Elliott J. Rouse

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Robotic prostheses commonly use joint-space impedance controllers parameterized by stiffness, damping, and an equilibrium angle to create desired behaviors. Although these controllers are often interpreted as equilibrium angle tracking controllers, their parameters are chosen such that ground and user interactions cause a different kinematic pattern to emerge, complicating their design, tuning, and interpretation. To address this challenge, we introduce an alternative formulation of the impedance controller comprising both a feedback position control term and feedforward torque control term. This equivalent form clarifies how the impedance parameters shape both the nominal and perturbed behaviors of the controller. In both theory and experiments with an above-knee amputee participant, we demonstrate that controllers with appropriately designed feedforward torque components can produce identical nominal behaviors despite differences in stiffness and damping, which primarily govern how the system responds to perturbations. Our findings offer important insights for prosthesis controller design and tuning: 1) our decoupled parameterization allows independent prescription of an impedance controller’s nominal and off-nominal behaviors; 2) tuning stiffness and damping based on nominal walking alone is insufficient; and 3) even non-impedance paradigms can benefit from applying impedance concepts to achieve robust real-world behavior.

The stiffness of passive lower-limb exoskeletons and orthoses governs their assistance. A common practice in the design of these systems is to assume the stiffness of the device is determined only by the intended elastic element (e.g., spring), while the structural components, human attachments, and soft tissues are considered rigid. In practice, the mechanical behavior of orthoses is significantly affected by the compliance of these elements, which drastically impacts the assistance provided. In this work, we present a linkage model with compliant elements that can accurately predict the applied stiffness of ankle-foot orthoses, and retroactively estimate the stiffness of unintended spring elements from published data. The compliant model accurately predicted the torque trajectories of two published passive orthoses with modeled peak torques within 4 % to 7 % of measured values. In contrast, the rigid model greatly overestimated the peak torques, predicting 203 % to 376 % of the measured values. The compliant model also indicated that an onboard joint encoder could only measure 52 % to 69 % of the peak ankle angle recorded with motion capture. The compliant model was also used to reassess the stiffness range of a variable-stiffness orthosis, indicating that its adjustable range is likely 69 % of rigid model predictions. Overall, this work highlights the need to consider how unmodeled compliance affects the mechanical behavior of orthoses and provides a foundation for further exploration.

Exoskeleton controllers have recently employed machine learning (ML) techniques to provide appropriate assistance throughout the terrains of the real world. One successful approach has been to learn a mapping between an exoskeleton wearer's kinematic measurements and a gait state vector that encodes how the wearer is currently walking (i.e. gait phase, speed), and then dynamically update the assistance based on the gait state. However, these methods require paired datasets of input kinematics to output gait states, which usually involves manual, time-consuming labeling of data from participants wearing specific exoskeletons and thus limits the scalability of these ML methods. A prior solution to this challenge—leveraging large pre-labeled datasets of normative human walking—introduces another problem, in that networks trained on these datasets learn only normative locomotion patterns, and thus may deteriorate when the data are changed by wearing the exoskeleton itself. In this context, we present an unsupervised-learning-based approach to both bypass the requirement of labeled data for gait state prediction and address the difficulty of domain adaptation from normative to exoskeleton-assisted walking. We validate our method in a set of walking simulations that featured exoskeleton data from 14 participants. This model showed significant improvements in state estimation relative to a model trained solely on pre-labeled normative walking, while also not requiring ground truth labels. This work presents a foundation that demonstrates labeled, device-specific data may not be required for predicting walking behavior in real time.

Accurate and reliable digital twins of humans and wearable robots can revolutionize rehabilitation robotics. Here, we introduce MyoAssist 0.1, a sub-suite of MyoSuite focused on musculoskeletal simulation environments with assistive devices such as prosthetics and exoskeletons. This open-source platform enables the study and development of human-device interactions, control strategies, and assistive robotics. We present two new simulation environments: myoMPL featuring an arm amputee model with a robotic prosthetic arm, and myoOSL featuring a leg amputee model with a robotic prosthetic leg. The myoMPL environment features a bimanual manipulation task for a shoulder disarticulation amputee using a Modular Prosthetic Limb (MPL), where the task is to pick up an object with the biological hand, pass it to the prosthetic hand, and place it at a target location. The myoOSL environment simulates an above-knee amputee using the Open-Source Leg (OSL) to traverse challenging terrains such as rough surfaces, hills, and stairs. Despite some simplifications in modeling the nuanced constraints of human and prosthetic systems under real-world conditions, these environments provide a foundational simulation framework that supports interdisciplinary research on the interplay between musculoskeletal dynamics and assistive devices. Both myoMPL and myoOSL are featured in MyoChallenge, an annual competition at the NeurIPS conference. All code is accessible through the MyoSuite GitHub repository.

The mechanical impedance of the human lower-limb joints during locomotion encodes our understanding of how the neuromotor system regulates the behavior of these tasks. Impedance is also a key component of several strategies for translating this behavior to robots, powered prosthetic limbs, and people empowered by exoskeletons. However, due to difficulty in making accurate measurements, there is little empirical evidence for the impedance behaviors of joints other than the ankle during active walking tasks. In this letter we propose a measurement system based on a highly backdrivable quasi-direct-drive actuator and a carefully calibrated actuator torque model. Bench-top validation with known mechanical impedance human-substitutes, confirms the viability of this system as an impedance measurement tool. A pilot study with two subjects utilizing a custom knee-exoskeleton apparatus confirms the feasibility of this system for human walking experiments.

Accurate impedance control is key for biomimetic mechanical behavior in lower-limb robotic prostheses. However, due to compliance, friction, and inertia in the drivetrain, the commonly used open-loop impedance control strategy can often produce inaccurate results without appropriate compensation. This article presents a controller that accounts for these dynamics to improve the impedance rendering accuracy of a robotic prosthesis research platform, the Open-Source Leg (OSL v2). We first develop a dynamic model of the OSL v2’s drivetrain and show that it accurately predicts the system's joint torque with 97% mean explained variance across a diverse array of experiments. We then present a controller that compensates for the OSL v2’s inherent dynamics using a combination of feedback linearization and actuator-state feedback control. We experimentally validate this controller on the OSL v2 with a rotary dynamometer and in treadmill walking experiments. We show that it can render various constant impedance behaviors with higher stiffness and damping accuracy than a baseline controller. We also show our controller's ability to replicate the variable impedance trajectories of the human ankle joint, suggesting that this control approach could enable robotic prostheses that are biomimetic in their mechanical impedance in addition to their kinematics and kinetics.

Challenges with community mobility are among the most prevalent disabilities worldwide, yet the current standard of care-passive orthotics-have remained largely unchanged for hundreds of years. Powered orthoses have been developed to address these shortcomings, but challenges with reliability, safety, weight, noise, and cost have stunted commercial translation. In this work, we present the Variable Stiffness Orthosis (VSO), a quasi-passive, ankle-foot orthosis that strikes a balance between powered and passive orthoses in terms of functionality and commercial practicality. The VSO can render customized, nonlinear torque-angle relationships via passive cam-based modules, which can feature extreme or even negative stiffness. A passive cam switching mechanism also decouples energy storage and return, allowing push off to be augmented with energy recycled from early stance phase, and changing equilibrium angle to simultaneously promote swing-phase foot clearance and standing stability. The VSO also features step-to-step stiffness adjustments spanning the softest to stiffest commercial AFOs via a motorized spring support. Pilot testing was performed on two participants with and without sciatic nerve injury (SNI). Both participants had activity-dependent stiffness preferences that spanned a large stiffness range. Preliminary results showed that using the VSO led to reduced foot drop, increased self-selected speed, increased total ankle moments, reduced biological moments, reduced toe striking, and reduced steppage in the participant with SNI compared to daily-use AFO and shoes-only conditions.

In ankle-foot orthosis development, it is challenging to both specify the appropriate ankle mechanics and design systems that can physically render them. Recently, a new ankle-foot orthosis-the Variable Stiffness Orthosis (VSO)–was introduced to allow customization of the shape of the joint’s torque-angle relationship via a cam-based transmission. A module in the VSO permits switching between two coupled torque-angle relationships at desired kinematic transitions. This module decouples energy storage and return (DESR), enabling new functionality, including varying the ankle’s equilibrium position and exchanging energy between gait phases. However, the torque-angle relationships are defined by many parameters and subject to substantial constraints. We developed an optimization framework to design two versions of the DESR module to address foot drop. The angle module was designed to maximize swing ankle angle, and the energy module was designed to maximize energy recycled from early stance phase to augment push off. We validated the results of the optimization with brute-force searching and empirically tested the DESR mechanics in a rotary dynamometer. The angle module facilitated swing angles of up to 0.63° dorsiflexion, while simultaneously permitting a plantarflexed standing angle, and the energy module recycled up to 1.84 J.

Common existing ankle-foot prostheses are carbon-composite springs that cannot vary their mechanics to meet the biomechanical demands of differing activities. This lack of adjustment leads to discomfort and compensatory movements, which ultimately lower mobility and quality of life for people with amputation. Emerging variable-stiffness ankle prostheses have the potential to address this challenge by intelligently changing their stiffness between activities. However, the appropriate stiffness for each user and activity remains an open question. One potential strategy is to modulate the stiffness to the preferred settings of the wearer. Prosthesis users are sensitive and consistent in the selection of their preferred stiffness settings. However, previous work has primarily focused on level walking; a detailed analysis of preferred stiffness variation across multiple activities remains unexplored. In this preliminary study, we quantified the preferred prosthetic ankle stiffness of four participants with below-knee amputation across five different activities. Preferred stiffness settings varied substantially between activities. When averaged across partic-ipants, the preferred ankle stiffness differed between activities by 31.8% of the preferred stiffness for level walking. This difference reflects changes in stiffness that span up to four categories of commercial prostheses. In addition, prosthetic ankle kinematics varied across activities and stiffness, with mean peak dorsiflexion reaching as great as 15.3° during incline walking. The differences in preferred stiffness across activities, coupled with the corresponding changes in kinematics, underscore the potential of user preference and its implications in variable-stiffness prostheses.

Modern prosthetic feet have spring-like mechanics, deflecting and storing energy during mid-stance, and returning this energy during terminal stance. Researchers and manufacturers of prosthetic feet often tout the high energy storage and return of new prosthetic designs, but there is limited evidence supporting the notion that more energy storage and return is best for the user in terms of either biomechanical outcomes or user preference. In this paper, the relationship between ankle stiffness and energy storage is evaluated at stiffness levels within ± 20% of the user-preferred (self-selected) stiffness. For all eight amputee subjects, energy storage and return was highest at stiffness levels below the preferred stiffness. These results indicate that maximal energy storage and return may occur at an uncomfortably low stiffness, casting doubt on the utility of energy storage and return as a metric for evaluating prosthetic designs.

BACKGROUND: Ankle-foot orthoses (AFOs) are commonly used by children with cerebral palsy (CP), but traditional solutions are unable to address the heterogeneity and evolving needs amongst children with CP. One key limitation lies in the inability of current passive devices to customize the torque-angle relationship, which is essential to adapt the support to the specific individual needs. Powered alternatives can provide customized behavior, but often face challenges with reliability, weight, and cost. Overall, clinicians find certain barriers that hinder their prescription. In recent work, the Variable Stiffness Orthosis (VSO) was developed, enabling stiffness customization without the need for motors or sophisticated control. METHODS: This work evaluates a pediatric version of the VSO (inGAIT-VSO) by investigating its impact on the walking performance of children with CP and its potential to be used as a tool for assessing the effect of variable stiffness on pathological gait. Data was collected for three typical developing (TD) children and six pediatric participants with CP over two sessions involving walking/balance tasks and questionnaires. RESULTS: The sensors of the inGAIT-VSO provided useful information to assess the impact of the device. Increasing the stiffness of the inGAIT-VSO significantly reduced participants' dorsiflexion and plantarflexion. Despite reduced range of motion, the peak restoring torque increased with stiffness. Overall the participants' gait pattern was altered by reducing crouch gait, preventing drop-foot and supporting body weight. Participants with CP exhibited significantly lower (p < 0.05) physiological cost when walking with the inGAIT-VSO compared to normal condition (own AFO or shoes only). Generally, the device did not impair walking and balance of the participants compared to normal conditions. According to the questionnaire results, the inGAIT-VSO was easy to use and participants reported positive experiences. CONCLUSION: The inGAIT-VSO stiffnesses significantly affected participants' plantarflexion and dorsiflexion and yielded objective data regarding walking performance in pathological gait (e.g. ankle angle, exerted torque and restored assistive energy). These effects were captured by the sensors integrated in the device without using external equipment. The inGAIT-VSO shows promise for customizing AFO stiffness and aiding clinicians in selecting a personalized stiffness based on objective metrics.

In addition to lifting up the body during gait, human legs provide stabilizing torques that can be modeled as a spring-damper mechanical impedance. While powered prosthetic leg actuators can also imitate spring-damper behaviors, the rendered impedance can be quite different from the desired impedance, stemming from unmodeled transmission characteristics (e.g., sliding friction, bearing damping, gear inefficiency, etc.). Moreover, for powered prostheses to mimic human joint impedance, they will need actuators that accurately render a wide range of mechanical impedances in a variety of ground contact conditions, including nearly free-swinging behavior in swing phase and stiff spring-like behavior in stance phase. For series-elastic prosthetic leg actuators, as in the Open-Source Leg (OSL), these sudden output inertia changes present a challenge for traditional cascaded impedance control. In this paper we propose a solution based on disturbance observers (DOBs) and full-state feedback (FSF) impedance control. The DOB serves to mask transmission imperfections, while the FSF controller (via pole-zero placement) specifies the actuator impedance that couples to the uncertain joint inertia. We validate our control framework on an OSL-like two-actuator dynamometry testbed.

Exoskeletons have enormous potential to improve human locomotive performance1–3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals. A learning-in-simulation framework for wearable robots uses dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments to assist versatile activities.

Ankle foot orthoses (AFOs) are one of the most prescribed mobility devices for individuals with walking disability from conditions like cerebral palsy (CP) and stroke. Current AFO designs offer a fixed stiffness during walking, functioning optimally at only a single speed. The primary goal of this study was to develop and validate a quasi-passive robotic AFO that automatically adjusted stiffness to a user's walking speed. We designed a leaf spring AFO with an adjustable pivot point actuated by a compact linear servo motor. We developed a walking speed estimator using onboard sensors to automatically adjust the pivot point location, and therefore device stiffness. First, we characterized stiffness range, adjustment response time, and battery life during walking. Next, we performed clinical device validation testing in five individuals with CP during stand-to-run acceleration bouts and at constant walking speeds. The AFO exhibited a stiffness range of 60 to 250 Nm/rad during normal walking and up to 300-400 Nm/rad under certain conditions (e.g., running) for the CP participants. The device was able to adjust stiffness by ∼200 Nm/rad during swing phase in ∼0.25 seconds. Battery life approached 6000 steps. The on-board controller accurately predicted relative changes in walking speed for the five participants with CP (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.92 ± 0.04), demonstrating the ability to automatically increase device stiffness with ambulation speed. This study advances the state-of-the-art for quasi-passive AFOs that can function optimally at different ambulation speeds.

Emerging wearable, assistive, and mobile robots seek to interact with the environment and/or humans in a compliant, dynamic, and adaptable way. Springs are critical to achieving this objective, but the associated increase in volume, mass, and complexity is limiting their application and impact in this rapidly developing field. This article presents a novel rotary spring architecture that is both lightweight and compact. Our two-part spring consists of radially-spaced cantilever beams that interface with an internal, gear-like camshaft. We present the concept and equations governing their mechanics and design. To facilitate broad adoption, we introduce an open-source design tool, which enables the design of custom springs in minutes instead of hours or days. We also empirically demonstrate our design with four test springs and validate the achievement of target spring rates and deflections. Finally, we present several redesigns of existing springs in the robotics literature to demonstrate the wide applicability of our spring architecture.

Background Ankle joint stiffness and viscosity are fundamental mechanical descriptions that govern the movement of the body and impact an individual’s walking ability. Hence, these internal properties of a joint have been increasingly used to evaluate the effects of pathology (e.g., stroke) and in the design and control of robotic and prosthetic devices. However, the reliability of these measurements is currently unclear, which is important for translation to clinical use. Research question Can we reliably measure the mechanical impedance parameters of the ankle while standing and walking? Methods Eighteen able-bodied individuals volunteered to be tested on two different days separated by at least 24 hours. Participants received several small random ankle dorsiflexion perturbations while standing and during the stance phase of walking using a custom-designed robotic platform. Three-dimensional motion capture cameras and a 6-component force plate were used to quantify ankle joint motions and torque responses during normal and perturbed conditions. Ankle mechanical impedance was quantified by computing participant-specific ensemble averages of changes in ankle angle and torque due to perturbation and fitting a second-order parametric model consisting of stiffness, viscosity, and inertia. The test-retest reliability of each parameter was assessed using intraclass correlation coefficients (ICCs). We also computed the minimal detectable change (MDC) for each impedance parameter to establish the smallest amount of change that falls outside the measurement error of the instrument. Results In standing, the reliability of stiffness, viscosity, and inertia was good to excellent (ICCs=0.67–0.91). During walking, the reliability of stiffness and viscosity was good to excellent (ICCs=0.74–0.84) while that of inertia was fair to good (ICCs=0.47–0.68). The MDC for a single subject ranged from 20%–65% of the measurement mean but was higher (>100%) for inertia during walking. Significance Results indicate that dynamic measures of ankle joint impedance were generally reliable and could serve as an adjunct clinical tool for evaluating gait impairments.

One challenge to achieving widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are "tuned" to optimize a physiological or biomechanical objective. However, these approaches are resource intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is likely derived from many factors, including comfort, fatigue, and stability, among others. This work introduces an approach to conveniently tune the four parameters of an exoskeleton controller to maximize user preference. Our overarching strategy is to leverage the wearer to internally balance the experiential factors of wearing the system. We used an evolutionary algorithm to recommend potential parameters, which were ranked by a neural network that was pretrained with previously collected user preference data. The controller parameters that had the highest preference ranking were provided to the exoskeleton, and the wearer responded with real-time feedback as a forced-choice comparison. Our approach was able to converge on controller parameters preferred by the wearer with an accuracy of 88% on average when compared with randomly generated parameters. User-preferred settings stabilized in 43 ± 7 queries. This work demonstrates that user preference can be leveraged to tune a partial-assist ankle exoskeleton in real time using a simple, intuitive interface, highlighting the potential for translating lower-limb wearable technologies into our daily lives.

Exoskeletons have shown great potential to enhance locomotion by augmenting the lower limb. While most research has focused on steady-state ambulatory activities, the ability to assist transient, ballistic tasks is also important for understanding the potential of exoskeletons in mobility enhancement. In this preliminary study (N = 5), we developed an individually-customized control strategy to assist vertical jumping. The control strategy was deployed on bilateral ankle exoskeletons (ExoBoot, Dephy Inc.). We structured the control strategy as a work loop that parameterized the assistance provided during the jump. We show that configuring the controller based on individual biomechanics and user preferences facilitates increased vertical jump height when using exoskeleton assistance. In addition, we demonstrate that a user's squat depth can have a significant (p < 0.05) impact on height achieved, but that this depth does not need to be optimized; rather, the exoskeleton provides the maximum performance assistance from both preferred- and deep-squat conditions. Jump height increased by 7.2% with the exoskeleton at its maximum assistance setting, which is comparable to or greater than previous systems.

Modern exoskeletons are typically developed to optimize for a single, physiological objective, the “gold standard” of which is a reduction of the wearer's metabolic rate. However, recent research suggests that these changes in metabolic rate are not yet perceivable on average. To address this gap, this study explores a novel economic value metric to quantify the value of exoskeleton assistance. The overarching goal of this work is the development of a perceptible metric that leverages the user experience to quantify exoskeleton success. We use the Vickrey second-price auction to obtain the monetary compensation needed for participants to continue walking for consecutive two-minute bouts. Comparing the participant's bidding trends when wearing and not wearing an exoskeleton captures the economic value of the experience, termed Marginal Value (MV). To reduce the logistical burden of the auction, we simulated human participants (robo-bidders) to compete alongside real participants. This work presents a sensitivity analysis to understand how the number and bidding behavior of the robo-bidders affects our economic value metric, MV. We found that MV was not significantly affected by the number of robo-bidders or their bidding behavior (i.e. effort rate). The bidding behavior of the human participants was affected by the robo-bidder effort rate, indicating that there is interplay in the bidding dynamics among the auction participants, but these changes do not significantly affect the marginal value. This study tentatively validates the current approach in generating our proposed metric for exoskeleton success, paving the way for economic value to be further explored as a holistic, personalized metric for the development of lower-limb exoskeletons.

A key step in the development of lightweight, high performance robotic systems is the modeling and selection of permanent magnet brushless direct current (BLDC) electric motors. Typical modeling analyses are completed a priori, and provide insight for properly sizing a motor for an application, specifying the required operating voltage and current, as well as assessing the thermal response and other design attributes (e.g.transmission ratio). However, to perform these modeling analyses, proper information about the motor's characteristics are needed, which are often obtained from manufacturer datasheets. Through our own experience and communications with manufacturers, we have noticed a lack of clarity and standardization in modeling BLDC motors, compounded by vague or inconsistent terminology used in motor datasheets. The purpose of this tutorial is to concisely describe the governing equations for BLDC motor analyses used in the design process, as well as highlight potential errors that can arise from incorrect usage. We present a power-invariant conversion from phase and line-to-line reference frames to a familiar q-axis DC motor representation, which provides a ``brushed'' analogue of a three phase BLDC motor that is convenient for analysis and design. We highlight potential errors including incorrect calculations of winding resistive heat loss, improper estimation of motor torque via the motor's torque constant, and incorrect estimation of the required bus voltage or resulting angular velocity limitations. A unified and condensed set of governing equations is available for designers in the Appendix. The intent of this work is to provide a consolidated mathematical foundation for modeling BLDC motors that addresses existing confusion and fosters high performance designs of future robotic systems.

Most commercial ankle-foot orthoses (AFOs) are passive structures that cannot modulate stiffness to assist with a diverse range of activities, such as stairs and ramps. It is sometimes possible to change the stiffness of passive AFOs through reassembly or benchtop adjustment, but they cannot change stiffness during use. Passive AFOs are also limited in their ankle mechanics and cannot replicate a biomimetic, nonlinear torque-angle relationship. Many research labs have developed ankle exoskeletons that show promise as viable alternatives to passive AFOs, but they face challenges with reliability, mass, and cost. Consequently, commercial translation has largely failed to date. Here we introduce the Variable Stiffness Orthosis (VSO), a quasi-passive variable stiffness ankle-foot orthosis that strikes a balance between powered and passive systems, in terms of mass, complexity, and onboard intelligence. The VSO has customizable torque-angle relationships via a cam transmission, and can make step-to-step stiffness adjustments via motorized reconfiguration of a spring support along a lead-screw. In this work, we introduce two versions: a nominal and a stiff prototype, which differ primarily in their mass and available stiffness levels. The available torque-angle relationships are measured on a custom dynamometer and closely match model predictions. The experimental results showed that the prototypes are capable of producing ankle stiffness coefficients between 9 - 330 Nm/rad.

Ankle propulsion is essential for efficient human walking. In recent years, several working principles have been investigated and applied to ankle-foot orthoses (AFOs) to enhance the work of the plantarflexor muscles and achieve proper propulsion during gait. Comparing the performance and effectiveness of different designs is difficult because researchers do not have a standardized set of criteria and procedures to follow. This leads to a wide range of tests being conducted, with variations in important factors such as walking speed and assistance provided, which greatly affect users' kinematics and kinetics. In this work, we investigate the possibilities and potential benefits of two of the most important design principles for supporting ankle propulsion with unpowered AFOs. To this end, we present and evaluate two AFO prototypes with springs parallel to the Achilles tendon based on: (i) a linear compression spring, and (ii) a customized leaf spring-cam transmission with a non-linear ankle torque-angle curve. The effects of both AFOs are reported for a case study with one healthy participant using both prototypes at two walking speeds under the same experimental conditions. Large reductions in muscular activity were found when the user received assistance, and ankle kinematics were influenced by the different assistance approaches. This case study was intended as a first step to provide insights on how two promising principles can passively support push-off during gait.

La propulsión que ejerce el tobillo es esencial para la ejecuci´on de una marcha humana eficiente. En los últimos años, se han venido aplicando diversos principios de funcionamiento a las ortesis de tobillo y pie (AFO), con el fin de mejorar el trabajo de los músculos flexores plantares y lograr así una propulsión adecuada durante la marcha. Es difícil comparar la ejecución y eficacia de los diferentes diseños debido a que los investigadores no siguen un conjunto estandarizado de criterios y procedimientos comunes. Esto lleva a la realización de una amplia gama de pruebas, con variaciones en factores importantes como la velocidad de marcha y la asistencia proporcionada, lo que afecta en gran medida a la cinemática y la cinética de los usuarios. Este trabajo está enfocado a comprender las posibilidades y los potenciales beneficios de dos importantes principios de diseño para asistir la propulsión del tobillo con una AFO no actuada. Para ello, presentamos y evaluamos dos prototipos de AFO con resorte paralelo al tendón de Aquiles, basados en: (i) un resorte de compresión lineal, y (ii) una transmisión no lineal y personalizada de leva con resorte de ballesta. Se presentan los efectos de ambas AFOs para un estudio de caso con un usuario sano usando ambos prototipos a dos velocidades de marcha bajo las mismas condiciones experimentales. Se encontraron grandes reducciones en la actividad muscular cuando el usuario recibió asistencia, y la cinemática del tobillo estuvo influenciada por los diferentes principios de dicha asistencia. Este estudio de caso fue pensado como un primer intento para proporcionar información sobre cómo dos principios prometedores pueden asistir de forma pasiva la propulsión de tobillo durante la marcha.

The Robotics Major at the University of Michigan was successfully launched in the 2022-23 academic year as an innovative step forward to better serve students, our communities, and our society. Building on our guiding principle of "Robotics with Respect" and our larger Robotics Pathways model, the Michigan Robotics Major was designed to define robotics as a true academic discipline with both equity and excellence as our highest priorities. Understanding that talent is equally distributed but opportunity is not, the Michigan Robotics Major has embraced an adaptable curriculum that is accessible through a diversity of student pathways and enables successful and sustained career-long participation in robotics, AI, and automation professions. The results after our planning efforts (2019-22) and first academic year (2022-23) have been highly encouraging: more than 100 students declared Robotics as their major, completion of the Robotics major by our first two graduates, soaring enrollments in our Robotics classes, thriving partnerships with Historically Black Colleges and Universities. This document provides our original curricular proposal for the Robotics Undergraduate Program at the University of Michigan, submitted to the Michigan Association of State Universities in April 2022 and approved in June 2022. The dissemination of our program design is in the spirit of continued growth for higher education towards realizing equity and excellence. The most recent version of this document is also available on Google Docs through this link: https://ocj.me/robotics_major

Abstract For augmentative exoskeletons that assist able-bodied users, a clear metric of success remains an open question. Here we leverage the Vickrey second-price auction to quantify the economic value added by lower-limb exoskeletons and their assistance. We posited that if exoskeletons provided helpful assistance during a difficult task, this value could be quantified through a lowering of participant auction bids to continue walking. The bidding results were compared across different conditions to determine the economic value of the exoskeleton, bearing in mind also the cost of wearing the added mass of the exoskeleton. Results show that the total value of the exoskeleton and assistance was modest. While most participants found the assistance itself valuable, this value was mostly offset by the extra mass added of wearing the exoskeleton. Our approach provides insight into how exoskeleton wearers may value different aspects of user experience. These results suggest economic value may be a powerful tool in the design and control of exoskeletons that maximize user benefit.

Springs are essential mechanical elements that are used across a wide variety of industries and mechanisms. Common across many spring types and applications is the importance of compactness, low mass and customizability. In this paper, we present a novel rotary spring design that is lightweight, compact and customizable. In addition, we empirically validate the design by experimentally quantifying the performance of two test springs on a custom dynamometry testbed. Our two-part spring geometry is comprised of a central rotating gear-like cam shaft, and a disk that includes a circular array of radially-spaced tapered cantilevered beams. The two springs that we designed and tested matched desired performance specifications within 3–6%, confirming the efficacy of this unique design approach.

This data is used to support and evaluate the conclusions draw in the paper "The Economic Value of Augmentative Exoskeletons and their Assistance". The abstract for this paper is reproduced here: For augmentative exoskeletons that assist able-bodied users, a clear metric of success remains an open question. Here we leverage the Vickrey second-price auction to quantify the economic value added by lower-limb exoskeletons and their assistance. We posited that if exoskeletons provided helpful assistance during a difficult task, this value could be quantified through a lowering of participant auction bids to continue walking. The bidding results were compared across different conditions to determine the economic value of the exoskeleton, bearing in mind also the cost of wearing the added mass of the exoskeleton. Results show that the total value of the exoskeleton and assistance was modest. While most participants found the assistance itself valuable, this value was mostly offset by the extra mass added of wearing the exoskeleton. Our approach provides insight into how exoskeleton wearers may value different aspects of user experience. These results suggest economic value may be a powerful tool in the design and control of exoskeletons that maximize user benefit.

&lt;p&gt;Understanding the success of augmentative exoskeletons is critical to their impact on society. Many exoskeletons are developed to address impairments stemming from neuromotor pathology, which provides guidance for understanding their abilities; however, for augmentative exoskeletons that assist able-bodied users, a clear metric of success remains an open question. In this study, we leverage an approach from economics--the Vickrey second-price auction--to quantify the economic value added by lower-limb exoskeletons during uphill walking. In our protocol, participants describe the monetary compensation needed to continue walking uphill for consecutive bouts of two minutes. To incentivize truthful descriptions of participant's “price to walk,” their compensation amounts were provided as bids in Vickrey auctions before each bout. We compared these data across different conditions to determine the marginal value of the exoskeleton weight, assistance, and weight + assistance. Our approach found that the total value of the exoskeleton and its assistance was modest ($3.40/hr, SD: $3.35/hr); while most participants found the assistance itself valuable ($19.76/hr, SD: $19.43/hr), this value was nearly offset by the cost of wearing the exoskeleton weight (-$18.59/hr, SD: $18.28/hr). Our approach and results demonstrate that economic value can be a powerful tool to develop exoskeletons that maximize user benefit. &lt;/p&gt;

Understanding the success of augmentative exoskeletons is critical to their impact on society. Many exoskeletons are developed to address impairments stemming from neuromotor pathology, which provides guidance for understanding their abilities; however, for augmentative exoskeletons that assist able-bodied users, a clear metric of success remains an open question. In this study, we leverage an approach from economics–the Vickrey second-price auction–to quantify the economic value added by lower-limb exoskeletons during uphill walking. In our protocol, participants describe the monetary compensation needed to continue walking uphill for consecutive bouts of two minutes. To incentivize truthful descriptions of participant’s “price to walk,” their compensation amounts were provided as bids in Vickrey auctions before each bout. We compared these data across different conditions to determine the marginal value of the exoskeleton weight, assistance, and weight + assistance. Our approach found that the total value of the exoskeleton and its assistance was modest ($3.40/hr, SD: $3.35/hr); while most participants found the assistance itself valuable ($19.76/hr, SD: $19.43/hr), this value was nearly offset by the cost of wearing the exoskeleton weight (-$18.59/hr, SD: $18.28/hr). Our approach and results demonstrate that economic value can be a powerful tool to develop exoskeletons that maximize user benefit.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).

Most impedance-based walking controllers for powered knee–ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this article presents a data-driven, phase-based controller for variable-task walking that uses continuously variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N=2$</tex-math></inline-formula> ) show that our data-driven controller 1) features highly linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a torque controller for an ankle exoskeleton that uses state estimation with a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ramp parameters during locomotion. The controller applies torque assistance based on the estimated phase and adapts the torque profile based on the estimated task variables to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in silico that the controller yields phase estimates that are more accurate than state of the art, while also estimating task variables with comparable accuracy to recent machine learning approaches. The controller implemented in an ankle exoskeleton successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (6 able-bodied subjects) and a real-world stress test with extremely uneven terrain.