The purpose of this study was to assess an upper body exoskeleton during automotive assembly processes that involve elevated arm postures. Sixteen team members at Toyota Motor Manufacturing Canada were fitted with a Levitate Airframe, and each team member performed between one and three processes with and without the exoskeleton. A total of 16 assembly processes were studied. Electromyography (EMG) data were collected on the anterior deltoid, biceps brachii, upper trapezius, and erector spinae. Team members also completed a usability survey. The exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p = .01, Δ = −3.2 %MVC, d = 0.56 medium effect) and fatigue risk value (p < .01, Δ = −5.1 %MVC, d = 0.62 medium effect) across the assembly processes, with no significant changes for the other muscles tested. A subset of nine assembly processes with a greater amount of time spent in arm elevations at or above 90° (30 vs. 24%) and at or above 135° (18 vs. 9%) appeared to benefit more from exoskeleton usage. For these processes, the exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p < .01, Δ = −5.1 %MVC, d = 0.95 large effect) and fatigue risk value (p < .01, Δ = −7.4 %MVC, d = 0.96 large effect). Team members responded positively about comfort and fatigue benefits, although there were concerns about the exoskeleton hindering certain job duties. The results support quantitative testing to match exoskeleton usage with specific job tasks and surveying team members for perceived benefits/drawbacks.

Wearable robotic devices (WRD) are still struggling to fulfill their vast potential. Inadequate daily life usability is one of the main hindrances to increased technology acceptance. Improving usability evaluation practices during the development of WRD could help address these limitations. In this work, we present the design and validation of a novel online platform aiming to fill this gap, the Interactive Usability Toolbox (IUT). This platform consists of a public website that offers an interactive, context-specific search within a database of 154 user research methods and educational information about usability. In a dedicated study, the effect of this platform to support usability evaluation was investigated. Twelve WRD experts were asked to complete the task of defining usability evaluation protocols for two specific use cases. The platform was provided to support one of the use cases. The quality and composition of the proposed protocols were assessed by (i) two blinded reviewers, (ii) the participants themselves, and (iii) the study coordinators. We showed that using the IUT significantly affected the proposed evaluation focus, shifting protocols from mainly effectiveness-oriented to more user-focused studies. The protocol quality, as rated by the external reviewers, remained equivalent to those designed with conventional strategies. A mixed-method usability evaluation of the platform yielded an overall positive image, with detailed suggestions for further improvements. The IUT is expected to positively affect the evaluation and development of WRD through its educational value, the context-specific recommendations supporting ongoing benchmarking endeavors, and highlighting the value of qualitative user research.

This article presents a novel smart sensor garment with integrated miniaturized inertial measurements units (IMUs) that can be used to monitor lower body kinematics during daily training activities, without the need of extensive technical assistance throughout the measurements. The smart sensor tights enclose five ultra-light sensor modules that measure linear accelerations, angular velocities, and the earth magnetic field in three directions. The modules are located at the pelvis, thighs, and shanks. The garment enables continuous measurement in the field at high sample rates (250 Hz) and the sensors have a large measurement range (32 g, 4,000°/s). They are read out by a central processing unit through an SPI bus, and connected to a centralized battery in the waistband. A fully functioning prototype was built to perform validation studies in a lab setting and in a field setting. In the lab validation study, the IMU data (converted to limb orientation data) were compared with the kinematic data of an optoelectronic measurement system and good validity (CMCs >0.8) was shown. In the field tests, participants experienced the tights as comfortable to wear and they did not feel restricted in their movements. These results show the potential of using the smart sensor tights on a regular base to derive lower limb kinematics in the field.

Industrial exoskeletons have recently gained importance as ergonomic interventions for physically demanding work activities. The growing demand for exoskeletons is leading to a need for new knowledge on the effectiveness of these systems. The Exoworkathlon, as a prospective study approach, aims to assess exoskeletons in realistic use cases and to evaluate them neutrally in their entirety. For this purpose, a first set of four realistic Parcours was developed with experts from relevant industries, the German Social Accident Insurance, and the Federal Institute for Occupational Safety and Health. In addition, a set of ratings was defined to assess subjective user feedback, work quality, and objective physiological parameters. Exoworkathlon aims to bring together developers, researchers, and end-users, strengthen collaborative exchanges, and promote a platform for the prospective holistic data collection for exoskeleton evaluation. In this article, the focus is on the background and methodology of Exoworkathlon.

Passive wearable exoskeletons are desirable as they can provide assistance during user movements while still maintaining a simple and low-profile design. These can be useful in industrial tasks where an ergonomic device could aid in load lifting without inconveniencing them and reducing fatigue and stress in the lower limbs. The SpringExo is a coil-spring design that aids in knee extension. In this paper, we describe the muscle activation of the knee flexors and extensors from seven healthy participants during repeated squats. The outcome measures are the timings of the key events during squat, flexion angle, muscle activation of rectus femoris and bicep femoris, and foot pressure characteristics of the participants. These outcome measures assess the possible effects of the device during lifting operations where reduced effort in the muscles is desired during ascent phase of the squat, without changing the knee and foot kinematics. The results show that the SpringExo significantly decreased rectus femoris activation during ascent (−2%) without significantly affecting either the bicep femoris or rectus femoris muscle activations in descent. This implies that the user could perform a descent without added effort and ascent with reduced effort. The exoskeleton showed other effects on the biomechanics of the user, increasing average squat time (+0.02 s) and maximum squat time (+0.1 s), and decreasing average knee flexion angle (−4°). The exoskeleton has no effect on foot loading or placement, that is, the user did not have to revise their stance while using the device.

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects.

The development of exoskeletons is currently a lengthy process full of challenges. We are proposing a framework to accelerate the process and make the resulting exoskeletons more user-centered. The needed accomplishments in science are described in an effort to lay the foundation for future research projects. Since the early 2000s, exoskeletons have been discussed as an emerging technology in industrial, medical, or military applications. Those systems are designed to support people during manual tasks. At first, those systems lacked broad acceptance. Many models found their niches in ongoing developments and more diverse systems entering the market. There are still applications that are in dire need of such assistance. Due to the lack of experience with body-worn robotics, the development of such systems has been shaped by trial and error. The lack of legacy products results in longer development times. In this paper, a process to generate a framework is presented to display the required research to enable future exoskeleton designers. Owing to their proximity to the user’s body, exoskeletons are highly complex systems that need sophisticated subsystems, such as kinematic, control, interaction design, or actuators, to be accepted by users. Due to the wide variety of fields and high user demands, a synchronized multidisciplinary effort is necessary. To achieve this, a process to develop a modular framework for exoskeleton design is proposed. It focuses on user- and use-case-centered solutions for matching kinematics, actuation, and control. To ensure the usefulness of the framework, an evaluation of the incorporated solutions is required.

In electronic musical instruments (EMIs), the concept of “sound sculpting” was proposed by Mulder, in which imaginary objects are manually sculpted to produce sounds, although promising has had some limitations: driven by pure intuition, only the objects’ geometrical properties were mapped to sound, while effort—which is often regarded as a key factor of expressivity in music performance—was neglected. The aim of this paper is to enhance such digital interactions by accounting for the perceptual measure of effort that is conveyed through well-established gesture-sound links in the ecologically valid conditions of non-digital music performances. Thus, it reports on the systematic exploration of effort in Dhrupad vocal improvisation, in which singers are often observed to engage with melodic ideas by manipulating intangible, imaginary objects with their hands. The focus is devising formalized descriptions to infer the amount of effort that such interactions are perceived to require and classify gestures as interactions with elastic versus rigid objects, based on original multimodal data collected in India for the specific study. Results suggest that a good part of variance for both effort levels and gesture classes can be explained through a small set of statistically significant acoustic and movement features extracted from the raw data and lead to rejecting the null hypothesis that effort is unrelated to the musical context. This may have implications on how EMIs could benefit from effort as an intermediate mapping layer and naturally opens discussions on whether physiological data may offer a more intuitive measure of effort in wearable technologies.

Telerehabilitation and robotics, either traditional rigid or soft, have been extensively studied and used to improve hand functionality after a stroke. However, a limited number of devices combined these two technologies to such a level of maturity that was possible to use them at the patients’ home, unsupervised. Here we present a novel investigation that demonstrates the feasibility of a system that integrates a soft inflatable robotic glove, a cloud-connected software interface, and a telerehabilitation therapy. Ten chronic moderate-to-severe stroke survivors independently used the system at their home for 4 weeks, following a software-led therapy and being in touch with occupational therapists. Data from the therapy, including automatic assessments by the robot, were available to the occupational therapists in real-time, thanks to the cloud-connected capability of the system. The participants used the system intensively (about five times more movements per session than the standard care) for a total of more than 8 hr of therapy on average. We were able to observe improvements in standard clinical metrics (FMA +3.9 ± 4.0, p < .05, COPM-P + 2.5 ± 1.3, p < .05, COPM-S + 2.6 ± 1.9, p < .05, MAL-AOU +6.6 ± 6.5, p < .05) and range of motion (+88%) at the end of the intervention. Despite being small, these improvements sustained at follow-up, 2 weeks after the end of the therapy. These promising results pave the way toward further investigation for the deployment of combined soft robotic/telerehabilitive systems at-home for autonomous usage for stroke rehabilitation.

Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.

This paper presents the design, modeling, analysis, fabrication, and experimental characterization of the soft robotic ankle-foot orthosis (SR-AFO), which is a wearable soft robot designed for ankle assistance, and a pilot human study of its use. Using two novel pneumatically powered soft actuators, the SR-AFO is designed to assist the ankle in multiple degrees-of-freedom during standing and walking tasks. The flat fabric pneumatic artificial muscle (ff-PAM) contracts upon pressurization and assists ankle plantarflexion in the sagittal plane. The multi-material actuator for variable stiffness (MAVS) aids in supporting ankle inversion/eversion in the frontal plane. Analytical models of the ff-PAM and MAVS were created to understand how the changing of the design parameters affects tensile force generation and stiffness support, respectively. The models were validated by both finite element analysis and experimental characterization using a universal testing machine. A set of human experiments was performed with able-bodied participants to evaluate: (a) lateral ankle support during quiet standing, (b) lateral ankle support during walking over compliant surfaces, and (c) plantarflexion assistance during push-off in treadmill walking. Group results revealed increased lateral ankle stiffness during quiet standing with the MAVS active, reduced lateral ankle deflection while walking over compliant surfaces with the MAVS active, and reduced muscle effort in ankle platarflexors during 40–60% of the gait cycle with the dual ff-PAM active. The SR-AFO shows promising results in providing lateral ankle support and plantarflexion assistance with able-bodied participants, which suggests a potential to help restore the gait of impaired users in future trials.

Current laboratory-based setups (optical marker cameras + force plates) for human motion measurement require participants to stay in a constrained capture region which forbids rich movement types. This study established a fully wearable system, based on commercially available sensors (inertial measurement units + pressure insoles), that can measure both kinematic and kinetic motion data simultaneously and support wireless frame-by-frame streaming. In addition, its capability and accuracy were tested against a conventional laboratory-based setup. An experiment was conducted, with 9 participants wearing the wearable measurement system and performing 13 daily motion activities, from slow walking to fast running, together with vertical jump, squat, lunge, and single-leg landing, inside the capture space of the laboratory-based motion capture system. The recorded sensor data were post-processed to obtain joint angles, ground reaction forces (GRFs), and joint torques (via multi-body inverse dynamics). Compared to the laboratory-based system, the established wearable measurement system can measure accurate information of all lower limb joint angles (Pearson’s r = 0.929), vertical GRFs (Pearson’s r = 0.954), and ankle joint torques (Pearson’s r = 0.917). Center of pressure (CoP) in the anterior–posterior direction and knee joint torques were fairly matched (Pearson’s r = 0.683 and 0.612, respectively). Calculated hip joint torques and measured medial–lateral CoP did not match with the laboratory-based system (Pearson’s r = 0.21 and 0.47, respectively). Furthermore, both raw and processed datasets are openly accessible (https://doi.org/10.5281/zenodo.6457662). Documentation, data processing codes, and guidelines to establish the real-time wearable kinetic measurement system are also shared (https://github.com/HuaweiWang/WearableMeasurementSystem).

Robotic assistive or rehabilitative devices are promising aids for people with neurological disorders as they help regain normative functions for both upper and lower limbs. However, it remains challenging to accurately estimate human intent or residual efforts non-invasively when using these robotic devices. In this article, we propose a deep learning approach that uses a brightness mode, that is, B-mode, of ultrasound (US) imaging from skeletal muscles to predict the ankle joint net plantarflexion moment while walking. The designed structure of customized deep convolutional neural networks (CNNs) guarantees the convergence and robustness of the deep learning approach. We investigated the influence of the US imaging’s region of interest (ROI) on the net plantarflexion moment prediction performance. We also compared the CNN-based moment prediction performance utilizing B-mode US and sEMG spectrum imaging with the same ROI size. Experimental results from eight young participants walking on a treadmill at multiple speeds verified an improved accuracy by using the proposed US imaging + deep learning approach for net joint moment prediction. With the same CNN structure, compared to the prediction performance by using sEMG spectrum imaging, US imaging significantly reduced the normalized prediction root mean square error by 37.55% ($ p $ < .001) and increased the prediction coefficient of determination by 20.13% ($ p $ < .001). The findings show that the US imaging + deep learning approach personalizes the assessment of human joint voluntary effort, which can be incorporated with assistive or rehabilitative devices to improve clinical performance based on the assist-as-needed control strategy.

Cumulative back muscle fatigue plays a role in the occurrence of low-back injuries in occupations that require repetitive lifting of heavy loads and working in forward leaning postures. Lift-support exoskeletons have the potential to reduce back and hip muscle activity, thereby delaying the onset of fatigue in these muscles. Therefore, exoskeletons are being considered a potentially important tool to further reduce workload-related injuries. However, today no standards have been established on how to benchmark the support level of lift-support exoskeletons. This work proposes an experimental protocol to quantify the support level of a lift-support exoskeletons on instant changes in muscle activity and fatigue development while maintaining a static forward leaning posture. It then applies the protocol to experimentally assess the effect of the support provided by a commercially available lift-support exoskeleton, the LiftSuit 2.0 (Auxivo AG, Schwerzenbach, Switzerland), on the user. In a sample of 14 participants, the amplitude of the muscle activity of the back muscles $ \left({\Delta}_{\mathrm{erectorspinae},\mathrm{thoracic}}\hskip0.35em =\hskip0.35em 33.0\%,{\Delta}_{\mathrm{erectorspinae},\mathrm{lumbar}}\hskip0.35em =\hskip0.35em 13.2\%\right) $ and hip muscles ( $ {\Delta}_{\mathrm{gluteusmaximus}}\hskip0.35em =\hskip0.35em 16.3\% $ ) was significantly reduced. Wearing the exoskeleton significantly reduced the amount of fatigue developed during the task ( $ {\Delta}_{\mathrm{quadratuslumborum}}\hskip0.35em =\hskip0.35em 10.1\%,{\Delta}_{\mathrm{gluteusmaximus}}\hskip0.35em =\hskip0.35em 44.0\% $ ). Changes in muscle fatigue can be objectively recorded and correlated with relevant changes for exoskeleton users: the time a task can be performed and perceived low-back fatigue. Thus, including such measures of fatigue in standardized benchmarking procedures will help quantify the benefits of exoskeletons for occupational use.

Previously reported wearable systems for people with Parkinson’s disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37 s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.

Shoulder exoskeletons (SEs) can assist the shoulder joint of workers during overhead work and are usually passive for good portability. However, current passive SEs face the challenge that their torque generators are often attached to the human arm, which adds a significant amount of weight to the user’s arms, resulting in additional energy consumption of the user. In this paper, we present a novel passive SE whose torque generator is attached to the user’s back and assists the shoulder joint through Bowden cables. Our approach greatly reduces the weight on the user’s arms and can accommodate complex shoulder joint movements with simple and lightweight mechanical structure based on Bowden cables. In addition, to match the nonlinear torque requirements of the shoulder joint, a unique spring-cam mechanism is proposed as the torque generator. To verify the effectiveness of the device, we conducted a usability test based on muscle activations of 10 healthy subjects. When assisting overhead work, the SE significantly reduced the mean and maximum electromyography signals of the shoulder-related muscles by up to 25%. The proposed SE contributes to further research on passive SE design to improve usability, especially in terms of reducing weight on human arms.

Inclusive musical instruments benefit from incorporating wearable interfaces into digital musical instrument design, creating opportunities for bodily felt experiences and movement-based interactions. In this article, we discuss the evolution of our inclusive design approach behind the design and performance practices of three wearable musical instruments. By focusing on the embodied, somatic, and tacit dimensions of movement-based musical interaction, we evaluate these case studies, combining the third and first-person perspectives. The design and implementation of the wearable sensing, utilizing the additive manufacturing techniques, are discussed for each instrument and its performer in specific cases of musical expression. This article further discusses how our approach integrates music performance as a crucial step into design and evaluation, utilizing these performance practices and such collaborative settings for improved diversity and inclusion. Finally, we examine how our design approach evolves from user-centered design to more participatory practices, offering people with diverse abilities a shared music performance space.

In diabetic peripheral neuropathy, offloading high-plantar-pressure areas using statically offloaded customized insoles or expensive sensors and actuators are commonly-followed treatment procedures. In this article, we propose the concept of dynamically self-offloading therapeutic footwear that operates mechanically without using sensors and actuators. We achieve this by using an array of snapping arches. When a load higher than a bespoke value is applied, these arches enter negative-stiffness regime and offload the high-pressure region by snapping to a different shape. They again return to their initial shape when the load disappears. Thus, they serve as both sensors and actuators that get actuated by person’s body weight. We present an analytical method to compute the switching load and the switchback time of such arches and use them to customize the footwear according to the person’s body weight, gait speed, and foot size. We identify the high-pressure regions from the clinical data and place the arches such that these high-pressure regions get dynamically offloaded, and the pressure gets redistributed to other regions. We considered 200 kPa as a limiting pressure to prevent the prolonged effects of high plantar pressure. To check the efficacy of the concept, a complete 3D-printed prototype made of thermoplastic polyurethane was tested and compared with barefoot and in-shoe plantar pressure for subjects recruited at a clinical facility. We notice that the self-offloading insole shows the plantar pressure reduction at all the foot regions, and significant offloading of 57% is observed at the forefoot region.

Force feedback is often beneficial for robotic teleoperation, as it enhances the user’s remote perception. Over the years, many kinesthetic haptic displays (KHDs) have been proposed for this purpose, which have different types of interaction and feedback, depending on their kinematics and their interface with the operator, including, for example, grounded and wearable devices acting either at the joint or operational space (OS) level. Most KHDs in the literature are for the upper limb, with a majority acting at the shoulder/elbow level, and others focusing on hand movements. A minority exists which addresses wrist motions. In this paper, we present the Wearable Delta (W$ \Delta $), a proof-of-concept wearable wrist interface with hybrid parallel–serial kinematics acting in the OS, able to render a desired force directly to the hand involving just the forearm–hand subsystem. It has six degrees of freedom (DoFs), three of which are actuated, and is designed to reduce the obstruction of the range of the user’s wrist. Integrated with positions/inertial sensors at the elbow and upper arm, the W$ \Delta $ allows the remote control of a full articulated robotic arm. The paper covers the whole designing process, from the concept to the validation, as well as a multisubject experimental campaign that investigates its usability. Finally, it presents a section that, starting from the experimental results, aims to discuss and summarize the W$ \Delta $ advantages and limitations and look for possible future improvements and research directions.