAP) is generated. Furthermore, RASSCMAPs can be derived to also comply with kinematic access constraints such as access points in laparoscopy. RASSCMAPs are versatile and applicable to any kind of surgical procedure; they can be used on the one hand for aiding in intra-operative experience-based system setup by visualizing online the robot's capability to perform a task. On the other hand, they can be used to find the optimal setup by applying a multi-objective optimization based on a genetic algorithm preoperatively, which is then transfered to the operating room during system setup. To illustrate these applications, the method is evaluated in two different use cases, namely, pedicle screw placement in vertebral fixation procedures and general laparoscopy. The proposed RASSCMAPs help in increasing the overall clinical value of RASS by reducing system setup time and guaranteeing proper robot reachability to successfully perform the intended surgeries., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Sundaram, Budjakoski, Klodmann and Roa.)

Aquatic resistance training has been proven to be beneficial to many people, in particular those struggling with degenerative joint diseases or recovering from other musculoskeletal issues as the reaction forces acting on the joints become lower, but without compromising the cardiovascular and neuromuscular benefit of the movement. Little has been written on the load produced by or measurements of the devices used in aquatic resistance training. Therefore, uncertainties exist regarding details of how much load can be applied onto the foot when performing the movements and how to quantify progression. In this study, an instrumented robotic arm was designed, built, and used to measure the load acting on the three different types of fins during a simulated flexion/extension movement of a knee. The angular velocities of the knee ranged from 25°/s to 150°/s, which represent the physiological range of in vivo movements. The results demonstrated that the load followed a second-order polynomial with the angular velocities. The load is therefore a function of the angular velocity, the surface area of the fins, and the location of the fins away from the joint center rotation. We modeled the progression of speeds at maximal voluntary movements based on previous studies. The maximum loads measured between 11 kg and 13 kg in extension and 6 kg and 9 kg in flexion at 150°/s rotational velocity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gislason, Einarsson, Ingvason, Saavedra and Waller.)

The Tactile Internet enables physical touch to be transmitted over the Internet. In the context of electronic medicine, an authenticated key agreement for the Tactile Internet allows surgeons to perform operations via robotic systems and receive tactile feedback from remote patients. The fifth generation of networks has completely changed the network space and has increased the efficiency of the Tactile Internet with its ultra-low latency, high data rates, and reliable connectivity. However, inappropriate and insecure authentication key agreements for the Tactile Internet may cause misjudgment and improper operation by medical staff, endangering the life of patients. In 2021, Kamil et al. developed a novel and lightweight authenticated key agreement scheme that is suitable for remote surgery applications in the Tactile Internet environment. However, their scheme directly encrypts communication messages with constant secret keys and directly stores secret keys in the verifier table, making the scheme vulnerable to possible attacks. Therefore, in this investigation, we discuss the limitations of the scheme proposed by Kamil scheme and present an enhanced scheme. The enhanced scheme is developed using a one-time key to protect communication messages, whereas the verifier table is protected with a secret gateway key to mitigate the mentioned limitations. The enhanced scheme is proven secure against possible attacks, providing more security functionalities than similar schemes and retaining a lightweight computational cost.

Advances in robotic construction are evident and increasing every year, bringing present and potential improvements. However, the economic and social impacts are hard to assess and quantify without physical in situ testing, which is expensive and time-consuming. This paper presents a methodology for the simulation of robotic construction technologies, namely drones, using a virtual reality environment. Our hypothesis is that a virtual reality simulation of a robotic construction (H1) has the potential of increasing the precision of predicting the construction duration and cost and (H2) allows for the detection of construction problems. The study begins with a review of the literature on drones, robotic arms, and hybrid automatic construction solutions, as well as virtual reality construction simulations, summarising the robotic technologies currently being used, mainly in academic research, to assemble construction elements. It then proposes a construction simulation methodology applied to three architectonic elements to analyse different approaches and different scenarios for robotic construction simulation methodology. A construction simulation is tested, and the data is analysed and compared with traditional construction methods, focussing on construction time and costs., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Published by Elsevier Ltd.)

Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, and life expectancy of the sensor in the fish farm to the server. The integrated data are transmitted to mobile devices through the Internet of Things, enabling administrators to monitor the water quality in a fish farm through mobile devices. Because the current pH sensors cannot be submerged in the liquid for a long time for measurements, human resources and time are required to take the instrument to each fish farm for testing at a fixed time. Therefore, a robotic arm was developed to complete automatic measurement and maintenance actions. We designed this arm with a programmable logic controller, a single chip combined with a wireless transmission module, and an embedded system. This system is divided into control, measurement, server, and mobility. The intelligent measurement equipment designed in this study can work 24 h per day, which effectively reduces the losses caused by personnel, material resources, and data errors.

Good ligament balance in total knee arthroplasty (TKA) is thought to improve clinical results, but is highly surgeon-dependent when performed without technological assistance. We therefore describe a TKA technique using the Mako robotic arm (Stryker, Kalamazoo, Michigan, USA) as sole means of balancing ligament tension by bone recuts associated to control by the VERASENSE load sensor (Orthosensor, Inc, Dania Beach, Florida, USA). In this preliminary series of 29 patients, 27 (93%) showed a well-balanced knee in extension at end of procedure, and 23 (79%) showed a well-balanced knee in flexion and extension, without any periarticular soft-tissue release. The load sensor analyzes ligament balance after the initial bone cuts, and guides possible further femoral or tibial recuts. This technique enables quantifiable alignment and control of ligament tension. Collecting objective intraoperative data should improve knowledge in placing TKA prostheses., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

Pain-related avoidance of movements that are actually safe (ie, overprotective behavior) plays a key role in chronic pain disability. Avoidance is reinforced through operant learning: after learning that a certain movement elicits pain, movements that prevent pain are more likely to be performed. Proprioceptive accuracy importantly contributes to motor learning and memory. Interestingly, reduced accuracy has been documented in various chronic pain conditions, prompting the question whether this relates to avoidance becoming excessive. Using robotic arm-reaching movements, we tested the hypothesis that poor proprioceptive accuracy is associated with excessive pain-related avoidance in pain-free participants. Participants first performed a task to assess proprioceptive accuracy, followed by an operant avoidance training during which a pain stimulus was presented when they performed one movement trajectory, but not when they performed another trajectory. During a test phase, movements were no longer restricted to 2 trajectories, but participants were instructed to avoid pain. Unbeknownst to the participants, the pain stimulus was never presented during this phase. Results supported our hypothesis. Furthermore, exploratory analyses indicated a reduction in proprioceptive accuracy after avoidance learning, which was associated with excessive avoidance and higher trait fear of pain. PERSPECTIVE: This study is the first to show that poorer proprioceptive accuracy is associated with excessive pain-related avoidance. This finding is especially relevant for chronic pain conditions, as reduced accuracy has been documented in these populations, and points toward the need for research on training accuracy to tackle excessive avoidance., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: This study explored whether robotic arm-assisted total knee arthroplasty (RATKA) has the advantage of restoring femoral rotational alignment compared to conventional total knee arthroplasty (COTKA)., Methods: Sixty patients (45 women and 15 men) attending our department from May 2019 to December 2020 were selected and divided into two groups, with 30 patients in each group, according to whether they underwent COTKA or RATKA. Femoral rotational alignment results, such as, posterior condylar angle (PCA), patella transverse axis-femoral transepicondylar axis angle (PFA), radiological findings, such as, hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA), and medial proximal tibial angle (MPTA), and operative data (operation time, intraoperative blood loss, tourniquet time, and length of stay (LOS), and clinical outcomes, such as maximum knee flexion angle (MKFA), Knee Society Score (KSS), and Western Ontario Mac Master University Index Score (WOMAC) were compared within and between the two groups., Results: PCA and PFA in the RATKA group were (0.6 ± 0.3)° and (0.9 ± 0.3)°, respectively, which were smaller than (1.5 ± 2.0)° and (3.1 ± 1.1)° in the COTKA group (P < 0.05), and were closer to 0°; the differences in HKA, LDFA, and MPTA were not statistically significant. With the exception of the LDFA, the HKA, MPTA, PCA, and PFA improved in both groups after surgery (P < 0.05). The blood loss and the LOS of RATKA group were 192.3 ± 23.1 mL and 8.2 ± 1.4 days, which were less than 203.7 ± 29.8 mL and 9.3 ± 1.1 days of the COTKA group, but the operation time showed no statistically significant difference, and the tourniquet time was longer (P < 0.05). The MKFA in the RATKA group was (123.0 ± 3.7)°, which was greater than (116.3 ± 4.6)° in the COTKA group (P < 0.05). In terms of scores, the postoperative results were better than the preoperative results in both groups (P < 0.05). However, there was no statistically significant difference between the groups., Conclusion: The accuracy of femoral rotational alignment reconstructed achieved by RATKA is significantly better than that of COTKA and is more conducive to the recovery of knee flexion function after surgery; although RATKA reduces intraoperative blood loss and postoperative LOS, the short-term clinical efficacy comparison has not yet demonstrated the advantages of robotic technology, and a more optimized design is needed to improve the efficiency of RATKA surgery., (© 2022 The Authors. Orthopaedic Surgery published by Tianjin Hospital and John Wiley & Sons Australia, Ltd.)

Robotic surgery is increasing in prevalence, thanks to its potential benefits for patients (e.g., reduced blood loss) and surgeons (e.g., ergonomics). It is important to know what inherent characteristics of potential surgeons may facilitate robotic surgery training and performance. Findings from previous studies indicate videogames can be inexpensive tools that help improve hand-eye coordination, coordination of 3-D movements with 2-D images, and spatial orientation. In the context of robotic-assisted knee arthroscopy using a MAKO robotic arm, this study explored performance and subjective experiences of novices (N = 104) with a fake bone shaving task at a public event. Participants' performance was measured based on how much of the bone they successfully shaved. Findings showed that duration of videogame play per week was negatively related to performance with the robotic arm. Male and female participants performed similarly on the bone shaving task, and reported similar difficulty with and enjoyment of the task. However, female participants who played videogames performed better than those who did not play videogames. Participants who were younger than 11 had the worst performance and the most difficulty with the robotic arm. Overall, the findings indicate that the effect of videogame experience on the performance with the robotic arm may differ based on gender and age. This has implications on the length of training for surgeons of different gender using videogames and other emerging technologies., (© 2021. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Background: This study aimed to compare the bone resection thickness, rate of minimal thickness insert usage, and postoperative range of motion (ROM) between robotic and navigated total knee arthroplasty (TKA)., Methods: Fifty consecutive posterior-stabilised (PS) robotic TKA and 50 case-controlled PS navigated TKA were enroled. Bone resection thickness, rate of minimal thickness insert usage, and postoperative flexion contracture over 5° were statistically compared., Results: The tibial bone resection thickness was significantly thinner in robotic TKA. The insert thickness was significantly lower, and the rate of minimal thickness insert usage was significantly higher in robotic TKA. The ROM at 3 months postoperatively and the rate of postoperative flexion contracture were comparable between the two groups., Conclusion: Robotic TKA can reduce the tibial bone resection thickness and increase the rate of minimal thickness insert usage without increasing postoperative flexion contracture as compared with navigated TKA., (© 2022 John Wiley & Sons Ltd.)

Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.

Joint arthroplasty is an effective method for treating end-stage joint lesions and damages. Robotic arm-assisted arthroplasty, a rapidly developing technology that combines navigation technology, minimally invasive technology, and precise control technology of the robotic arm, can achieve accurate preoperative planning, optimal selection of implants, minimally invasive surgery, precise osteotomy, and accurate placement of the artificial joint. It has the characteristics of high accuracy and stability, and thus is more and more widely used in the field of joint surgery. In this paper, we systematically reviewed the application and clinical efficacy of robotic arm-assisted technology in hip and knee arthroplasty to provide reference for its future promotion., (Copyright © 2021 Chinese Medical Association. Production and hosting by Elsevier B.V. All rights reserved.)

Aims: This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted unicompartmental knee arthroplasty (RAUKA) with manual medial unicompartmental knee arthroplasty (mUKA)., Methods: Searches of PubMed, MEDLINE, and Google Scholar were performed in November 2021 according to the Preferred Reporting Items for Systematic Review and Meta--Analysis statement. Search terms included "robotic", "unicompartmental", "knee", and "arthroplasty". Published clinical research articles reporting the learning curves and cost-effectiveness of MAKO RAUKA, and those comparing the component precision, functional outcomes, survivorship, or complications with mUKA, were included for analysis., Results: A total of 179 articles were identified from initial screening, of which 14 articles satisfied the inclusion criteria and were included for analysis. The papers analyzed include one on learning curve, five on implant positioning, six on functional outcomes, five on complications, six on survivorship, and three on cost. The learning curve was six cases for operating time and zero for precision. There was consistent evidence of more precise implant positioning with MAKO RAUKA. Meta-analysis demonstrated lower overall complication rates associated with MAKO RAUKA (OR 2.18 (95% confidence interval (CI) 1.06 to 4.49); p = 0.040) but no difference in re-intervention, infection, Knee Society Score (KSS; mean difference 1.64 (95% CI -3.00 to 6.27); p = 0.490), or Western Ontario and McMaster Universities Arthritis Index (WOMAC) score (mean difference -0.58 (95% CI -3.55 to 2.38); p = 0.700). MAKO RAUKA was shown to be a cost-effective procedure, but this was directly related to volume., Conclusion: MAKO RAUKA was associated with improved precision of component positioning but was not associated with improved PROMs using the KSS and WOMAC scores. Future longer-term studies should report functional outcomes, potentially using scores with minimal ceiling effects and survival to assess whether the improved precision of MAKO RAUKA results in better outcomes. Cite this article: Bone Joint J  2022;104-B(5):541-548.

Aims: The aim of this study was to compare any differences in the primary outcome (biphasic flexion knee moment during gait) of robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) at one year post-surgery., Methods: A total of 76 patients (34 bi-UKA and 42 TKA patients) were analyzed in a prospective, single-centre, randomized controlled trial. Flat ground shod gait analysis was performed preoperatively and one year postoperatively. Knee flexion moment was calculated from motion capture markers and force plates. The same setup determined proprioception outcomes during a joint position sense test and one-leg standing. Surgery allocation, surgeon, and secondary outcomes were analyzed for prediction of the primary outcome from a binary regression model., Results: Both interventions were shown to be effective treatment options, with no significant differences shown between interventions for the primary outcome of this study (18/35 (51.4%) biphasic TKA patients vs 20/31 (64.5%) biphasic bi-UKA patients; p = 0.558). All outcomes were compared to an age-matched, healthy cohort that outperformed both groups, indicating residual deficits exists following surgery. Logistic regression analysis of primary outcome with secondary outcomes indicated that the most significant predictor of postoperative biphasic knee moments was preoperative knee moment profile and trochlear degradation (Outerbridge) (R 2 = 0.381; p = 0.002, p = 0.046). A separate regression of alignment against primary outcome indicated significant bi-UKA femoral and tibial axial alignment (R 2 = 0.352; p = 0.029), and TKA femoral sagittal alignment (R 2 = 0.252; p = 0.016). The bi-UKA group showed a significant increased ability in the proprioceptive joint position test, but no difference was found in more dynamic testing of proprioception., Conclusion: Robotic arm-assisted bi-UKA demonstrated equivalence to TKA in achieving a biphasic gait pattern after surgery for osteoarthritis of the knee. Both treatments are successful at improving gait, but both leave the patients with a functional limitation that is not present in healthy age-matched controls. Cite this article: Bone Joint J  2022;103-B(4):433-443.

Under the delicate control of the brain, people can perform graceful movements through the coordination of muscles, bones, ligaments, and joints. If artificial intelligence can be used to establish a control system that simulates the movements of human arms, it is believed that the application scope of robotic arms in assisting people's daily life can be greatly increased. The purpose of this study is to build a general system that can use intelligent techniques to assist in the construction of a personalized rehabilitation system. More importantly, this research hopes to establish an intelligent system that can be adjusted according to the needs of the problem domain, that is, the system can move toward the direction of problem-solving through autonomous learning. The artificial neural molecular system (ANM system), developed early in our laboratory, which captured the close structure/function relationship of biological systems, was used. The system was operated on the V-REP (Virtual Robot Experimentation Platform). The results show that the ANM system can use self-learning methods to adjust the start-up time, rotation angle, and the sequence of the motor operation of different motors in order to complete the designated task assignment.

Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Peng, Li, Zhao, Xu, Xu and Wu.)

Various insects can entrap and stabilize air plastrons and bubbles underwater. When these bubbles interact with surfaces underwater, they create air capillary bridges that de-wet surfaces and even allow underwater reversible adhesion. In this study, a robotic arm with interchangeable three-dimensional (3D)-printed bubble-stabilizing units is used to create air capillary bridges underwater for manipulation of small objects. Particles of various sizes and shapes, thin sheets and substrates of diverse surface tensions, from hydrophilic to superhydrophobic, can be lifted, transported, placed, and oriented using one- or two-dimensional arrays of bubbles. Underwater adhesion, derived from the air capillary bridges, is quantified depending on the number, arrangement, and size of bubbles and the contact angle of the counter surface. This includes a variety of commercially available materials and chemically modified surfaces. Overall, it is possible to manipulate millimeter- to sub-millimeter-scale objects underwater. This includes cleaning submerged surfaces from colloids and arbitrary contaminations, folding thin sheets to create three-dimensional structures, and precisely placing and aligning objects of various geometries. The robotic underwater manipulator can be used for automation and control in cell culture experiments, lab-on-chip devices, and manipulation of objects underwater. It offers the ability to control the transport and release of small objects without the need for chemical adhesives, suction-based adhesion, anchoring devices, or grabbers.

This work explores additive manufacturing (AM) of concrete by using a six-axis robotic arm and its use in large-scale, autonomous concrete construction. Concrete AM uses an extrusion method to deposit concrete beads in layers to create a three-dimensional (3D) shape. This method has been found to have many uses and advantages in construction applications. The lack of formwork and autonomous nature of this manufacturing method allows for new geometries and materials to be printed in unsafe or challenging environments. Autonomous construction has been suggested as a method of creating habitats in rapid-response scenarios. This article discusses research toward one such system that could be used to rapidly construct necessary habitats in response to low-resource and emergency situations. This required addressing certain limitations of a six-axis robotic arm platform along with overcoming system challenges to achieve deliverables for NASA's "3D Printed Habitat Challenge." This included system design to increase the build volume, integrate embedding, print non-coplanar sections, and minimize travel moves to address the challenges associated with continuous extrusion of cementitious material. The system was demonstrated by printing a one-third scale habitat, which represents the first 3d-printed fully enclosed structure at an architectural scale without the use of support., Competing Interests: The authors declare no conflict of interest., (Copyright 2022, Mary Ann Liebert, Inc., publishers.)

Robotic-arm-assisted unicompartmental knee arthroplasty (RUKA) was developed to increase the accuracy of bone alignment and implant positioning. This retrospective study explored whether RUKA has more favorable overall outcomes than conventional unicompartmental knee arthroplasty (CUKA). A total of 158 patients with medial compartment osteoarthritis were recruited, of which 85 had undergone RUKA with the Mako system and 73 had undergone CUKA. The accuracy of component positioning and bone anatomical alignment was compared using preoperative and postoperative radiograph. Clinical outcomes were evaluated using questionnaires, which the patients completed preoperatively and then postoperatively at six months, one year, and two years. In total, 52 patients from the RUKA group and 61 from the CUKA group were eligible for analysis. The preoperative health scores and Kellgren-Lawrence scores were higher in the RUKA group. RUKA exhibited higher implant positioning accuracy, thus providing a superior femoral implant angle, properly aligned implant placement, and a low rate of overhang. RUKA also achieved higher accuracy in bone anatomical alignment (tibial axis angle and anatomical axis angle) than CUKA, but surgical time was longer, and blood loss was greater. No significant differences were observed in the clinical outcomes of the two procedures.