Conor J. Walsh

Actuators for wearable robots are difficult to design due to the competing requirements of minimizing weight and form factor while achieving desired performance requirements. Current solutions often rely on a “one-size-fits-all” approach utilizing classical motors and gears. Here, we present a thin, high torque density wearable actuator consisting of a cycloid gear and PCB axial flux motor, along with a modeling and design optimization framework to minimize actuator and battery mass while maintaining target performance. The model predicts motor mass with 95.9% accuracy and battery power with 75.9-87.9% accuracy. We then apply this design and optimization framework to quantify the benefits of creating optimized sizes of wearable actuators for gait assistance for stroke survivors, which was found to reduce average actuator and battery mass by 384 g with just 5 sizes. Furthermore, sizing the actuator and battery together resulted in an average of 28.9% to 48.3% more weight savings than sizing only the motor or only the battery, respectively, for individually optimized actuators. This paradigm of sizing actuators like clothing, along with new actuator architectures, may yield weight savings that could improve adoption of daily-wear assistive devices.

Stroke-induced abnormal upper extremity (UE) joint coupling limits independent joint control and impairs functional arm use. While previous studies have predominantly evaluated UE joint coupling within the plane of motion (in-plane) and its effect on functional task performance, minimizing unnecessary joint movements outside the plane of motion (out-of-plane) across all UE degrees of freedom (DOFs) is essential for coordinated movements. To address this limitation, we developed an experimental procedure leveraging 3D motion capture to evaluate in- and out-of-plane joint coupling for seven UE DOFs during isolated shoulder, elbow, and wrist joint movements, and functional task performance during an object transfer task. We introduce a method to calculate the in- and out-of-plane joint coupling ratio (JCR) for seven UE DOFs. We investigated the contribution of stroke-induced abnormal in- and out-of-plane joint coupling to explain deficits in functional task performance using hierarchical regression analysis. In 18 individuals post-stroke, joint coupling was abnormal in- and out-of-plane for seven UE DOFs, evident by significantly higher JCR values for the paretic arm compared to the non-paretic arm. The regression model using both in- and out-of-plane joint coupling explained significantly higher variance, up to 33.8%, in stroke-induced deficits in movement duration, hand trajectory smoothness, trunk displacement, hand movement extent, and peak velocity time compared to models only using either in-plane or out-of-plane joint coupling. Our work advances post-stroke abnormal joint coupling evaluation methods across all UE DOFs, required to more comprehensively understand stroke-induced impairments in independent UE joint control and their effect on functional task performance.

Aerodynamic Surface Dimpling Static aerodynamic surfaces cannot adapt to dynamic wind profiles, limiting performance under variable operating conditions. In article number 2505817, Katia Bertoldi and co-workers introduce a stretch-induced dimpling textile metamaterial that tunes aerodynamic properties even when body-conformed. Wind-tunnel tests and simulations show drag modulation up to 20%. Active stretching enables optimal performance across variable speeds, opening transformative applications in wearables, aerospace, maritime, and civil engineering.

Accurately estimating kinetic metrics, such as braking and propulsion forces, in real-world running environments enhances our understanding of performance, fatigue, and injury. Wearable inertial measurement units (IMUs) offer a potential solution to estimate kinetic metrics outside the lab when combined with machine learning. However, current IMU-based kinetic estimation models are trained and evaluated within a single environment, often on lab treadmills. The transferability of these treadmill-trained models during overground running in and out of the lab is underexplored, and the individualization and validation of such models remain a challenge. Toward bridging this gap, we trained a generalized model on treadmill data of 15 recreational runners and evaluated braking and propulsion force estimates during overground running in and out of the lab. We explored fine-tuning with individual data from lab-based overground running to quantify model performance improvements with individualization. The generalized and fine-tuned models were extrapolated to outdoor running for a subset of five participants, and estimates were compared to lab-based overground measurements. Evaluating the generalized model with a leave-one-out cross validation yielded overground braking and propulsion force root mean squared error of 4.3 ± 1.1 % bodyweight (%BW). Fine-tuning this model with eight strides reduced error to 2.6 ± 0.5 %BW. Outdoor force predictions from the fine-tuned model better aligned with expected linear trends between braking/propulsion impulses and speed than the generalized model. These results provide insights into the accuracy and applicability of IMU data-driven models for braking and propulsion estimation during overground running, facilitating the development of practical, individualized biomechanical analysis tools for real-world use.

Introducing engineering design early in science education can be a path toward inspiring young students to develop feelings of belonging in STEM. For some educators, robotics kits are seen as a classroom-ready platform for teaching engineering design. However, once the kits are complete, how can teachers continue to leverage those materials for continued learning? In this paper, we describe a curriculum to expand past basic kits to engage students in engineering and bioinspired design. In this case study, after students built a robot gripper kit, teachers guided their classes through needs identification, bio-inspired design practices, and prototype building to address problems they encounter in their daily lives and communities. We show examples of student work and their reflections on the engineering design process. This process allows students to expand their thinking of robotics applications and build confidence in their role as innovators. This paper provides teachers with curriculum and materials needed to extend the use of a variety of robotics kits in their classrooms.

Portable wearable robots offer promise for assisting people with upper limb disabilities. However, movement variability between individuals and trade-offs between supportiveness and transparency complicate robot control during real-world tasks. We address these challenges by first developing a personalized ML intention detection model to decode user's motion intention from IMU and compression sensors. Second, we leverage a physics-based hysteresis model to enhance control transparency and adapt it for practical use in real-world tasks. Third, we combine and integrate these two models into a real-time controller to modulate the assistance level based on the user's intention and kinematic state. Fourth, we evaluate the effectiveness of our control strategy in improving arm function in a multi-day evaluation. For 5 individuals post-stroke and 4 living with ALS wearing a soft shoulder robot, we demonstrate that the controller identifies shoulder movement with 94.2% accuracy from minimal change in the shoulder angles (elevation: 3.4°, depression: 1.7°) and reduces arm-lowering force by 31.9% compared to a baseline controller. Furthermore, the robot improves movement quality by increasing their shoulder elevation/depression (17.5°), elbow (10.6°) and wrist flexion/extension (7.6°) ROMs; reducing trunk compensation (up to 25.4%); and improving hand-path efficiency (up to 53.8%).

Despite broad acceptance among economists, carbon taxes face persistent public resistance.We measure the sources and distribution of welfare losses from unexpected European carbon price changes by estimating their impact on consumer prices, labor income, financial wealth, and government transfers.A 1% carbon-policy-induced increase in energy prices leads to an average welfare loss of about 0.5%of a household's three-year consumption, primarily driven by indirect labor-income effects.Younger, poorer, and less educated households, especially in Southern and Eastern Europe, bear a disproportionate burden.These findings suggest public opposition to carbon taxes could stem from legitimate distributional concerns.

Recent work has shown that exoskeletons controlled through data-driven methods can dynamically adapt assistance to various tasks for healthy young adults. However, applying these methods to populations with neuromotor gait deficits, such as post-stroke hemiparesis, is challenging. This is due not only to high population heterogeneity and gait variability but also to a lack of post-stroke gait datasets to train accurate models. Despite these challenges, data-driven methods offer a promising avenue for control, potentially allowing exoskeletons to function safely and effectively in unstructured community settings. This work presents a first step towards enabling adaptive plantarflexion and dorsiflexion assistance from data-driven torque estimation during post-stroke walking. We trained a multi-task Temporal Convolutional Network (TCN) using collected data from four post-stroke participants walking on a treadmill ($R^2$ of $0.74 \pm 0.13$). The model uses data from three inertial measurement units (IMU) and was pretrained on healthy walking data from 6 participants. We implemented a wearable prototype for our ankle torque estimation approach for exoskeleton control and demonstrated the viability of real-time sensing, estimation, and actuation with one post-stroke participant.

Nearly all soft wearable robots rely on textiles to distribute actuation forces to the human body; however, the mechanical hysteresis of these materials significantly complicates device control. If not properly accounted for, this history-dependent behavior can result in substantial over-/under-support for which the human user must actively compensate. While a number of hysteresis modeling approaches have been proposed, these techniques are either (a) heuristic-driven and do not accurately reflect the observed physical behavior or (b) rely on complex benchtop calibration procedures that are not amenable to wearable applications where the complete human-robot system must be holistically considered. In this work, we present a new strategy to predict the complex hysteretic response of the combined human-robot system given its full state history using a mathematical technique known as a Preisach model. Our approach is directly personalized to each individual with data collected on the body in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo>∼</mml:mo> <mml:mn>90</mml:mn> </mml:math> seconds. We demonstrate the technique with a previously proposed soft wearable robot for shoulder assistance, though the concept is applicable to any joint. To benchmark the efficacy of our approach against previously proposed strategies, we performed an open-loop trajectory tracking procedure with 12 human participants and an articulated mannequin. Our strategy achieved an average shoulder elevation angle tracking accuracy of 5.3° across human participants, representing a significant improvement compared to prior techniques. We anticipate that this new approach will facilitate significantly improved soft wearable robot control by providing reliable estimates of the full hysteretic system response, enabling more robust physical human-robot interaction and coordination.

Static aerodynamic surfaces are inherently limited in their ability to adapt to dynamic velocity profiles or environmental changes, restricting their performance under variable operating conditions. This challenge is particularly pronounced in high-speed competitive sports, such as cycling and downhill skiing, where the properties of a static textile surface are mismatched with highly dynamic wind-speed profiles. Here, an textile metamaterial is introduced that is capable of variable aerodynamic profiles through a stretch-induced dimpling mechanism, even when tightly conformed to a body or object. Wind-tunnel experiments are used to characterize the variable aerodynamic performance of the dimpling mechanism, while Finite Element (FE) simulations efficiently characterize the design space to identify optimal textile metamaterial architectures. By controlling dimple size, the aerodynamic performance of the textile can be tailored for specific wind-speed ranges, resulting in an ability to modulate drag force at target wind-speeds by up to 20%. Furthermore, the potential for active control of a textiles' aerodynamic properties is demonstrated, in which controlled stretching allows the textile to sustain optimal performance across a dynamic wind-speed profile. These findings establish a new approach to aerodynamic metamaterials, with surface dimpling and thus variable fluid-dynamic properties offering transformative applications for wearables, as well as broader opportunities for aerospace, maritime, and civil engineering systems.

ABSTRACT We develop a framework to measure the welfare impact of macroeconomic shocks throughout the distribution. The first-order impact of a shock is summarized by the induced movements in agents’ feasible sets: their budget constraint and borrowing constraints. We combine estimated impulse response functions with micro-data on household consumption bundles, asset holdings, and labor income for different U.S. households. We find that inflationary oil shocks are regressive, but monetary expansions are progressive, and there is substantial heterogeneity throughout the life cycle. In all cases, the dominant channel is the effect of the shock on the cost of accumulating assets, not movements in goods prices or labor income.

Background Variability in movement is critical for performance under dynamic conditions. Stroke causes focal injury to the motor system, disrupts voluntary motor control, and leads to less smooth and more variable upper extremity movements. Few studies have characterized trial-by-trial variation in upper extremity movement smoothness and its clinical and neuroanatomic correlates in the first week post-stroke. Objective To evaluate trial-by-trial variation in upper extremity movement smoothness during planar reaching and relate it to clinical outcomes and neuroanatomical injury after acute stroke. Methods Twenty-two patients (4.4 ± 1.7 days post-stroke) and 22 able-bodied adults completed a planar center-out reaching task. Smoothness was quantified with spectral arc length (SPARC). Median and interquartile range (IQR, a quantification of trial-by-trial variation) of SPARC values were assessed. Patients completed a clinical assessment battery acutely and at 90 days post-stroke. MRI-derived stroke lesions were analyzed to estimate basal ganglia, motor cortex, and corticospinal tract injury. Intraclass correlation, Spearman’s correlation, and multivariate regression evaluated trial-by-trial variation and its relation to clinical assessments, outcomes, and neuroanatomical injury. Results Post-stroke reaching was less smooth and more variable (larger IQR) compared to able-bodied adults. Variability in post-stroke smoothness was primarily driven by within-subject, trial-by-trial variation. More variable smoothness, even after controlling for median smoothness, related to worse performance on clinical assessments and 90-day outcomes. More variable smoothness related to greater corticospinal tract injury (ρ = 0.537, P = .011), but not to basal ganglia or motor cortex injury. Conclusion Trial-by-trial variation of movement is valuable for understanding sensorimotor control post-stroke and has implications for targeted neurorehabilitation.

Inertial measurement units offer a cost-effective, portable alternative to lab-based motion capture systems. However, measuring joint angles and movement trajectories with inertial measurement units is challenging due to signal drift errors caused by biases and noise, which are amplified by numerical integration. Existing approaches use anatomical constraints to reduce drift but require body parameter measurements. Learning-based approaches show promise but often lack accuracy for broad applications (e.g., strength training). Here, we introduce the Activity-in-the-loop Kinematics Estimator, an end-to-end machine learning model incorporating human behavioral constraints for enhanced kinematics estimation using two inertial measurement units. It integrates activity classification with kinematics estimation, leveraging limited movement patterns during specific activities. In dynamic scenarios, our approach achieved trajectory and shoulder joint angle errors under 0.021 m and $$6.5^\circ$$ , respectively, 52% and 17% lower than errors without including activity classification. These results highlight accurate motion tracking with minimal inertial measurement units and domain-specific context. Inertial measurement units offer a cost-effective, portable alternative to lab-based systems for measuring human motion. Here, the authors predict human motion using inertial measurement units combined with machine learning, leveraging limited movement patterns and reduced motion variability during specific activities.

Wearable robotic devices have become increasingly prevalent in both occupational and rehabilitative settings, yet their widespread adoption remains inhibited by usability barriers related to comfort, restriction, and noticeable functional benefits. Acknowledging the importance of user perception in this context, this study explores preference-based controller optimization for a back exosuit that assists lifting. Considering the high mental and metabolic effort discrete motor tasks impose, we used a forced-choice Bayesian Optimization approach that promotes sampling efficiency by leveraging domain knowledge about just noticeable differences between assistance settings. Optimizing over two control parameters, preferred settings were consistent within and uniquely different between participants. We discovered that overall, participants preferred asymmetric parameter configurations with more lifting than lowering assistance, and that preferences were sensitive to user anthropometrics. These findings highlight the potential of perceptually guided assistance optimization for wearable robotic devices, marking a step toward more pervasive adoption of these systems in the real world.

Back exoskeletons are gaining attention for preventing occupational back injuries, but they can disrupt movement, a burden that risks abandonment. Enhanced adaptability is proposed to mitigate burdens, but perceptual benefits are less known. This study investigates the perceptual and biomechanical impacts of a SLACK suit (non-assistive) controller versus three controllers with varying adaptability: a Weight-Direction-Angle adaptive (WDA-ADPT) that scales assistance based on the weight of the boxes using a chest-mounted camera and machine learning algorithm, movement direction, and trunk flexion angle, and standard Direction-Angle adaptive (DA-ADPT) and Angle adaptive (A-ADPT) controllers. Fifteen participants performed a variable weight (2, 8, 14 kg) box-transfer task. WDA-ADPT achieved the highest perceptual score (88%) across survey categories and reduced peak back extensor (BE) muscle amplitudes by 10.1%. DA-ADPT had slightly lower perceptual (76%) and peak BE reduction (8.5%). A-ADPT induced hip restriction, which could explain the lowest perceptual score (55%) despite providing the largest reductions in peak BE muscle activity (17.3%). Reduced perceptual scores achieved by DA and A-ADPT were explained by controllers providing too much or little assistance versus actual task demands. These findings underscore that scaling assistance to task demands improves biomechanical benefits and the perception of the device's suitability.

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. <strong>-Gemma K. Alderton</strong>.

ABSTRACT: Amyotrophic lateral sclerosis is a progressive neurodegenerative disease affecting upper and lower motor neurons that control voluntary muscles. With no known cure, clinical care is focused on symptom management to maximize function and quality of life. Assistive technology plays a crucial role and enables some restoration of movement and function despite disease progression. This scoping review assesses the effectiveness of assistive technologies tested in people living with amyotrophic lateral sclerosis, specifically those designed to compensate for upper and lower extremity, trunk, and cervical muscle weakness. A comprehensive search was conducted across PubMed, CINAHL, ERIC, and Google Scholar and through citation chasing. We included 26 articles that tested an assistive device on at least one person living with amyotrophic lateral sclerosis and evaluated the device's effectiveness in restoring movement or providing stabilization to support functional mobility or activities of daily living. Most studies were pilot feasibility or usability trials, with small numbers of amyotrophic lateral sclerosis participants. The devices showed various benefits, including improved range of motion, function, and participation in daily activities. This review highlights the potential for assistive devices to enhance function in people living with amyotrophic lateral sclerosis and underscores the need for comprehensive studies involving larger cohorts of individuals at different stages of amyotrophic lateral sclerosis.

The Fugl Meyer Assessment (FMA) is a widely-used assessment for tracking motor function recovery post-stroke. Due to the limited access to rehabilitation, there exists a need for remote and automated assessment solutions. Wearable sensors and data-driven methods have shown promise for enabling automatic upper extremity FMA (FMA-UE) estimation, but minimizing user input motion and aligning with current clinical activities will aid the adoption of sensor-based assessments. In this work, we present an FMA-UE estimator which can make score predictions for a key subset of the assessment (70$\% $ of all items) using data from inertial measurement units (IMUs) placed on the arms and the trunk from three volitional reaching motions representative of functional daily activities. We collected a dataset of eleven stroke participants performing a subset of FMA-UE, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subject-out cross validation, the estimator achieved a normalized RMSE of 7$\% $, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE of post-stroke individuals. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor. The proposed estimator has the potential to broaden the possibility of automatic assessment via wearable sensors.

Spinal cord injuries (SCI) often lead to upper limb impairment, necessitating innovative solutions for daily assistance beyond traditional rigid robotics due to their impractical weight and size. Despite still preliminary, soft wearables are arising as a possible solution to fill this gap. Here, we demonstrated an enhanced version of a soft inflatable robot that assists the shoulder against gravity, previously tested with different neurological conditions. Noteworthy improvements include a single-layer actuator, simplifying manufacturing, a built-in bending angle and a nylon hammock, for better armpit conformity. We characterized the actuator (approximately 8Nm at 90∘ at 70kPa) and demonstrated its good transparency, both from a kinematic and a muscular standpoint. Then, on 11 healthy individuals, we showed reductions in shoulder muscle activity (both at the anterior and middle deltoid) while performing a lift and hold task, ranging from 16% to almost 60% of the maximum voluntary contraction. More importantly, we confirmed these effects on two SCI individuals SCI, at two different stages of recovery. While preliminary, considering the limited exploration of soft wearable robots for the shoulder in SCI cases, this is a significant advancement playing an important role in the development of future soft technology for SCI assistance.

Gait rehabilitation is critical for regaining locomotor independence after neuromotor injuries like stroke. Rehabilitation literature indicates the need for such therapy to continue beyond the clinic in order to maintain motor function and support recovery. However, implementing community-based rehabilitation requires the ability to monitor gait in the real-world with clinically relevant accuracies. Despite advances in machine learning, achieving this performance with single sensing modalities has been challenging using wearable sensors like inertial measurement units (IMUs) and pressure insoles. Here, we investigate the benefits of multi-modal sensing by integrating IMU and insole data to develop individualized machine learning models in people post-stroke that estimate propulsion, a key biomechanical variable. We show that in the lab, IMU + Insole models improve performance relative to IMU only and Insole only models, with an average root-mean-squared-error (RMSE) of 0.80 %bodyweight (%BW) across the stance phase. We obtain RMSEs of 0.71%BW for peak paretic propulsion and 0.19%BW s for paretic propulsion impulse, which are within corresponding clinical thresholds. We then explore the application of this algorithm to track propulsion changes in the real-world for two participants during variable-speed walking and two participants during active gait interventions, either functional electrical stimulation or exosuit-applied resistance. For these participants, we observe similar changes in measured propulsion in the lab and estimated propulsion out of the lab across speeds and interventions. Overall, this work aims to address the challenges in applying machine learning methods for individuals post-stroke and presents an investigation into the feasibility of developing estimation methods for real-world propulsion estimation during gait rehabilitation.

While participation in undergraduate research has many benefits for students including increased confidence and persistence in engineering, community college students do not have the same access to research as students at four-year institutions. To address this disparity, we developed a soft robotics-focused undergraduate research lab at a community college within a multi-institution collaboration. In

Muscle fatigue is a common physiological phenomenon whose onset can impair physical performance and increase the risk of injury. Traditional assessments of muscle fatigue are primarily constrained by their dependence on maximum voluntary contractions (MVCs), which not only rely heavily on participant motivation, reducing measurement accuracy, but also require large, stationary equipment such as isokinetic dynamometers, limiting their application to discrete assessments in lab-based environments. In this work, we introduce a wearable muscle fatigue tracking strategy that employs low-profile single-element ultrasound and electrical stimulation. This integrated approach demonstrates that muscle deformation from electrically-induced muscle contractions correlates with muscle fatigue, thus circumventing the need for bulky hardware and eliminating the variability associated with human volition. We define a deformation index, which fuses stimulation-induced changes in muscle thickness with baseline muscle swelling to track muscle fatigue. Our results demonstrate that the deformation index reliably tracks muscle fatigue (r = 0.85 ± 0.15), under specific conditions, namely extended joint angles and increased stimulation, as measured by changes in knee extension torque during a series of dynamic, volitional fatiguing contractions on 8 subjects on an isokinetic dynamometer. This approach has the potential to enable real-time, semi-continuous muscle fatigue monitoring in unconstrained environments.

Controlling hand exoskeletons for assisting impaired patients in grasping tasks is challenging because it is difficult to infer user intent. We hypothesize that majority of daily grasping tasks fall into a small set of categories or modes which can be inferred through real-time analysis of environmental geometry from 3D point clouds. This paper presents a low-cost, real-time system for semantic image labeling of household scenes with the objective to inform and assist activities of daily living. The system consists of a miniature depth camera, an inertial measurement unit and a microprocessor. It is able to achieve 85% or higher accuracy at classification of predefined modes while processing complex 3D scenes at over 30 frames per second. Within each mode it can detect and localize graspable objects. Grasping points can be correctly estimated on average within 1 cm for simple object geometries. The system has potential applications in robotic-assisted rehabilitation as well as manual task assistance.

Back exosuits offer the potential to reduce occupational back injuries but require in-field acceptance and use to realize this potential. For this study, 146 employees trialed an active back exosuit in the field for 4 hours, completing an acceptance usability survey. Comparing the 80% of employees willing to continue wearing this device (N=117) to those who were not (N=29) revealed that employees willing to wear this device for a longer-term study generally were more likely to perceive this back exosuit to be effective (helpful) and compatible (minimally disruptive) to their everyday work. Using an optimal tree approach, we demonstrate that intent-to-use could be predicted with 78% accuracy by interacting features of perceived exosuit effectiveness and work compatibility. This study reinforces the importance of task matching, noticeable relief, and unobtrusive design to facilitate short-term employee acceptance of industrial wearable robotic technology.

Exosuits have the potential to mitigate musculoskeletal stress and prevent back injuries during industrial tasks. This study aimed to 1) validate the implementation of a soft active exosuit into a musculoskeletal model of the spine by comparing model predicted muscle activations versus corresponding surface EMG measurements and 2) evaluate the effect of the exosuit on peak back and hip muscle forces. Fourteen healthy participants performed squat and stoop lift and lower tasks with boxes of 6 and 10 kg, with and without wearing a 2.7 kg soft active exosuit. Participant-specific musculoskeletal models, which included the exosuit, were created in OpenSim. Model validation focused on the back and hip extensors, where temporal agreement between EMG and model estimated muscle activity was generally strong to excellent (average cross-correlation coefficients ranging from .84 - .98). Root mean square errors of muscle activity (0.05 – 0.1) were similar with and without the exosuit, and compared well to prior model validation studies without the exosuit (average root mean square errors ranging from .05 - .19). In terms of performance, the exosuit reduced the estimated peak erector spinae forces during lifting and lowering phases across all lifting tasks but reduced peak hip extensor muscles forces only in a squat lift task of 10 kg. These reductions in total peak muscle forces were approximately 1.7 – 4.2 times greater than the corresponding exosuit assistance force, which were 146±19 N and 102±14 N at the times of peak erector spinae forces in lifting and lowering, respectively. Overall, the results support the hypothesis that exosuits reduce soft tissue loading, and thereby potentially reduce fatigue and injury risk during manual materials handling tasks. Incorporating exosuits into musculoskeletal models is a valid approach to understand the impact of exosuit assistance on muscle activity and forces.

Wearable robotic devices have shown promise in aiding the mitigation of lower back injuries by reducing strain on muscles within the posterior chain, predominantly the erector spinae. Being a determinant of muscle strain, lumbar moments represent valuable measurements in assessing the efficacy of such devices and could further provide a more granular control input than kinematically derived heuristics. To date, computing lumbar moments is a cumbersome process, largely due to the time-intensive setup and processing requirements associated with optical motion capture (OMC) based inverse dynamics. Despite recent advances in wearable sensor-based alternatives, these limitations complicate studies that investigate real-time assistance adaptations to variations in task or loading conditions, which could ultimately provide valuable insight into how differences in control strategies affect spine kinetics and injury risk. Here, we explore the potential of using body-worn, inertial measurement units in combination with lab-integrated force plates, instead of a fully OMC based approach to estimate lumbar moments within participants. To this end, we examine two deep learning architectures, a baseline fully connected neural network (FCNN) and a long-short-term memory (LSTM) network, particularly suited for capturing temporal dependencies within the input data. We validated our approach on experiment conditions and external loads that were not present within the training set. Both models achieved high accuracy (1.58 ± 1.02 Nm RMSE) and excellent correlation (r = 0.95 ± 0.06) with OMC-based lumbar moment estimates.

Wearable robots have shown great promise in aiding individuals with reduced mobility and enhancing human performance across various applications. To achieve optimal assistance, accurate estimation of muscle dynamics has shown promise in designing adaptive control strategies. Among different techniques, B-mode ultrasonography produced with linear array transducers have gained popularity as a gold-standard imaging tool, providing non-invasive solutions to measure in-vivo muscle dynamics. B-mode ultrasound has been employed to infer muscle thickness, fascicle lengths, pennation angles, and muscle force using neural networks, offering a valuable tool for designing individualized control strategies. The effectiveness of this measuring tool depends on integrating transducers into the wearable robot, but B-mode relies on large transducers. Studies have explored smaller single-element transducers for better wearability for muscle thickness estimation. However, their ability to infer more complex muscle architecture parameters using automated techniques is yet to be determined. In this study, we propose an approach to extract M-mode traces from B-mode images to simulate signals from single-element transducers. We then employ various machine learning architectures to infer muscle pennation angle and fascicle length. Preliminary results indicate promising performance from the CNN+Transformer (2-layer spatial) + Transformer (2-layer temporal) models, with results from the CNN+LSTM models (with a RMSE of 0.02 radian for pennation angle and 2.54mm for fascicle lengths). This study paves the way for enabling the use of smaller and more portable single-element transducers for wearable robotic applications. The link to the code is https: https://github.com/raku-slyu/AB-Mode-Utrasound.

Locomotion requires careful coordination across the various joints and muscles of the body, which can be disrupted after neuromotor injuries such as stroke. People post-stroke often have weakness in their paretic, or more impaired, ankle plantarflexors and a corresponding reliance on the hip joint to generate sufficient forward propulsion. The field of robotic rehabilitation has developed wearable systems that provide joint-and task-specific training for survivors of stroke, and in turn, increase use of the ankle muscles. However, capturing ankle use at the plantarflexor level remains a challenge with conventional tools given the unknown relative contributions of the dorsiflexor muscles. Moreover, variability across individuals complicates the interpretation of user response to these robotic interventions. In this work, we used standard biomechanical analysis as well as shear wave tensiometry in five people post-stroke to gain insight into user-specific ankle and hip adaptations in response to three levels of targeted plantarflexion exosuit resistance. We show that at a group and individual-level, evidence suggests a shift in biomechanical strategy from relying on the hip to using the ankle to modulate propulsion, with a subset of participants completely shifting to the ankle by the end of training. This work represents a step towards exploring more individualized methods for characterizing user response during adaptation to wearable robotic training interventions.

Surface-Level Muscle Deformation In article number 2400444, Jonathan T. Alvarez, Conor J. Walsh, and co-workers introduce a non-contact method using a 2D laser profilometer to measure surface-level muscle deformation, a promising signal for estimating joint torque. The findings demonstrate strong correlations between deformation metrics—peak radial displacement, surface curvature, and surface strain—and volitional elbow torque across varying measurement locations and joint angles. This methodology standardizes evaluations, informing future wearable sensor design.

BACKGROUND: During inpatient rehabilitation, physical therapists (PTs) often need to manually advance patients' limbs, adding physical burden to PTs and impacting gait retraining quality. Different electromechanical devices alleviate this burden by assisting a patient's limb advancement and supporting their body weight. However, they are less ideal for neuromuscular engagement when patients no longer need body weight support but continue to require assistance with limb advancement as they recover. The objective of this study was to determine the feasibility of using a hip flexion exosuit to aid paretic limb advancement during inpatient rehabilitation post-stroke. METHODS: Fourteen individuals post-stroke received three to seven 1-hour walking sessions with the exosuit over one to two weeks in addition to standard care of inpatient rehabilitation. The exosuit assistance was either triggered by PTs or based on gait events detected by body-worn sensors. We evaluated clinical (distance, speed) and spatiotemporal (cadence, stride length, swing time symmetry) gait measures with and without exosuit assistance during 2-minute and 10-meter walk tests. Sessions were grouped by the assistance required from the PTs (limb advancement and balance support, balance support only, or none) without exosuit assistance. RESULTS: PTs successfully operated the exosuit in 97% of sessions, of which 70% assistance timing was PT-triggered to accommodate atypical gait. Exosuit assistance eliminated the need for manual limb advancement from PTs. In sessions with participants requiring limb advancement and balance support, the average distance and cadence during 2-minute walk test increased with exosuit assistance by 2.2 ± 3.1 m and 3.4 ± 1.9 steps/min, respectively (p < 0.017). In sessions with participants requiring balance support only, the average speed during 10-meter walk test increased with exosuit by 0.07 ± 0.12 m/s (p = 0.042). Clinical and spatiotemporal measures of independent ambulators were similar with and without exosuit (p > 0.339). CONCLUSIONS: We incorporated a unilateral hip flexion exosuit into inpatient stroke rehabilitation in individuals with varying levels of impairments. The exosuit assistance removed the burden of manual limb advancement from the PTs and resulted in improved gait measures in some conditions. Future work will understand how to optimize controller and assistance profiles for this population.

Climate-smart agriculture (CSA) is a conceptual framework for responding climate-related risk in agriculture across the three pillars of Mitigation, Resilience, and Production. Existing tools have been developed which seek to operationalise the CSA concept to evaluate and benchmark progress; each of which have their own relative strengths and weaknesses. The translation of this concept into actionable projects/portfolios hence requires the careful evaluation of potential trade-offs and synergies between these three pillars. The hereby presented decision-making tool aims to offer a basis for a structured evaluation of such trade-offs and synergies. It does so by assessing five different outcome pathways on how they contribute to a project's performance across the three pillars of CSA. We aspire that the use of this tool will allow for more deliberate design and implementation of projects in agricultural development, increasing the resilience and productivity of farming systems whilst ensuring the sustainable use of the environmental resource-based agriculture depends on. This tool was applied in a workshop setting to evaluate the relative strengths and weaknesses of two distinct projects; demonstrating the utility in visualising the same performance in different ways. Of particular importance was ability to demonstrate how focusing on productivity and adaptation may trade-off mitigation activities. The results of the case study application demonstrated the challenge in meeting all the CSA requirements; particularly where the main objective of a project is to enhance and increase productivity. This reinforces how supporting all three pillars is challenging for a single project and therefore CSA is arguably more achievable when viewed in terms of a portfolio of activities which can collectively compensate for the limitations of a single project.

Abstract The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination ( R 2 ) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback.

Abstract Wearable technology excels in estimating kinematic and physiological data, but estimating biological torques remains an open challenge. Deformation of the skin above contracting muscles—surface‐level muscle deformation—has emerged as a promising signal for joint torque estimation. However, a lack of ground‐truth measures of surface‐level muscle deformation has complicated the evaluation of wearable sensors designed to measure surface‐level muscle deformation. A non‐contact methodology is proposed for ground‐truth measurement of surface‐level muscle deformation using a 2D laser profilometer. It shows how three metrics of surface‐level muscle deformation—peak radial displacement: r = 0.94 ± 0.05, surface curvature: r = 0.78 ± 0.10, surface strain: r = 0.83 ± 0.12—correlate strongly to changes in volitional elbow torque, further exploring the impact of measurement location or joint angle on these relationships. A nonlinear, lead‐lag relationship between surface‐level muscle deformation and torque is also found. The findings suggest that surface‐level muscle deformation is a promising signal for non‐invasive, real‐time estimates of torque. By standardizing measurement, the methodology can help inform the design of future wearable sensors.

Community-based locomotor training post-stroke has shown improvements in independent ambulation by increasing dose, intensity, and specificity of walking practice. Robotic ankle exoskeletons hold the potential to facilitate continued rehabilitation at home, but understanding what aspects of the design are most relevant for successful translation to the community presents a challenge. Here, we design a portable rigid ankle exoskeleton to use as a research platform for investigating the effect of assistance on post-stroke gait during overground, community-based walking. We first test our device with stroke survivors and validate its potential for future community use. We then present a systematic method for quantifying power transmission losses at each transmission stage from the battery to the wearer, using data gathered from walking trials with healthy participants. Our evaluation method revealed inefficiencies in power transfer at the interface level, likely resulting from the compliance in the structural components of the system, which motivates future redesign considerations. Overall, our method provides a framework to identify and characterize the components that must be redesigned to lower exoskeleton weight and maximize performance.

Abstract Experiential learning in biomedical engineering curricula is a critical component to developing graduates who are equipped to contribute to technical design tasks in their careers. This paper presents the development and implementation of an undergraduate and graduate-level soft material robotics design course focused on applications in medical device design. The elective course, offered in a bioengineering department, includes modules on technical topics and hands-on projects relevant to readings, all situated within a human-centered design course. After learning and using first principles governing soft robot design and exploring literature in soft robotics, students propose a new advance in the field in a hands-on design and prototype project. The course described here aims to create a structure to engage students in fabrication and the design approaches taken by practitioners in a specific field, applied here in soft robotics, but applicable to other areas of biomedical engineering. This teaching tips article details the pedagogical tools used to facilitate design and collaboration within the course. Additionally, we aim to highlight ways in which the course creates (1) opportunities to engage undergraduates in design in preparation for capstone courses, (2) outward facing opportunities to connect with practitioners in the field, and (3) the ability to adapt this hands-on experience within a typical lecture structure as well as a hybrid online and in-person offering, thus expanding its utility in bioengineering departments. We reflect on course elements that can inform future design-based course offerings in soft robotics and other design-based multidisciplinary fields in bioengineering.

Running injuries are prevalent, but their exact mechanisms remain unknown largely due to limited real-world biomechanical analysis. Reducing overstriding, the horizontal distance that the foot lands ahead of the body, may be relevant to reducing injury risk. Here, we leverage the geometric relationship between overstriding and lower extremity sagittal segment angles to demonstrate that wearable inertial measurement units (IMUs) can predict overstriding during treadmill and overground running in the laboratory. Ten recreational runners matched their strides to a metronome to systematically vary overstriding during constant-speed treadmill running and showed similar overstriding variation during comfortable-speed overground running. Linear mixed models were used to analyze repeated measures of overstriding and sagittal segment angles measured with motion capture and IMUs. Sagittal segment angles measured with IMUs explained 95% and 98% of the variance in overstriding during treadmill and overground running, respectively. We also found that sagittal segment angles measured with IMUs correlated with peak braking force and explained 88% and 80% of the variance during treadmill and overground running, respectively. This study highlights the potential for IMUs to provide insights into landing and loading patterns over time in real-world running environments, and motivates future research on feedback to modify form and prevent injury.

Bayesian optimization (BO) with Gaussian processes (GP) has become an indispensable algorithm for black box optimization problems. Not without a dash of irony, BO is often considered a black box itself, lacking ways to provide reasons as to why certain parameters are proposed to be evaluated. This is particularly relevant in human-in-the-loop applications of BO, such as in robotics. We address this issue by proposing ShapleyBO, a framework for interpreting BO's proposals by game-theoretic Shapley values.They quantify each parameter's contribution to BO's acquisition function. Exploiting the linearity of Shapley values, we are further able to identify how strongly each parameter drives BO's exploration and exploitation for additive acquisition functions like the confidence bound. We also show that ShapleyBO can disentangle the contributions to exploration into those that explore aleatoric and epistemic uncertainty. Moreover, our method gives rise to a ShapleyBO-assisted human machine interface (HMI), allowing users to interfere with BO in case proposals do not align with human reasoning. We demonstrate this HMI's benefits for the use case of personalizing wearable robotic devices (assistive back exosuits) by human-in-the-loop BO. Results suggest human-BO teams with access to ShapleyBO can achieve lower regret than teams without.

Skeletal muscle velocity is a key indicator of neuromuscular function, and monitoring its changes plays important role in tracking the progression of musculoskeletal diseases, injuries, and fatigue. However, existing methods for non-invasive estimation of skeletal muscle velocity primarily use B- mode ultrasound, often with processing methods that are time-consuming or computationally expensive, with varying accuracy based on tissue structure. Here, we propose a spectral Doppler envelope estimation method designed for skeletal muscle measurements. When compared to the modified signal noise slope intersection (MSNSI) method, our method reduces the overall mean absolute error by 13.9% and the mean absolute zero error by 82.1%. We validated our method using a portable ultrasound system on a benchtop setup that mimics the acoustic properties, measurement angles, and velocity patterns of skeletal muscles. Ex vivo and in vivo muscle velocity estimates of parallel and pennate muscles will be compared to those obtained using the MSNSI method and manual tracking of B-Mode images. Our proposed method could enable automated estimation of skeletal muscle velocities during dynamic activities in unconstrained environments, providing new insight into neuromuscular function and movement biomechanics, with potential applications in monitoring fatigue, disease progression, or injury recovery. [Work supported by the National Science Foundation.]