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Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.

Ultra-wideband (UWB) localization has emerged in GPS-denied environments as a crucial facilitator for diverse industries, including logistics, healthcare applications, and societal domains. Despite notable progress, UWB algorithms from the scientific literature are often evaluated in isolation in very specific conditions and hence difficult to compare. This paper introduces a novel benchmark platform designed to assess the performance of 11 prominent UWB accuracy-enhancing algorithms, both independently and in combination. A key feature of the platform is its incorporation of multiple diverse evaluation metrics, including mean average error, latency, and spatial error. We showcase the significance of adopting alternative metrics such as spatial error, which often, depending on the use case, offers greater relevance than the prevalently used mean average error. Furthermore, we show that “more is better” does not hold true when combining multiple accuracy-improving algorithms for UWB systems: combining multiple accuracy-improving algorithms reveals instances of diminishing returns and can even result in overall performance decline. Additionally, we caution against blind reliance on accuracy outcomes reported in the scientific literature when designing UWB systems for use cases that are different in terms of requirements or environment. Finally, we also provide algorithmic recommendations for distinct surroundings, exemplary applications, and usage contexts, assisting in driving efficient design in future UWB research and adoption.

This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared control system for a mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those with impaired motor function due to conditions such as stroke, the system utilizes BCI to interpret user intentions from electroencephalography signals and eye tracking to identify the object of focus, thus refining control commands. This integration seeks to create a more intuitive and responsive assistive robot control strategy. The real-world usability was evaluated, demonstrating significant potential to improve autonomy for individuals with severe motor impairments. The control system was compared with an eye-tracking-based alternative to identify areas needing improvement. Although BCI achieved an acceptable success rate of 0.83 in the final phase, eye tracking was more effective with a perfect success rate and consistently lower completion times (p<0.001). The user experience responses favored eye tracking in 11 out of 26 questions, with no significant differences in the remaining questions, and subjective fatigue was higher with BCI use (p=0.04). While BCI performance lagged behind eye tracking, the user evaluation supports the validity of our control strategy, showing that it could be deployed in real-world conditions and suggesting a pathway for further advancements.

As the industry shifts to automated manufacturing and the assembly of parts in smaller batches, there is a clear need for an efficient design of grippers. This paper presents a method for automated grasp planning and finger design for multiple parts using four grasp quality measures that capture the following important requirements for grasping: (i) uniform contact force distribution; (ii) better gravity wrench resistance; (iii) robustness against gripper positioning error; and (iv) ability to resist larger external wrench on the object. We introduce the fingertip score to quantify the grasp performance of a fingertip design over all the objects. The method takes the CAD model of the objects as the input and outputs the optimal grasp location and the best finger design. We use the method for a three-point grasp with a parallel jaw gripper. We validate our method on two sets of objects. Results show how each grasp quality measure behaves on different objects and the variation in the fingertip score with finger design. Finally, we test the effectiveness of the optimal finger design experimentally. The three-point grasp is suitable for grasping objects larger than is possible with shape-matching fingertips.

Abstract Self-healing soft robots show enormous potential to recover functional performance after healing the damages. However, healing in these systems is limited by the recontact of the fracture surfaces. This paper presents for the first time a shape memory alloy (SMA) wire-reinforced soft bending actuator made out of a castor oil-based self-healing polymer, with the incorporated ability to recover from large incisions via shape memory assisted healing. The integrated SMA wires serve three major purposes; (i) Large incisions are closed by contraction of the current-activated SMA wires that are integrated into the chamber. These pull the fracture surfaces into contact, enabling the healing. (ii) The heat generated during the activation of the SMA wires is synergistically exploited for accelerating the healing. (iii) Lastly, during pneumatic actuation, the wires constrain radial expansion and one-side longitudinal extension of the soft chamber, effectuating the desired actuator bending motion. This novel approach of healing is studied via mechanical and ultrasound tests on the specimen level, as well as via bending characterization of the pneumatic robot in multiple damage healing cycles. This technology allows soft robots to become more independent in terms of their self-healing capabilities from human intervention.

Flexible, soft materials are increasingly used for the fabrication of soft robots, as the inherent compliance and shock‐absorbance protect the robot from mechanical impact. Soft universal grippers take full advantage of this adaptability, facilitating effective and safe grasping of various objects. However, due to their predominantly soft material composition, these grippers have limited lifetimes, especially when operating in unstructured and unfamiliar environments. The self‐healing universal gripper (SHUG) is proposed, which can grasp various objects and recover from substantial realistic damages autonomously. It integrates damage detection, heat‐assisted healing, and healing evaluation. Notably, unlike other universal grippers, the entire SHUG can be fully reprocessed and recycled. The gripper's functionality relies on the particle jamming of steel balls enclosed within a self‐healing membrane. Thanks to the thermoreversible covalent Diels–Alder bonds in self‐healing polymer membrane, the gripper is able to recover from macroscopic damages including scratches and punctures. Temperature‐assisted healing is regulated in a closed‐loop manner using an embedded thermocouple and Joule heater. Experimental validation demonstrates the adaptability, resilience, and recyclability of the SHUG.

While vacuum grippers offer an economical solution, the environmental impact of energy waste through their suction cups (SCs) cannot be overlooked. This waste stems from three key factors: i) air losses from idle SCs arranged in arrays, ii) inadequate sealing on target surfaces leading to air leaks, and iii) damage from sharp objects resulting in leaking perforations. To overcome these challenges, in this article, a comprehensive approach is presented that involves the development of a i) self‐closing, ii) multi‐material, and iii) self‐healing system based on reversible elastomers cross‐linked via the Diels–Alder (DA) reaction. The system incorporates a fully autonomous self‐closing mechanism to prevent energy waste in SC arrays during periods of non‐contact. Fluid–structure interaction simulations are utilized to analyze the design. Versatility and stability are achieved by incorporating hyper‐flexible and stiff elastomers in a multi‐material design, supported by covalent DA cross‐links that ensure robustness through high‐strength multi‐material interfaces. These DA cross‐links also enable self‐healing capabilities, allowing the SCs to recover from macroscopic damages within 1 day at ambient conditions or in a single hour with mild heating (80–90 °C), restoring full performance. Additionally, in the article, a recycling method is introduced for multi‐material SCs based on the mechanical separation of reversible polymers.

Abstract Background A mechanical ankle–foot prosthesis (Talaris Demonstrator) was developed to improve prosthetic gait in people with a lower-limb amputation. This study aims to evaluate the Talaris Demonstrator (TD) during level walking by mapping coordination patterns based on the sagittal continuous relative phase (CRP). Methods Individuals with a unilateral transtibial amputation, transfemoral amputation and able-bodied individuals completed 6 minutes of treadmill walking in consecutive blocks of 2 minutes at self-selected (SS) speed, 75% SS speed and 125% SS speed. Lower extremity kinematics were captured and hip–knee and knee–ankle CRPs were calculated. Statistical non-parametric mapping was applied and statistical significance was set at 0.05. Results The hip–knee CRP at 75% SS walking speed with the TD was larger in the amputated limb of participants with a transfemoral amputation compared to able-bodied individuals at the beginning and end of the gait cycle (p = 0.009). In people with a transtibial amputation, the knee–ankle CRP at SS and 125% SS walking speeds with the TD were smaller in the amputated limb at the beginning of the gait cycle compared to able-bodied individuals (p = 0.014 and p = 0.014, respectively). Additionally, no significant differences were found between both prostheses. However, visual interpretation indicates a potential advantage of the TD over the individual's current prosthesis. Conclusion This study provides lower-limb coordination patterns in people with a lower-limb amputation and reveals a possible beneficial effect of the TD over the individuals’ current prosthesis. Future research should include a well-sampled investigation of the adaptation process combined with the prolonged effects of the TD.

One of the main restrictions of commercial cobots can be found in their limited payload to mass ratios. Flexible link manipulators seem to offer interesting advantages over traditional rigid robotics, in terms of lower self-weight, lower energy consumption and safer operation. However, the design and loading specifications in the general literature on flexible link manipulators differ from those expected in a collaborative industrial setting. In this paper, we want to investigate whether the use of flexible links can be truly beneficial for collaborative robotics. Firstly, the theoretical potential of flexible links to increase the payload to mass ratio is investigated. The feasibility to design a cylindrical flexible link for specific, realistic loading conditions is investigated, and the effect of link flexibility on the demanded motor torque and maximum reachable payload is visualised, while considering cylindrical links. Subsequently, to get insights in the accuracy and usability of such a manipulator, we experimentally quantify to what extent the undesired side effects of the flexible design can be counteracted using an appropriate controller. To comply with the envisioned application of collaborative robotics, a control strategy based on strain measurements along the link and robust to payload changes is proposed. The obtained accuracy was measured by tracking the end effector position using a Vicon motion capture system, considering two types of single link designs; firstly, a very flexible link setup with rectangular cross section, and secondly, a cylindrical flexible link, loaded to reach a payload to mass ratio of 1.

Soft fiber-reinforced actuators have demonstrated significant potential across various robotics applications. However, the actuation motion in these actuators is typically limited to a single type of motion behavior, such as bending, extending, and twisting. Additionally, a combination of bending with twisting and extending with twisting can occur in fiber-reinforced actuators. This paper presents two novel hybrid actuators in which shape memory alloy (SMA) wires are used as reinforcement for pneumatic actuation, and upon electrical activation, they create a twisting motion. As a result, the hybrid soft SMA-reinforced actuators can select between twisting and bending, as well as twisting and extending. In pneumatic mode, a bending angle of 40° and a longitudinal strain of 20% were achieved for the bending/twisting and extending/twisting actuators, respectively. When the SMA wires are electrically activated by the Joule effect, the actuators achieved more than 90% of the maximum twisting angle (24°) in almost 2 s. Passive recovery, facilitated by the elastic response of the soft chamber, took approximately 10 s. The double-helical reinforcement by SMA wires not only enables twisting in both directions but also serves as an active recovery mechanism to more rapidly return the finger to the initial position (within 2 s). The resulting pneumatic–electric-driven soft actuators enhance dexterity and versatility, making them suitable for applications in walking robots, in-pipe crawling robots, and in-hand manipulation.

This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers. The acquired point cloud scans, including ground-truth transformations, are made publicly available. This dataset includes several cleaned-up scans of nine 3D-printed objects. Our main contribution lies in assessing the performance of three classical (GO-ICP, RANSAC, FGR) and three learning-based (PointNetLK, RPMNet, ROPNet) methods on real-world scans, using a wide range of metrics. These include recall, accuracy and computation time. Our comparison shows a high accuracy for GO-ICP, as well as PointNetLK, RANSAC and RPMNet combined with ICP refinement. However, apart from GO-ICP, all methods show a significant number of failure cases when applied to scans containing more noise or requiring larger transformations. FGR and RANSAC are among the quickest methods, while GO-ICP takes several seconds to solve. Finally, while learning-based methods demonstrate good performance and low computation times, they have difficulties in training and generalizing. Our results can aid novice researchers in the field in selecting a suitable registration method for their application, based on quantitative metrics. Furthermore, our code can be used by others to evaluate novel methods.

Ultra-wideband (UWB) has gained increasing interest for providing real-time positioning to robots in GPS-denied environments. For a robot to act on this information, it also requires its heading. This is, however, not provided by UWB. To overcome this, either multiple tags are used to create a local reference frame connected to the robot or a single tag is combined with ego-motion estimation from odometry or Inertial Measurement Unit (IMU) measurements. Both odometry and the IMU suffer from drift, and it is common to use a magnetometer to correct the drift on the heading; however, magnetometers tend to become unreliable in typical GPS-denied environments. To overcome this, a lightweight particle filter was designed to run in real time. The particle filter corrects the ego-motion heading and location drift using the UWB measurements over a moving horizon time frame. The algorithm was evaluated offline using data sets collected from a ground robot that contains line-of-sight (LOS) and non-line-of-sight conditions. An RMSE of 13 cm and 0.12 (rad) was achieved with four anchors in the LOS condition. It is also shown that it can be used to provide the robot with real-time position and heading information for the robot to act on it in LOS conditions, and it is shown to be robust in both experimental conditions.

The variability in the shapes and sizes of objects presents a significant challenge for two-finger robotic grippers when it comes to manipulating them. Based on the chemistry of vitrimers (a new class of polymer materials that have dynamic covalent bonds, which allow them to reversibly change their mechanical properties under specific conditions), we present two designs as 3D-printed shape memory polymer-based shape-adaptive fingertips (SMP-SAF). The fingertips have two main properties needed for an effective grasping. First, the ability to adapt their shape to different objects. Second, exhibiting variable rigidity, to lock and retain this new shape without the need for any continuous external triggering system. Our two design strategies are: 1) A curved part, which is suitable for grasping delicate and fragile objects. In this mode and prior to gripping, the SMP-SAFs are straightened by the force of the parallel gripper and are adapted to the object by shape memory activation. 2) A straight part that takes on the form of the objects by contact force with them. This mode is better suited for gripping hard bodies and provides a more straightforward shape programming process. The SMP-SAFs can be programmed by heating them up above glass transition temperature (54°C) via Joule-effect of the integrated electrically conductive wire or by using a heat gun, followed by reshaping by the external forces (without human intervention), and subsequently fixing the new shape upon cooling. As the shape programming process is time-consuming, this technique suits adaptive sorting lines where the variety of objects is not changed from grasp to grasp, but from batch to batch.

Occupational back-support exoskeletons, categorized as active or passive, hold promise for mitigating work-related musculoskeletal disorders. However, their impact on combined physical and cognitive aspects of industrial work performance remains inadequately understood, especially regarding potential differences between exoskeleton categories. A randomized, counterbalanced cross-over study was conducted, comparing the active CrayX, passive Paexo Back, and a no exoskeleton condition. A 15-min dual task was used to simulate both cognitive and physical aspects of industrial work performance. Cognitive workload parameters included reaction time, accuracy, and subjective measures. Physical workload included movement duration, segmented in three phases: (1) walking to and grabbing the box, (2) picking up, carrying, and putting down the box, and (3) returning to the starting point. Comfort of both devices was also surveyed. The Paexo significantly increased movement duration in the first segment compared to NoExo (Paexo = 1.55 ± 0.19 s; NoExo = 1.32 ± 0.17 s; p

Robot-to-human communication is important for mutual understanding during human-robot collaboration. Most of the current collaborative robots (cobots) are designed with low levels of anthropomorphism. Therefore, the ability of cobots to express human-like communication is limited. In this work, we present an open-source platform named Antropo to increase the level of anthropomorphism of Franka Emika-a widely used collaborative robot arm. The Antropo platform includes three modules: a camera module for expressing eye gaze, a light module for visual feedback, and a sound module for acoustic feedback. These modules can be rapidly prototyped through 3D printers, laser-cutters, and off-the-shelf components available at a low cost. The Antropo platform can be easily installed on the Franka Emika robot. The added communication channels can be synchronised with the robot's motions to enhance mutual understanding. All hardware CAD design files and software files are released. The platform can be used to study human-like behaviours of cobots and the effects of these behaviours on different aspects of human-robot collaboration. We demonstrate the Antropo platform in an assembly task in which the Franka Emika robot expresses various human-like communicative behaviours via the added communication channels. We also present two industrial applications in which the Antropo platform was customised for the Universal Robots UR16e.

Despite being primarily categorized as non-autonomous self-healing polymers, we demonstrate the ability of Diels–Alder polymers to heal macroscopic damages at room temperature, resulting in complete restoration of their mechanical properties within a few hours. Moreover, we observe immediate partial recovery, occurring mere minutes after reuniting the fractured surfaces. This fast room-temperature healing is accomplished by employing an off-stoichiometric maleimide-to-furan ratio in the polymer network. Through an extensive investigation of seven Diels–Alder polymers, the influence of crosslink density on self-healing, thermal, and (thermo-)mechanical performance was thoroughly examined. Crosslink density variations were achieved by adjusting the molecular weight of the monomers or utilizing the off-stoichiometric maleimide-to-furan ratio. Quasistatic tensile testing, dynamic mechanical analysis, dynamic rheometry, differential scanning calorimetry, and thermogravimetric analysis were employed to evaluate the individual effects of these parameters on material performance. While lowering the crosslink density in the polymer network via decreasing the off-stoichiometric ratio demonstrated the greatest acceleration of healing, it also led to a slight decrease in (dynamic) mechanical performance. On the other hand, reducing crosslink density using longer monomers resulted in faster healing, albeit to a lesser extent, while maintaining the (dynamic) mechanical performance.

Locomotion mode recognition provides the prosthesis control with the information on when to switch between different walking modes, whereas the gait phase detection indicates where we are in the gait cycle. But powered prostheses often implement a different control strategy for each locomotion mode to improve the functionality of the prosthesis. Existing studies employed several classical machine learning methods for locomotion mode recognition. However, these methods were less effective for data with complex decision boundaries and resulted in misclassifications of motion recognition. Deep learning-based methods potentially resolve these limitations as it is a special type of machine learning method with more sophistication. Therefore, this study evaluated three deep learning-based models for locomotion mode recognition, namely recurrent neural network (RNN), long short-term memory (LSTM) neural network, and convolutional neural network (CNN), and compared the recognition performance of deep learning models to the machine learning model with random forest classifier (RFC). The models are trained from data of one inertial measurement unit (IMU) placed on the lower shanks of four able-bodied subjects to perform four walking modes, including level ground walking (LW), standing (ST), and stair ascent/stair descent (SA/SD). The results indicated that CNN and LSTM models outperformed other models, and these models were promising for applying locomotion mode recognition in real-time for robotic prostheses.

Damage detection is one of the critical challenges in operating soft robots in an industrial setting. In repetitive tasks, even a small cut or fatigue can propagate to large damage ceasing the complete operation process. Although research has shown that damage detection can be performed through an embedded sensor network, this approach leads to complicated sensorized systems with additional wiring and equipment, made using complex fabrication processes and often compromising the flexibility of the soft robotic body. Alternatively, in this paper, we proposed a non-invasive approach for damage detection and localization on soft grippers. The essential idea is to track changes in non-linear dynamics of a gripper due to possible damage, where minor changes in material and morphology lead to large differences in the force and torque feedback over time. To test this concept, we developed a classification model based on a bidirectional long short-time memory (biLSTM) network that discovers patterns of dynamics changes in force and torque signals measured at the mounting point. To evaluate this model, we employed a two-fingered Fin Ray gripper and collected data for 43 damage configurations. The experimental results show nearly perfect damage detection accuracy and 97% of its localization. We have also tested the effect of the gripper orientation and the length of time-series data. By shaking the gripper with an optimal roll angle, the localization accuracy can exceed 95% and increase further with additional gripper orientations. The results also show that two periods of the gripper oscillation, i.e., roughly 50 data points, are enough to achieve a reasonable level of damage localization.

Abstract Objective Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe’s secondary industries. Methods Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet. Results The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles. Conclusion WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

Untethered robots carry their own power supply in the form of a battery pack, which has a crucial impact on the robot’s performance. Although battery technologies are richly studied and optimized for applications such as electric vehicles, computers and smartphones, they are often a mere afterthought in the design process of a robot system. This tutorial paper proposes criteria to evaluate the suitability of different battery technologies for robotic applications. Taking into consideration the requirements of different applications, the capabilities of relevant battery technologies are evaluated and compared. The tutorial also discusses current limitations and new technological developments, pointing out opportunities for interdisciplinary research between the battery technology and robotics communities.

Wearable robots have become increasingly prevalent in various applications, including rehabilitation, power augmentation, and assistance. However, one of the challenges in designing wearable robots is how to attach them to the human body. The attachment method should be secure, reliable, comfortable, effective, and controlled for the user. Moreover, the attachment points should not interfere with the user’s daily activities, and the attachment process should not be time-consuming or complicated. Typical straps nowadays require a time-consuming and cumbersome donning and doffing procedure from therapists for users needing rehabilitation therapy. Therefore, we propose a novel pneumatically actuated soft strap to enclose the limb and automate part of the strapping procedure. This paper proposes a preliminary design utilizing soft bending actuators for attaching physical interfaces to humans, with integrated active elements for facilitating and automating the strapping process. Finite element analysis was conducted to assess pressure requirements, bending curvature, and geometry, with simulation results demonstrating a promising agreement, with a root mean square error (RMSE) of 3.4° in bending angle. In the future, an additional locking mechanism would be required to provide the necessary holding force and fully constrain the limb.

Musculoskeletal disorders caused by poor work posture are a serious concern in the industry since they lead to absenteeism and medical leave from work. In the context of human–robot collaboration, this issue can be mitigated if collaborative robots support human workers to perform their tasks more ergonomically. In this work, we propose a method to optimize human posture during human–robot collaboration using the Particle Swarm Optimization (PSO) algorithm. Our approach involves assigning an appropriate location to the robot’s end-effector to minimize the distance between the optimized posture of the human and their current posture in the working space. To measure human posture, we use the Rapid Entire Body Assessment score (REBA) calculated from body joint angles captured by a Kinect camera. To validate the effectiveness of our proposed method, we conducted a user study with 20 participants in a virtual reality environment. The PSO algorithm could position the robot end-effector to the optimal position close to real time. Our results showed that our method could improve ergonomics by 66%, indicating its potential for use in human–robot collaborative applications.

Brain–computer interfaces (BCIs) have the potential to enable individuals to interact with devices by detecting their intention from brain activity. A common approach to BCI is to decode movement intention from motor imagery (MI), the mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition devices with a high number of sensors are typically necessary to achieve the spatial resolution required for reliable analysis. This entails high monetary and computational costs that make these approaches impractical for everyday use. This study investigates the trade-off between accuracy and complexity when decoding MI from fewer EEG sensors. Data were acquired from 15 healthy participants performing MI with a 64-channel research-grade EEG device. After performing a quality assessment by identifying visually evoked potentials, several decoding pipelines were trained on these data using different subsets of electrode locations. No significant differences (p = [0.18–0.91]) in the average decoding accuracy were found when using a reduced number of sensors. Therefore, decoding MI from a limited number of sensors is feasible. Hence, using commercial sensor devices for this purpose should be attainable, reducing both monetary and computational costs for BCI control.

In the field of soft robotics, knowledge of material science is becoming more and more important. However, many researchers have a background in only one of both domains. To aid the understanding of the other domain, this tutorial describes the complete process from polymer synthesis over fabrication to testing of a soft finger. Enough background is provided during the tutorial such that researchers from both fields can understand and sharpen their knowledge. Self-healing polymers are used in this tutorial, showing that these polymers that were once a specialty, have become accessible for broader use. The use of self-healing polymers allows soft robots to recover from fatal damage, as shown in this tutorial, which increases their lifespan significantly.

The number of collaborative robots that perform different tasks in close proximity to humans is increasing. Previous studies showed that enabling non-expert users to program a cobot reduces the cost of robot maintenance and reprogramming. Since this approach is based on an interaction between the cobot and human partners, in this study, we investigate whether making this interaction more transparent can improve the interaction and lead to better performance for non-expert users. To evaluate the proposed methodology, an experiment with 67 participants is conducted. The obtained results show that providing explanation leads to higher performance, in terms of efficiency and efficacy, i.e., the number of times the task is completed without teaching a wrong instruction to the cobot is two times higher when explanations are provided. In addition, providing explanation also increases users’ satisfaction and trust in working with the cobot.

Abstract Background In the last decades, several powered ankle-foot orthoses have been developed to assist the ankle joint of their users during walking. Recent studies have shown that the effects of the assistance provided by powered ankle-foot orthoses depend on the assistive profile. In compliant actuators, the stiffness level influences the actuator’s performance. However, the effects of this parameter on the users has not been yet evaluated. The goal of this study is to assess the effects of the assistance provided by a variable stiffness ankle actuator on healthy young users. More specifically, the effect of different onset times of the push-off torque and different actuator’s stiffness levels has been investigated. Methods Eight healthy subjects walked with a unilateral powered ankle-foot orthosis in several assisted walking trials. The powered orthosis was actuated in the sagittal plane by a variable stiffness actuator. During the assisted walking trials, three different onset times of the push-off assistance and three different actuator’s stiffness levels were used. The metabolic cost of walking, lower limb muscles activation, joint kinematics, and gait parameters measured during different assisted walking trials were compared to the ones measured during normal walking and walking with the powered orthosis not providing assistance. Results This study found trends for more compliant settings of the ankle actuator resulting in bigger reductions of the metabolic cost of walking and soleus muscle activation in the stance phase during assisted walking as compared to the unassisted walking trial. In addition to this, the study found that, among the tested onset times, the earlier ones showed a trend for bigger reductions of the activation of the soleus muscle during stance, while the later ones led to a bigger reduction in the metabolic cost of walking in the assisted walking trials as compared to the unassisted condition. Conclusions This study presents a first attempt to show that, together with the assistive torque profile, also the stiffness level of a compliant ankle actuator can influence the assistive performance of a powered ankle-foot orthosis.

The use of powered ankle-foot prostheses for below-knee amputees leads to challenges like the peak power of the applied actuator and biomechanical features of the prosthesis foot. This paper proposes an efficient powered ankle-foot prosthesis with a series elastic actuator. By combining the geared five-bar spring (GFBS) mechanism and the traditional series elastic actuator (SEA), a series elastic with geared five-bar (SGFB) actuator is built. The new SGFB actuator has the benefits of both the GFBS and the SEA on mimicking biomechanics of the human ankle and reducing the peak power of the motor. The healthy walking gait in the experiment results indicates that the optimized SGFB prosthesis foot including a 150W Maxon DC motor can provide a 70kg subject enough net positive energy with an energy efficiency of 35.3% during normal speed walking in the treadmill trials. The experiment of the SGFB prosthesis foot in semi-active mode shows the advantage on closely mimicking the human biomechanics during the control dorsiflexion phase and the importance of injecting positive energy during the powered plantarflexion phase. The experiment results also show that the optimization of different parameters within the electromechanical model considering the efficiency of the whole drive train can effectively reduce the motor's peak power to 132 W by making the motor more effective in high-power conditions.

Robots have been used in joint attention (JA) tasks with children with autism spectrum disorder (ASD). However, very few studies compared JA performance of children with ASD with typically developing children (TD) when interacting with a robotic partner and a traditional human partner. This study aims to: (a) to explore whether there are differences in response to and initiation of JA between ASD and TD children with two interactive partners: an adult and a social robot (NAO); and (b) to explore which characteristics of ASD children predicting their performance in robot-assisted JA tasks. Twenty-seven ASD and forty TD children were involved in this study in which they were exposed to diffident JA tasks. Mixed results were found per type of JA behavior over groups and conditions. Our results show that both ASD and TD children performed better with the human partner than with the robot in response to JA tasks. Among the characteristics of ASD children, ADOS total score is associated with response to JA performance. No significant result related to initiation of JA was found.

With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the in situ printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated in situ strain sensing elements, showed that material selection affects the sensor signal significantly.

Human motion intention detection is an essential part of the control of upper-body exoskeletons. While surface electromyography (sEMG)-based systems may be able to provide anticipatory control, they typically require exact placement of the electrodes on the muscle bodies which limits the practical use and donning of the technology. In this study, we propose a novel physical interface for exoskeletons with integrated sEMG- and pressure sensors. The sensors are 3D-printed with flexible, conductive materials and allow multi-modal information to be obtained during operation. A K-Nearest Neighbours classifier is implemented in an off-line manner to detect reaching movements and lifting tasks that represent daily activities of industrial workers. The performance of the classifier is validated through repeated experiments and compared to a unimodal EMG-based classifier. The results indicate that excellent prediction performance can be obtained, even with a minimal amount of sEMG electrodes and without specific placement of the electrode.

This article provides a theory for provably safe and computationally efficient distributed constrained control, and describes an application to a swarm of nano-quadrotors with limited on-board hardware and subject to multiple state and input constraints. We provide a formal extension of the explicit reference governor framework to address the case of distributed systems. The efficacy, robustness, and scalability of the proposed theory is demonstrated by an extensive experimental validation campaign and a comparative simulation study on single and multiple nano-quadrotors. The control strategy is implemented in real-time on-board palm-sized unmanned erial vehicles, and achieves safe swarm coordination without relying on any offline trajectory computations.

Humins waste valorization is considered to be an essential pathway to improve the economic viability of many biorefinery processes and further promote their circularity by avoiding waste formation. In this research, the incorporation of humins in a Diels–Alder (DA) polymer network based on furan-maleimide thermoreversible crosslinks was studied. A considerable enhancement of the healing efficiency was observed by just healing for 1 h at 60 °C at the expense of a reduction of the material mechanical properties, while the unfilled material showed no healing under the same conditions. Nevertheless, the thermal healing step favored the irreversible humins polycondensation, thus strengthening the material while keeping the enhanced healing performance. Our hypothesis states a synergistic healing mechanism based on humins flowing throughout the damage, followed by thermal humins crosslinking during the healing trigger, together with DA thermoreversible bonds recombination. A multi-material soft robotic gripper was manufactured out of the proposed material, showing not only improved recovery of the functional performance upon healing but also stiffness-tunable features by means of humins thermal crosslinking. For the first time, both damage healing and zone reinforcement for further damage prevention are achieved in a single intrinsic self-healing system.

Abstract In the last two decades, numerous powered ankle-foot orthoses have been developed. Despite similar designs and control strategies being shared by some of these devices, their performance in terms of achieving a comparable goal varies. It has been shown that the effect of powered ankle-foot orthoses on healthy users is altered by some factors of the testing protocol. This paper provides an overview of the effect of powered walking on healthy and weakened users. It identifies a set of key factors influencing the performance of powered ankle-foot orthoses, and it presents the effects of these factors on healthy subjects, highlighting the similarities and differences of the results obtained in different works. Furthermore, the outcomes of studies performed on elderly and impaired subjects walking with powered ankle-foot orthoses are compared, to outline the effects of powered walking on these users. This article shows that several factors mutually influence the performance of powered ankle-foot orthoses on their users and, for this reason, the determination of their effects on the user is not straightforward. One of the key factors is the adaptation of users to provided assistance. This factor is very important for the assessment of the effects of powered ankle-foot orthoses on users, however, it is not always reported by studies. Moreover, future works should report, together with the results, the list of influencing factors used in the protocol, to facilitate the comparison of the obtained results. This article also underlines the need for a standardized method to benchmark the actuators of powered ankle-foot orthoses, which would ease the comparison of results between the performed studies. In this paper, the lack of studies on elderly and impaired subjects is highlighted. The insufficiency of these studies makes it difficult to assess the effects of powered ankle-foot orthoses on these users. To summarize, this article provides a detailed overview of the work performed on powered ankle-foot orthoses, presenting and analyzing the results obtained, but also emphasizing topics on which more research is still required.

Brain-machine interfaces (BMI) allows individuals to control an external device by controlling their own brain activity, without requiring bodily or muscle movements. Performing voluntary movements is associated with the experience of agency ("sense of agency") over those movements and their outcomes. When people voluntarily control a BMI, they should likewise experience a sense of agency. However, using a BMI to act presents several differences compared to normal movements. In particular, BMIs lack sensorimotor feedback, afford lower controllability and are associated with increased cognitive fatigue. Here, we explored how these different factors influence the sense of agency across two studies in which participants learned to control a robotic hand through motor imagery decoded online through electroencephalography. We observed that the lack of sensorimotor information when using a BMI did not appear to influence the sense of agency. We further observed that experiencing lower control over the BMI reduced the sense of agency. Finally, we observed that the better participants controlled the BMI, the greater was the appropriation of the robotic hand, as measured by body-ownership and agency scores. Results are discussed based on existing theories on the sense of agency in light of the importance of BMI technology for patients using prosthetic limbs.

The current state of the art in compliant actuation has already good performance, but this is still insufficient to provide a decent autonomy for the next generation of robots. In this paper, a next step is taken to improve the efficiency of actuators by tackling and enhancing the Series-Parallel Elastic Constant Torque Actuation (SPECTA) concept, which has previously been analyzed in simulations. In this work, the efficiency is increased further by decoupling the springs and their driving parts through the use of locking mechanisms, such that the motors are not always loaded and the springs can easily store energy from both input or output. Simulations have been performed to confirm this and they also showed that, in the SPECTA concept, it is always better to use high-speed motors instead of high-torque motors, even with non-efficient gearing. In this paper, the SPECTA concept is also validated experimentally with the use of a newly built test setup. In light of the obtained results, showing an increase in efficiency for almost all working points, it can be stated that SPECTA is a promising new actuation technology that allows for an increase in energy recuperation, efficiency, and autonomy.

We present a dataset of behavioral data recorded from 61 children diagnosed with Autism Spectrum Disorder (ASD). The data was collected during a large-scale evaluation of Robot Enhanced Therapy (RET). The dataset covers over 3000 therapy sessions and more than 300 hours of therapy. Half of the children interacted with the social robot NAO supervised by a therapist. The other half, constituting a control group, interacted directly with a therapist. Both groups followed the Applied Behavior Analysis (ABA) protocol. Each session was recorded with three RGB cameras and two RGBD (Kinect) cameras, providing detailed information of children's behavior during therapy. This public release of the dataset comprises body motion, head position and orientation, and eye gaze variables, all specified as 3D data in a joint frame of reference. In addition, metadata including participant age, gender, and autism diagnosis (ADOS) variables are included. We release this data with the hope of supporting further data-driven studies towards improved therapy methods as well as a better understanding of ASD in general.

Abstract Background Here we present how the CYBERLEGs Beta-Prosthesis was modified with a new control system to participate in the Powered Leg Prosthesis event, and to report on our experience at the CYBATHLON 2016 which was held in Zurich, Switzerland in October 2016. The prosthesis has two active degrees of freedom which assist the user with extra joint power at the knee and ankle to complete tasks. The CYBATHLON is a championship for people with disabilities competing in six disciplines, using advanced assistive devices. Tasks for CYBATHLON 2016 were chosen to reflect everyday normal task such as sitting and standing from a chair, obstacle avoidance, stepping stones, slope walking and descent, and stair climbing and descent. Methods The control schemata were presented along with the description of each of the six tasks. The participant of the competition, the pilot, ran through each of the trials under lab conditions and representative behaviors were recorded. Results The VUB CYBERLEGs prosthesis was able to accomplish, to some degree, five of the six tasks and here the torque and angle behaviors of the device while accomplishing these tasks are presented. The relatively simple control methods were able to provide assistive torque during many of the events, particularly sit to stand and stair climbing. For example, the prosthesis was able to consistently provide over 30 Nm in arresting knee torque in the sitting task, and over 20 Nm while standing. Peak torque of the device was not sufficient for unassisted stair climbing, but was able to provide around 60 Nm of assistance in both ascent and descent. Use of the passive behaviors of the device were shown to be able to trigger state machine events reliably for certain tasks. Conclusions Although the performance of the CYBERLEGs prosthesis during CYBATHLON 2016 did not compare to the other top of the market designs with regards to speed, the device performed all of the tasks that were deemed possible by the start of the competition. Moreover, the Pilot was able to accomplish tasks in ways the Pilot’s personal microcontrolled prosthesis could not, with limited powered prosthesis training. Future studies will focus on decreasing weight, increasing reliability, incorporating better control, and increasing the velocity of the device. This is only a case study and actual benefits to clinical outcomes are not yet understood and need to be further investigated. This competition was a unique experience to illuminate problems that future versions of the device will be able to solve.

With the growing popularity of Human–Robot Interactions, a series of robotic assistive devices have been created over the last decades. However, due to the lack of easily integrable resources, the development of these custom made devices turns out to be long and expensive. Therefore, the SMARCOS, a novel off-the-shelf Smart Variable Stiffness Actuator for human-centered robotic applications is proposed in this paper. This modular actuator combines compliant elements and sensors as well as low-level controller and high-bandwidth communication. The characterisation of the actuator is presented in this manuscript, followed by two use-cases wherein the benefits of such technology can be truly exploited. The actuator provides a lightweight design that can serve as the building blocks to facilitate the development of robotic applications.

Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules.

In recent work, the thermoreversible Diels–Alder reaction between furan and maleimide functional groups has been studied extensively in the context of self-healing elastomers and thermosets. To elaborate the influence of the stoichiometric ratio between the maleimide and furan reactive groups on the thermomechanical properties and viscoelastic behavior of formed reversible covalent polymer networks, a series of Diels–Alder-based networks with different stoichiometric ratios was synthesized. Differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA) and dynamic rheology measurements were performed on the reversible polymer networks, to relate the reversible network structure to the material properties and reactivity. Such knowledge allows the design and optimization of the thermomechanical behavior of the reversible networks for intended applications. Lowering the maleimide-to-furan ratio creates a deficit of maleimide functional groups, resulting in a decrease in the crosslink density of the system, and a consequent decrease in the glass transition temperature, Young’s modulus, and gel transition temperature. The excess of unreacted furan in the system results in faster reaction and healing kinetics and a shift of the reaction equilibrium.

Contribution: This article presents an engineering outreach activity that aims to teach K-12 students how to develop a tendon-based soft robotic finger. The primary objectives of this STEM activity are to introduce students to the fundamentals of soft robotics, its interdisciplinary nature, and to offer them a hands-on and engaging learning experience using the project-based- learning approach. Background: Soft robotics, an interdisciplinary field combining chemistry, materials science, and robotics, has the potential to revolutionize the design and development of robots. However, introducing the fundamental concepts of soft robotics to K-12 students can be challenging since traditional robotic activities often require complex programming, technical expertise and expensive equipment and software. Intended Outcomes: Increasing the students' understanding of soft robotics principles, materials, and polymer processing. Positively impacting students' perception of engineering as a potential career path by enhancing their attitudes toward STEM. Application Design: Students could develop manually actuated soft robotic fingers within a 45-min workshop by utilizing 3-D printed molds, rapidly curing elastomeric materials, and the basic mold casting method. The outreach activity is intentionally designed to simplify the technology used by eliminating the need for complex programming, and to focus on utilizing novel materials and basic concepts to construct actuating soft robots, providing an effective and engaging STEM activity for K-12 students. Findings: The success/effectiveness of the activity was evaluated in three ways: 1) through direct inspection on the performance of the student-fabricated soft finger during the workshop; 2) through the pre- and post-tests to evaluate the learning outcomes; and 3) by conducting a STEM outreach survey to gather student feedback on the quality of the outreach activity and their attitudes toward STEM. During the workshop activities, the students were able to effectively follow the instructions, construct a tendon-based soft robotic finger, and manually actuate the finger using the tendon. According to the results of pre- and post-tests, the students increased their understanding regarding the principles of soft robotics, materials and polymer processing. Furthermore, the STEM outreach survey of IEEE powered "TryEngineering Portal" revealed that the developed outreach activity enhanced the achievement of pedagogical and quality outcome goals and measures, as well as program targets and objectives.

Soft pneumatic actuators with a channel network (pneu-net) based on thermoplastic elastomers are compatible with fused deposition modeling (FDM). However, conventional filament-based fused deposition modeling (FDM) printers are not well suited for thermoplastic elastomers with a shore hardness (Sh < 70A). Therefore, in this study, a pellet-based FDM printer was used to print pneumatic actuators with a shore hardness of Sh18A. Additionally, the method allowed the in situ integration of soft piezoresistive sensing elements during the fabrication. The integrated piezoresistive elements were based on conductive composites made of three different styrene-ethylene-butylene-styrene (SEBS) thermoplastic elastomers, each with a carbon black (CB) filler with a ratio of 1:1. The best sensor behavior was achieved by the SEBS material with a shore hardness of Sh50A. The dynamic and quasi-static sensor behavior were investigated on SEBS strips with integrated piezoresistive sensor composite material, and the results were compared with TPU strips from a previous study. Finally, the piezoresistive composite was used for the FDM printing of soft pneumatic actuators with a shore hardness of 18 A. It is worth mentioning that 3 h were needed for the fabrication of the soft pneumatic actuator with an integrated strain sensing element. In comparison to classical mold casting method, this is faster, since curing post-processing is not required and will help the industrialization of pneumatic actuator-based soft robotics.

Achieving human-like locomotion with humanoid platforms often requires the use of variable stiffness actuators (VSAs) in multi-degree-of-freedom robotic joints. VSAs possess 2 motors for the control of both stiffness and equilibrium position. Hence, they add mass and mechanical complexity to the design of humanoids. Mass distribution of the legs is an important design parameter, because it can have detrimental effects on the cost of transport. This work presents a novel VSA module, designed to be implemented in a bio-inspired humanoid robot, Binocchio, that houses all components on the same side of the actuated joint. This feature allowed to place the actuator's mass to more proximal locations with respect to the actuated joint instead of concentrating it at the joint level, creating a more favorable mass distribution in the humanoid. Besides, it also facilitated it's usage in joints with centralized multi-degree of freedom (DoF) joints instead of cascading single DoF modules. The design of the VSA module is presented, including it's integration in the multi-DoFs joints of Binocchio. Experiments validated the static characteristics of the VSA module to accurately estimate the output torque and stiffness. The dynamic responses of the driving and stiffening mechanisms are shown. Finally, experiments show the ability of the actuation system to replicate the envisioned human-like kinematic, torque and stiffness profiles for Binocchio.

ObjectivesThe objectives are to determine neural dynamics during gait using electro-encephalography and source localization, and to investigate the attentional demand during walking in able-bodied individuals, and individuals with an amputation.Materials & methodsSix able-bodied individuals conducted one experimental trial, and 6 unilateral transtibial and 6 unilateral transfemoral amputees performed 2 experimental trials; the first with the prosthesis currently used by the subjects and the second with a novel powered transtibial prosthesis, i.e. the Ankle Mimicking Prosthetic foot 4.0. Each experimental trial comprised 2 walking tasks; 6 and 2 minutes treadmill walking at normal speed interspersed by 5 minutes of rest. During 6 minutes walking the Sustained Attention to Response (go-no go) Task, which measures reaction time and accuracy, was performed. Electro-encephalographic data were gathered when subjects walked 2 minutes. Motor-related cortical potentials and brain source activity during gait were examined. Normality and (non-) parametric tests were conducted (pResults and discussionIn contrast to transtibial amputees, transfemoral amputees required more attentional demands during walking with Ankle Mimicking Prosthetic foot 4.0 compared to the current passive prosthetic device and able-bodied individuals (reaction time and accuracy: p≤0.028). Since risk of falling is associated with altered attentional demands, propulsive forces of the novel device need to be better controlled for transfemoral amputees. No motor-related cortical potentials at Cz were observed in transfemoral amputees walking with the novel prosthesis, whereas motor-related cortical potentials between transtibial amputees and able-bodied individuals during walking at normal speed did not differ. The first positive electro-physiological peak deflection appeared during toe-off phase and showed higher activity within the underlying brain sources in transtibial amputees walking with Ankle Mimicking Prosthetic foot 4.0 compared to able-bodied individuals. The required higher neural input to accomplish the same physical activity compared to able-bodied individuals is possibly due to the limited acclimation period to the novel device and consequently increased afferent sensory feedback for postural control.

Despite enhancements in the development of robotic systems, the energy economy of today's robots lags far behind that of biological systems. This is in particular critical for untethered legged robot locomotion. To elucidate the current stage of energy efficiency in legged robotic systems, this paper provides an overview on recent advancements in development of such platforms. The covered different perspectives include actuation, leg structure, control and locomotion principles. We review various robotic actuators exploiting compliance in series and in parallel with the drive-train to permit energy recycling during locomotion. We discuss the importance of limb segmentation under efficiency aspects and with respect to design, dynamics analysis and control of legged robots. This paper also reviews a number of control approaches allowing for energy efficient locomotion of robots by exploiting the natural dynamics of the system, and by utilizing optimal control approaches targeting locomotion expenditure. To this end, a set of locomotion principles elaborating on models for energetics, dynamics, and of the systems is studied.

The CYBERLEGs Beta-Prosthesis is an active transfemoral prosthesis that can provide the full torque required for reproducing average level ground walking at both the knee and ankle in the sagittal plane. The prosthesis attempts to produce a natural level ground walking gait that approximates the joint torques and kinematics of a non-amputee while maintaining passively compliant joints, the stiffnesses of which were derived from biological quasi-stiffness measurements. The ankle of the prosthesis consists of a series elastic actuator with a parallel spring and the knee is composed of three different systems that must compliment each other to generate the correct joint behavior: a series elastic actuator, a lockable parallel spring and an energy transfer mechanism. Bench testing of this new prosthesis was completed and demonstrated that the device was able to create the expected torque-angle characteristics for a normal walker under ideal conditions. The experimental trials with four amputees walking on a treadmill to validate the behavior of the prosthesis proved that although the prosthesis could be controlled in a way that allowed all subjects to walk, the accurate timing and kinematic requirements of the output of the device limited the efficacy of using springs with quasi-static stiffnesses. Modification of the control and stiffness of the series springs could provide better performance in future work.

Sensing pressure at the physical interface between the robot and the human has important implications for wearable robots. On the one hand, monitoring pressure distribution can give valuable benefits on the aspects of comfortability and safety of such devices. Additionally, on the other hand, they can be used as a rich sensory input to high level interaction controllers. However, a problem is that the commercial availability of this technology is mostly limited to either low-cost solutions with poor performance or expensive options, limiting the possibilities for iterative designs. As an alternative, in this manuscript we present a three-dimensional (3D) printed flexible capacitive pressure sensor that allows seamless integration for wearable robotic applications. The sensors are manufactured using additive manufacturing techniques, which provides benefits in terms of versatility of design and implementation. In this study, a characterization of the 3D printed sensors in a test-bench is presented after which the sensors are integrated in an upper arm interface. A human-in-the-loop calibration of the sensors is then shown, allowing to estimate the external force and pressure distribution that is acting on the upper arm of seven human subjects while performing a dynamic task. The validation of the method is achieved by means of a collaborative robot for precise force interaction measurements. The results indicate that the proposed sensors are a potential solution for further implementation in human–robot interfaces.

Due to the epochal changes introduced by “Industry 4.0”, it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human–robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative “direct instrumental evaluations” and “rating of standard methods”, allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers’ awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

Background: Social robots may represent an important technological instrument for improving the social abilities of children with autism spectrum disorders (ASD). Objectives: This work describes exploratory research in which a doctor game scenario, involving a social robot and a human partner, was implemented and tested with children with ASD. Patients and Methods: Thirty children between the ages of four and eight who had been diagnosed with ASD were included in this study. The children alternated between playing a doctor game with the social robot Probo and playing the same game with a human partner, and were exposed to each condition only once in a counter-balanced order. The symbolic play task and the experimental setup were designed to test whether the children would engage in more collaborative behaviors while playing the doctor game with a social robot than performing the same activities with the human partner. Results: Although the children with ASD were more interested in and entertained by the robotic partner, the children did not have better performance in the game, and did not show significantly more collaborative play and engagement while playing with the social robot, compared with the human partner. Conclusions: A longer and more thorough study is needed to interpret these findings and better understand the ASD child-robot interaction. This paper concludes with a discussion of the related literature and proposals for future research directions.

Fast and accurate gait phase detection is essential to achieve effective powered lower-limb prostheses and exoskeletons. As the versatility but also the complexity of these robotic devices increases, the research on how to make gait detection algorithms more performant and their sensing devices smaller and more wearable gains interest. A functional gait detection algorithm will improve the precision, stability, and safety of prostheses, and other rehabilitation devices. In the past years the state-of-the-art has advanced significantly in terms of sensors, signal processing, and gait detection algorithms. In this review, we investigate studies and developments in the field of gait event detection methods, more precisely applied to prosthetic devices. We compared advantages and limitations between all the proposed methods and extracted the relevant questions and recommendations about gait detection methods for future developments.