Individualization of exosuit assistance based on measured muscle dynamics during versatile walking

A Reconfigurable Soft Linkage Robot via Internal "Virtual" Joints

Traditional robots derive their capabilities of movement through rigid structural "links" and discrete actuated "joints." Alternatively, soft robots are composed of flexible materials that permit movement across a continuous range of their body and appendages and thus are not restricted in where they can bend. While trade-offs between material choices may restrain robot functionalities within a narrow spectrum, we argue that bridging the functional gaps between soft and hard robots can be achieved from a hybrid design approach that utilizes both the reconfigurability and the controllability of traditional soft and hard robot paradigms. In this study, we present a hybrid robot with soft inflated "linkages," and rigid internal joints that can be spatially reconfigured. Our method is based on the geometric pinching of an inflatable beam to form mechanical pinch-joints connecting the inflated robot linkages. Such joints are activated and controlled via internal motorized modules that can be relocated for on-demand joint-linkage configurations. We demonstrate two applications that utilize joint reconfigurations: a deployable robot manipulator and a terrestrial crawling robot with tunable gaits.

Untethered soft actuators for soft standalone robotics

Soft actuators produce the mechanical force needed for the functional movements of soft robots, but they suffer from critical drawbacks since previously reported soft actuators often rely on electrical wires or pneumatic tubes for the power supply, which would limit the potential usage of soft robots in various practical applications. In this article, we review the new types of untethered soft actuators that represent breakthroughs and discuss the future perspective of soft actuators. We discuss the functional materials and innovative strategies that gave rise to untethered soft actuators and deliver our perspective on challenges and opportunities for future-generation soft actuators.

Wearable bioadhesive ultrasound shear wave elastography

Acute liver failure (ALF) is a critical medical condition defined as the rapid development of hepatic dysfunction. Conventional ultrasound elastography cannot continuously monitor liver stiffness over the course of rapidly changing diseases for early detection due to the requirement of a handheld probe. In this study, we introduce wearable bioadhesive ultrasound elastography (BAUS-E), which can generate acoustic radiation force impulse (ARFI) to induce shear waves for the continuous monitoring of modulus changes. BAUS-E contains 128 channels with a compact design with only 24 mm in the azimuth direction for comfortable wearability. We further used BAUS-E to continuously monitor the stiffness of in vivo rat livers with ALF induced by d-galactosamine over 48 hours, and the stiffness change was observed within the first 6 hours. BAUS-E holds promise for clinical applications, particularly in patients after organ transplantation or postoperative care in the intensive care unit (ICU).

Continuous Locomotion Mode and Task Identification for an Assistive Exoskeleton Based on Neuromuscular-Mechanical Fusion

Human walking parameters exhibit significant variability depending on the terrain, speed, and load. Assistive exoskeletons currently focus on the recognition of locomotion terrain, ignoring the identification of locomotion tasks, which are also essential for control strategies. The aim of this study was to develop an interface for locomotion mode and task identification based on a neuromuscular-mechanical fusion algorithm. The modes of level and incline and tasks of speed and load were explored, and seven able-bodied participants were recruited. A continuous stream of assistive decisions supporting timely exoskeleton control was achieved according to the classification of locomotion. We investigated the optimal algorithm, feature set, window increment, window length, and robustness for precise identification and synchronization between exoskeleton assistive force and human limb movements (human-machine collaboration). The best recognition results were obtained when using a support vector machine, a root mean square/waveform length/acceleration feature set, a window length of 170, and a window increment of 20. The average identification accuracy reached 98.7% ± 1.3%. These results suggest that the surface electromyography-acceleration can be effectively used for locomotion mode and task identification. This study contributes to the development of locomotion mode and task recognition as well as exoskeleton control for seamless transitions.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks

OBJECTIVE

Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation.

METHODS

In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network.

RESULTS

Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively.

CONCLUSION AND SIGNIFICANCE

This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue

OBJECTIVE

Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability.

METHODS

We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions.

RESULTS

We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001).

CONCLUSION

These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics.

SIGNIFICANCE

The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Effects of high-intensity gait training with and without soft robotic exosuits in people post-stroke: a development-of-concept pilot crossover trial

INTRODUCTION

High-intensity gait training is widely recognized as an effective rehabilitation approach after stroke. Soft robotic exosuits that enhance post-stroke gait mechanics have the potential to improve the rehabilitative outcomes achieved by high-intensity gait training. The objective of this development-of-concept pilot crossover study was to evaluate the outcomes achieved by high-intensity gait training with versus without soft robotic exosuits.

METHODS

In this 2-arm pilot crossover study, four individuals post-stroke completed twelve visits of speed-based, high-intensity gait training: six consecutive visits of Robotic Exosuit Augmented Locomotion (REAL) gait training and six consecutive visits without the exosuit (CONTROL). The intervention arms were counterbalanced across study participants and separated by 6 + weeks of washout. Walking function was evaluated before and after each intervention using 6-minute walk test (6MWT) distance and 10-m walk test (10mWT) speed. Moreover, 10mWT speeds were evaluated before each training visit, with the time-course of change in walking speed computed for each intervention arm. For each participant, changes in each outcome were compared to minimal clinically-important difference (MCID) thresholds. Secondary analyses focused on changes in propulsion mechanics and associated biomechanical metrics.

RESULTS

Large between-group effects were observed for 6MWT distance (d = 1.41) and 10mWT speed (d = 1.14). REAL gait training resulted in an average pre-post change of 68 ± 27 m (p = 0.015) in 6MWT distance, compared to a pre-post change of 30 ± 16 m (p = 0.035) after CONTROL gait training. Similarly, REAL training resulted in a pre-post change of 0.08 ± 0.03 m/s (p = 0.012) in 10mWT speed, compared to a pre-post change of 0.01 ± 06 m/s (p = 0.76) after CONTROL. For both outcomes, 3 of 4 (75%) study participants surpassed MCIDs after REAL training, whereas 1 of 4 (25%) surpassed MCIDs after CONTROL training. Across the training visits, REAL training resulted in a 1.67 faster rate of improvement in walking speed. Similar patterns of improvement were observed for the secondary gait biomechanical outcomes, with REAL training resulting in significantly improved paretic propulsion for 3 of 4 study participants (p < 0.05) compared to 1 of 4 after CONTROL.

CONCLUSION

Soft robotic exosuits have the potential to enhance the rehabilitative outcomes produced by high-intensity gait training after stroke. Findings of this development-of-concept pilot crossover trial motivate continued development and study of the REAL gait training program.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Improved Fascicle Length Estimates From Ultrasound Using a U-net-LSTM Framework

Brightness-mode (B-mode) ultrasound has been used to measure in vivo muscle dynamics for assistive devices. Estimation of fascicle length from B-mode images has now transitioned from time-consuming manual processes to automatic methods, but these methods fail to reach pixel-wise accuracy across extended locomotion. In this work, we aim to address this challenge by combining a U-net architecture with proven segmentation abilities with an LSTM component that takes advantage of temporal information to improve validation accuracy in the prediction of fascicle lengths. Using 64,849 ultrasound frames of the medial gastrocnemius, we semi-manually generated ground-truth for training the proposed U-net-LSTM. Compared with a traditional U-net and a CNNLSTM configuration, the validation accuracy, mean square error (MSE), and mean absolute error (MAE) of the proposed U-net-LSTM show better performance (91.4%, MSE =0.1± 0.03 mm, MAE =0.2± 0.05 mm). The proposed framework could be used for real-time, closed-loop wearable control during real-world locomotion.

Effects of a soft robotic exosuit on the quality and speed of overground walking depends on walking ability after stroke

BACKGROUND

Soft robotic exosuits can provide partial dorsiflexor and plantarflexor support in parallel with paretic muscles to improve poststroke walking capacity. Previous results indicate that baseline walking ability may impact a user's ability to leverage the exosuit assistance, while the effects on continuous walking, walking stability, and muscle slacking have not been evaluated. Here we evaluated the effects of a portable ankle exosuit during continuous comfortable overground walking in 19 individuals with chronic hemiparesis. We also compared two speed-based subgroups (threshold: 0.93 m/s) to address poststroke heterogeneity.

METHODS

We refined a previously developed portable lightweight soft exosuit to support continuous overground walking. We compared five minutes of continuous walking in a laboratory with the exosuit to walking without the exosuit in terms of ground clearance, foot landing and propulsion, as well as the energy cost of transport, walking stability and plantarflexor muscle slacking.

RESULTS

Exosuit assistance was associated with improvements in the targeted gait impairments: 22% increase in ground clearance during swing, 5° increase in foot-to-floor angle at initial contact, and 22% increase in the center-of-mass propulsion during push-off. The improvements in propulsion and foot landing contributed to a 6.7% (0.04 m/s) increase in walking speed (R2 = 0.82). This enhancement in gait function was achieved without deterioration in muscle effort, stability or cost of transport. Subgroup analyses revealed that all individuals profited from ground clearance support, but slower individuals leveraged plantarflexor assistance to improve propulsion by 35% to walk 13% faster, while faster individuals did not change either.

CONCLUSIONS

The immediate restorative benefits of the exosuit presented here underline its promise for rehabilitative gait training in poststroke individuals.

Hip and Knee Joint Kinematics Predict Quadriceps Hyperreflexia in People with Post-stroke Stiff-Knee Gait

Wearable assistive technology for the lower extremities has shown great promise towards improving gait function in people with neuromuscular injuries. But common secondary impairments, such as hypersensitive stretch reflexes or hyperreflexia, have been often neglected. Incorporation of biomechanics into the control loop could improve individualization and avoid hyperreflexia. However, adding hyperreflexia prediction to the control loop would require expensive or complex measurement of muscle fiber characteristics. In this study, we explore a clinically accessible biomechanical predictor set that can accurately predict rectus femoris (RF) reaction after knee flexion assistance in pre-swing by a powered orthosis. We examined a total of 14 gait parameters based on gait kinematic, kinetic, and simulated muscle-tendon states from 8 post-stroke individuals with Stiff-Knee gait (SKG) wearing a knee exoskeleton robot. We independently performed both parametric and non-parametric variable selection approaches using machine learning regression techniques. Both models revealed the same four kinematic variables relevant to knee and hip joint motions were sufficient to effectively predict RF hyperreflexia. These results suggest that control of knee and hip kinematics may be a more practical method of incorporating quadriceps hyperreflexia into the exoskeleton control loop than the more complex acquisition of muscle fiber properties.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Introduction

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface.

Methods

To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model.

Results

Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed.

Discussion

The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

What good is a measure of muscle length? The how and why of direct measurements of skeletal muscle motion

Over the past 50 years our understanding of the central role that muscle motion has in powering movement has accelerated significantly. Fundamental to this progress has been the development of methods for measuring the length of muscles and muscle fibers in vivo. A measurement of muscle fiber length might seem a trivial piece of information on its own. Yet when combined with knowledge of the properties of skeletal muscle it has proven a powerful tool for understanding the mechanics and energetics of locomotion and informing models of motor control. In this perspective we showcase the value of direct measurements of muscle fiber length from four different techniques: sonomicrometry, fluoromicrometry, magnetomicrometry, and ultrasound. For each method, we review its history and provide a high-level user's guide for researchers choosing tools for measuring muscle length in vivo. We highlight key insights that these measurements have provided, including the importance of passive elastic mechanisms and how skeletal muscle properties govern locomotor performance. The diversity of locomotor behaviors revealed across comparative studies has provided an important tool for discovering the rules for muscle function that span vertebrate locomotion more broadly, including in humans.

Influences of dynamic load phase shifts on the energetics and biomechanics of humans

Using load-suspended backpacks to reduce vertical peak dynamic load exerted on humans can reduce metabolic costs. However, is it possible to further reduce metabolic cost by modulating dynamic load phase shift? If so, is anti-phase better than the others? In this study, we investigated the biomechanics, energetics and trunk response under phase shifts. Nine subjects wearing an active backpack with 19.4 kg loads walked on a treadmill at 5 km h-1 with four phase shift trials (T1-T4) and a load-locked trial (LK). Our results show that anti-phase trial (T3) assists ankle more and reduces the moment and gastrocnemius medialis activity, while T4 assists knee more and reduces the moment and rectus femoris activity. Due to the load injecting more mechanical energy into human in T3 and T4, the positive centre-of-mass work is significantly reduced. However, the gross metabolic rate is lowest in T4 and 4.43% lower than in T2, which may be because the activations of erector spinae and gluteus maximus are reduced in T4. In addition, T3 increases trunk extensor effort, which may weaken the metabolic advantage. This study provides guidance for improving assistance strategies and human-load interfaces and deepens the understanding of the energetics and biomechanics of human loaded walking.

Can a passive unilateral hip exosuit diminish walking asymmetry? A randomized trial

BACKGROUND

Asymmetric walking gait impairs activities of daily living in neurological patient populations, increases their fall risk, and leads to comorbidities. Accessible, long-term rehabilitation methods are needed to help neurological patients restore symmetrical walking patterns. This study aimed to determine if a passive unilateral hip exosuit can modify an induced asymmetric walking gait pattern. We hypothesized that a passive hip exosuit would diminish initial- and post-split-belt treadmill walking after-effects in healthy young adults.

METHODS

We divided 15 healthy young adults evenly between three experimental groups that each completed a baseline trial, an adaptation period with different interventions for each group, and a post-adaptation trial. To isolate the contribution of the exosuit we compared a group adapting to the exosuit and split-belt treadmill (Exo-Sb) to groups adapting to exosuit-only (Exo-only) and split-belt only (Sb-only) conditions. The independent variables step length, stance time, and swing time symmetry were analyzed across five timepoints (baseline, early- and late adaptation, and early- and late post-adaptation) using a 3 × 5 mixed ANOVA.

RESULTS

We found significant interaction and time effects on step length, stance time and swing time symmetry. Sb-only produced increased step length asymmetry at early adaptation compared to baseline (p < 0.0001) and an after-effect with increased asymmetry at early post-adaptation compared to baseline (p < 0.0001). Exo-only increased step length asymmetry (in the opposite direction as Sb-only) at early adaptation compared to baseline (p = 0.0392) but did not influence the participants sufficiently to result in a post-effect. Exo-Sb produced similar changes in step length asymmetry in the same direction as Sb-only (p = 0.0014). However, in contrast to Sb-only there was no significant after-effect between early post-adaptation and baseline (p = 0.0885).

CONCLUSION

The passive exosuit successfully diminished asymmetrical step length after-effects induced by the split-belt treadmill in Exo-Sb. These results support the passive exosuit's ability to alter walking gait patterns.

Spatial hybrid adaptive impedance learning control for robots in repetitive interactive tasks

The existing model-based impedance learning control methods can provide variable impedance regulation for physical human-robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters. The use of deadzone modifications in hybrid adaptation maintains the accuracy of the parameter estimation when the tracking error is small relative to the modeling error. The control stability is analyzed by a Lyapunov-based analysis in the spatial domain, and the control effectiveness and superiority is illustrated on a parallel robot in repetitive tasks with different task completion time.

Development of a Bioimpedance and sEMG Fusion Sensor for Gait Phase Detection: Validation with a Transtibial Amputee

Accurate gait phase detection is crucial for safe and efficient robotic prosthesis control in lower limb amputees. Several sensing modalities, including mechanical and biological signals, have been proposed to improve the accuracy of gait phase detection. In this paper, we propose a bioimpedance and sEMG fusion sensor for high-accuracy gait phase detection. We fabricated a wearable band-type sensor for multichannel bioimpedance and sEMG measurement, and we conducted gait experiments with a transtibial amputee to obtain biosignal data. Finally, we trained a deep-learning-based gait phase detection algorithm and evaluated its detection performance. Our results showed that using both bioimpedance and sEMG yielded the highest accuracy of 95.1%. Using only sEMG yielded a higher accuracy (90.9%) than that using only bioimpedance (85.1%). Therefore, we conclude that using both signals simultaneously is beneficial for improving the accuracy of gait phase detection. In addition, the proposed sensor can be applied to several applications by improving the accuracy of motion intention detection.

Exercise with a wearable hip-assist robot improved physical function and walking efficiency in older adults

Wearable assistive robotics has emerged as a promising technology to supplement or replace motor functions and to retrain people recovering from an injury or living with reduced mobility. We developed delayed output feedback control for a wearable hip-assistive robot, the EX1, to provide gait assistance. Our purpose in this study was to investigate the effects of long-term exercise with EX1 on gait, physical function, and cardiopulmonary metabolic energy efficiency in elderly people. This study used parallel experimental (exercise with EX1) and control groups (exercise without EX1). A total of 60 community-dwelling elderly persons participated in 18 exercise intervention sessions during 6 weeks, and all participants were assessed at 5 time points: before exercise, after 9 exercise sessions, after 18 sessions, and 1 month and 3 months after the last session. The spatiotemporal gait parameters, kinematics, kinetics, and muscle strength of the trunk and lower extremities improved more after exercise with EX1 than in that without EX1. Furthermore, the effort of muscles over the trunk and lower extremities throughout the total gait cycle (100%) significantly decreased after exercise with EX1. The net metabolic energy costs during walking significantly improved, and functional assessment scores improved more in the experimental group than in the control group. Our findings provide evidence supporting the application of EX1 in physical activity and gait exercise is effective to improve age-related declines in gait, physical function, and cardiopulmonary metabolic efficiency among older adults.

Ankle exoskeleton torque controllers based on soleus muscle models

Powered exoskeletons are typically task-specific, but to facilitate their wider adoption they should support a variety of tasks, which requires generalizeable controller designs. In this paper, we present two potential controllers for ankle exoskeletons based on soleus fascicles and Achilles tendon models. The methods use an estimate of the adenosine triphosphate hydrolysis rate of the soleus based on fascicle velocity. Models were evaluated using muscle dynamics from the literature, which were measured with ultrasound. We compare the simulated behavior of these methods against each other and to human-in-the-loop optimized torque profiles. Both methods generated distinct profiles for walking and running with speed variations. One of the approaches was more appropriate for walking, while the other approach estimated profiles similar to the literature for both walking and running. Human-in-the-loop methods require long optimizations to set parameters per individual for each specific task, the proposed methods can produce similar profiles, work across walking and running, and be implemented with body-worn sensors without requiring torque profile parameterization and optimization for every task. Future evaluations should examine how human behavior changes due to external assistance when using these control models.

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics Via an Artificial Neural Network

Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics from an altered walking speed show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 ± 3.1%, 7.3 ± 1.6%, 8.3 ± 2.3%, and 10.0 ± 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation.

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance

In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3-5%, suggesting that the error may be acceptable in the generation of assistive force profiles.

Humanlike spontaneous motion coordination of robotic fingers through spatial multi-input spike signal multiplexing

With advances in robotic technology, the complexity of control of robot has been increasing owing to fundamental signal bottlenecks and limited expressible logic state of the von Neumann architecture. Here, we demonstrate coordinated movement by a fully parallel-processable synaptic array with reduced control complexity. The synaptic array was fabricated by connecting eight ion-gel-based synaptic transistors to an ion gel dielectric. Parallel signal processing and multi-actuation control could be achieved by modulating the ionic movement. Through the integration of the synaptic array and a robotic hand, coordinated movement of the fingers was achieved with reduced control complexity by exploiting the advantages of parallel multiplexing and analog logic. The proposed synaptic control system provides considerable scope for the advancement of robotic control systems.

Deep-Learning-Assisted Underwater 3D Tactile Tensegrity

The growth of underwater robotic applications in ocean exploration and research has created an urgent need for effective tactile sensing. Here, we propose an underwater 3-dimensional tactile tensegrity (U3DTT) based on soft self-powered triboelectric nanogenerators and deep-learning-assisted data analytics. This device can measure and distinguish the magnitude, location, and orientation of perturbations in real time from both flow field and interaction with obstacles and provide collision protection for underwater vehicles operation. It is enabled by the structure that mimics terrestrial animals' musculoskeletal systems composed of both stiff bones and stretchable muscles. Moreover, when successfully integrated with underwater vehicles, the U3DTT shows advantages of multiple degrees of freedom in its shape modes, an ultrahigh sensitivity, and fast response times with a low cost and conformability. The real-time 3-dimensional pose of the U3DTT has been predicted with an average root-mean-square error of 0.76 in a water pool, indicating that this developed U3DTT is a promising technology in vehicles with tactile feedback.

Modulation of Achilles tendon force with load carriage and exosuit assistance

Exosuits have the potential to assist locomotion in both healthy and pathological populations, but the effect of exosuit assistance on the underlying muscle-tendon tissue loading is not yet understood. In this study, we used shear wave tensiometers to characterize the modulation of Achilles tendon force with load carriage and exosuit assistance at the ankle. When walking (1.25 m/s) unassisted on a treadmill with load carriage weights of 15 and 30% of body weight, peak Achilles tendon force increased by 11 and 23%, respectively. Ankle exosuit assistance significantly reduced peak Achilles tendon force relative to unassisted, although the magnitude of change was variable across participants. Peak Achilles tendon force was significantly correlated with peak ankle torque for unassisted walking across load carriage conditions. However, when ankle plantarflexor assistance was applied, the relationship between peak tendon force and peak biological ankle torque was no longer significant. An outdoor pilot study was conducted in which a wearable shear wave tensiometer was used to measure Achilles tendon wave speed and compare across an array of assistance loading profiles. Reductions in tendon loading varied depending on the profile, highlighting the importance of in vivo measurements of muscle and tendon forces when studying and optimizing exoskeletons and exosuits.

Soft Wearable Robots: Development Status and Technical Challenges

In recent years, more and more research has begun to focus on the flexible and lightweight design of wearable robots. During this process, many novel concepts and achievements have been continuously made and shown to the public, while new problems have emerged at the same time, which need to be solved. In this paper, we give an overview of the development status of soft wearable robots for human movement assistance. On the basis of a clear definition, we perform a system classification according to the target assisted joint and attempt to describe the overall prototype design level in related fields. Additionally, it is necessary to sort out the latest research progress of key technologies such as structure, actuation, control and evaluation, thereby analyzing the design ideas and basic characteristics of them. Finally, we discuss the possible application fields, and propose the main challenges of this valuable research direction.

Personalizing exoskeleton assistance while walking in the real world

Personalized exoskeleton assistance provides users with the largest improvements in walking speed1 and energy economy2-4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s-1. Human movements encode information that can be used to personalize assistive devices and enhance performance.

Real time high voltage capacitance for rapid evaluation of dielectric elastomer actuators

Dielectric elastomer actuators (DEAs) are soft electromechanical transducers that have enabled robotic, haptic, and optical applications. Despite their advantages in high specific energy, large bandwidth, and simple fabrication, their widespread adoption is limited by poor long-term performance. While the mechanical work output has been studied extensively, the electrical energy input has rarely been characterized. Here we report a method to continuously monitor high voltage capacitance during DEA actuation to directly measure the electrical energy consumption. Our approach can track energy conversion efficiency, but also show changes in the device's properties in real-time. This unprecedented insight enables a novel way to study DEAs, evaluate degradation mechanisms, and correlate material structure to device performance. Moreover, it provides a data acquisition platform for data-driven optimization and prediction of long-term actuator performance. This work is a necessary step towards developing ultra-resilient DEAs and enabling a wide range of applications, from wearable devices to soft machines across different scales.

A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control

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% ( < .001) and increased the prediction coefficient of determination by 20.13% ( < .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.

A review of the design of load-carrying exoskeletons

The increasing necessity of load-carrying activities has led to greater human musculoskeletal damage and an increased metabolic cost. With the rise of exoskeleton technology, researchers have begun exploring different approaches to developing wearable robots to augment human load-carrying ability. However, there is a lack of systematic discussion on biomechanics, mechanical designs, and augmentation performance. To achieve this, extensive studies have been reviewed and 108 references are selected mainly from 2013 to 2022 to address the most recent development. Other earlier 20 studies are selected to present the origin of different design principles. In terms of the way to achieve load-carrying augmentation, the exoskeletons reviewed in this paper are sorted by four categories based on the design principles, namely load-suspended backpacks, lower-limb exoskeletons providing joint torques, exoskeletons transferring load to the ground and exoskeletons transferring load between body segments. Specifically, the driving modes of active and passive, the structure of rigid and flexible, the conflict between assistive performance and the mass penalty of the exoskeleton, and the autonomy are discussed in detail in each section to illustrate the advances, challenges, and future trends of exoskeletons designed to carry loads.

Simultaneous Prediction of Wrist and Hand Motions via Wearable Ultrasound Sensing for Natural Control of Hand Prostheses

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneous prediction of wrist rotation (pronation/ supination)¹ and finger gestures for transradial amputees via a wearable ultrasound array. We target six finger gestures with concurrent wrist rotation in four transradial amputees. Results show that MOGP outperforms previously reported subclass discriminant analysis for both predictions of discrete finger gestures and continuous wrist rotation. Moreover, we find that MTDL has the potential to improve the accuracy of finger gesture prediction compared to MOGP and classification-specific deep learning, albeit at the expense of reducing the accuracy of wrist rotation prediction. Extended comparative analysis shows the superiority of ultrasound over surface electromyography. This paper prioritizes exploring the performance of wearable ultrasound on the simultaneous prediction of wrist and hand motions for transradial amputees, demonstrating the potential of ultrasound in future prosthetic control. Our ultrasound-based adaptive prosthetic control dataset (UltraPro) will be released to promote the development of the prosthetic community.

Bioadhesive ultrasound for long-term continuous imaging of diverse organs

Continuous imaging of internal organs over days could provide crucial information about health and diseases and enable insights into developmental biology. We report a bioadhesive ultrasound (BAUS) device that consists of a thin and rigid ultrasound probe robustly adhered to the skin via a couplant made of a soft, tough, antidehydrating, and bioadhesive hydrogel-elastomer hybrid. The BAUS device provides 48 hours of continuous imaging of diverse internal organs, including blood vessels, muscle, heart, gastrointestinal tract, diaphragm, and lung. The BAUS device could enable diagnostic and monitoring tools for various diseases.

Modulating Energy Among Foot-Ankle Complex With an Unpowered Exoskeleton Improves Human Walking Economy

Over the course of both evolution and development, the human musculoskeletal system has been well shaped for the cushion function of the foot during foot-strike and the impulsive function of the ankle joint during push-off. Nevertheless, an efficient energy interaction between foot structure and ankle joint is still lacking in the human body itself, which may limit the further potential of economical walking. Here we showed the metabolic expenditure of walking can be lessened by an unpowered exoskeleton robot that modulates energy among the foot-ankle complex towards a more effective direction. The unpowered exoskeleton recycles negative mechanical energy of the foot that is normally dissipated in heel-strike, retains the stored energy before mid-stance, and then transfers the energy to the ankle joint to assist the push-off. The modulation process of the exoskeleton consumes no input energy, yet reduces the metabolic cost of walking by 8.19 ± 0.96 % (mean ± s.e.m) for healthy subjects. The electromyography measurements demonstrate the activities of target ankle plantarflexors decreased significantly without added effort for the antagonistic muscle, suggesting the exoskeleton enhanced the subjects' energy efficiency of the foot-ankle complex in a natural manner. Furthermore, the exoskeleton also provides cushion assistance for walking, which leads to significantly decreased activity of the quadriceps muscle during heel-strike. Rather than strengthening the functions of existing biological structures, developing the complementary energy loop that does not exist in the human body itself also shows its potential for gait assistance.

Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review

The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.

Hardware Circuits Design and Performance Evaluation of a Soft Lower Limb Exoskeleton

Soft lower limb exoskeletons (LLEs) are wearable devices that have good potential in walking rehabilitation and augmentation. While a few studies focused on the structure design and assistance force optimization of the soft LLEs, rarely work has been conducted on the hardware circuits design. The main purpose of this work is to present a new soft LLE for walking efficiency improvement and introduce its hardware circuits design. A soft LLE for hip flexion assistance and a hardware circuits system with scalability were proposed. To assess the efficacy of the soft LLE, the experimental tests that evaluate the sensor data acquisition, force tracking performance, lower limb muscle activity and metabolic cost were conducted. The time error in the peak assistance force was just 1%. The reduction in the normalized root-mean-square EMG of the rectus femoris was 7.1%. The net metabolic cost in exoskeleton on condition was reduced by 7.8% relative to walking with no exoskeleton. The results show that the designed hardware circuits can be applied to the soft LLE and the soft LLE is able to improve walking efficiency of wearers.

Characterizing the relationship between peak assistance torque and metabolic cost reduction during running with ankle exoskeletons

BACKGROUND

Reducing the energy cost of running with exoskeletons could improve enjoyment, reduce fatigue, and encourage participation among novice and ageing runners. Previously, tethered ankle exoskeleton emulators with offboard motors were used to greatly reduce the energy cost of running with powered ankle plantarflexion assistance. Through a process known as "human-in-the-loop optimization", the timing and magnitude of assistance torque was optimized to maximally reduce metabolic cost. However, to achieve the maximum net benefit in energy cost outside of the laboratory environment, it is also necessary to consider the tradeoff between the magnitude of device assistance and the metabolic penalty of carrying a heavier, more powerful exoskeleton.

METHODS

In this study, tethered ankle exoskeleton emulators were used to characterize the effect of peak assistance torque on metabolic cost during running. Three recreational runners participated in human-in-the-loop optimization at four fixed peak assistance torque levels to obtain their energetically optimal assistance timing parameters at each level.

RESULTS

We found that the relationship between metabolic rate and peak assistance torque was nearly linear but with diminishing returns at higher torque magnitudes, which is well-approximated by an asymptotic exponential function. At the highest assistance torque magnitude of 0.8 Nm/kg, participants' net metabolic rate was 24.8 ± 2.3% (p = 4e-6) lower than running in the unpowered devices. Optimized timing of peak assistance torque was as late as allowed during stance (80% of stance) and optimized timing of torque removal was at toe-off (100% of stance); similar assistance timing was preferred across participants and torque magnitudes.

CONCLUSIONS

These results allow exoskeleton designers to predict the energy cost savings for candidate devices with different assistance torque capabilities, thus informing the design of portable ankle exoskeletons that maximize net metabolic benefit.

Robotic assistive technologies get more personal

Personalized and adaptive control systems can improve the efficacy of assistive technologies in rehabilitation.

Can humans perceive the metabolic benefit provided by augmentative exoskeletons?

BACKGROUND

The purpose of augmentative exoskeletons is to help people exceed the limitations of their human bodies, but this cannot be realized unless people choose to use these exciting technologies. Although human walking efficiency has been highly optimized over generations, exoskeletons have been able to consistently improve this efficiency by 10-15%. However, despite these measurable improvements, exoskeletons today remain confined to the laboratory. To achieve widespread adoption, exoskeletons must not only exceed the efficiency of human walking, but also provide a perceivable benefit to their wearers.

METHODS

In this study, we quantify the perceptual threshold of the metabolic efficiency benefit provided during exoskeleton-assisted locomotion. Ten participants wore bilateral ankle exoskeletons during continuous walking. The assistance provided by the exoskeletons was varied in 2 min intervals while participants provided feedback on their metabolic rate. These data were aggregated and used to estimate the perceptual threshold.

RESULTS

Participants were able to detect a change in their metabolic rate of 22.7% (SD: 17.0%) with 75% accuracy. This indicates that in the short term and on average, wearers cannot yet reliably perceive the metabolic benefits of today's augmentative exoskeletons.

CONCLUSIONS

If wearers cannot perceive the benefits provided by these technologies, it will negatively affect their impact, including long-term adoption and product viability. Future exoskeleton researchers and designers can use these methods and results to inform the development of exoskeletons that reach their potential.

Improving Walking Economy With an Ankle Exoskeleton Prior to Human-in-the-Loop Optimization

Lower limb robotic exoskeletons have shown the capability to enhance human locomotion for healthy individuals or to assist motion rehabilitation and daily activities for patients. Recent advances in human-in-the-loop optimization that allowed for assistance customization have demonstrated great potential for performance improvement of exoskeletons. In the optimization process, subjects need to experience multiple types of assistance patterns, thus, leading to a long evaluation time. Besides, some patterns may be uncomfortable for the wearers, thereby resulting in unpleasant optimization experiences and inaccurate outcomes. In this study, we investigated the effectiveness of a series of ankle exoskeleton assistance patterns on improving walking economy prior to optimization. We conducted experiments to systematically evaluate the wearers' biomechanical and physiological responses to different assistance patterns on a lightweight cable-driven ankle exoskeleton during walking. We designed nine patterns in the optimization parameters range which varied peak torque magnitude and peak torque timing independently. Results showed that metabolic cost of walking was reduced by 17.1 ± 7.6% under one assistance pattern. Meanwhile, soleus (SOL) muscle activity was reduced by 40.9 ± 19.8% with that pattern. Exoskeleton assistance changed maximum ankle dorsiflexion and plantarflexion angle and reduced biological ankle moment. Assistance pattern with 48% peak torque timing and 0.75 N·m·kg⁻¹ peak torque magnitude was effective in improving walking economy and can be selected as an initial pattern in the optimization procedure. Our results provided a preliminary understanding of how humans respond to different assistances and can be used to guide the initial assistance pattern selection in the optimization.

A sound approach to improving exoskeletons and exosuits

[Figure: see text].