A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks.

The quadriceps are particularly susceptible to fatigue during repetitive lifting, lowering, and carrying (LLC), affecting worker performance, posture, and ultimately lower-back injury risk. Although robotic exoskeletons have been developed and optimized for specific use cases like lifting-lowering, their controllers lack the versatility or customizability to target critical muscles across many fatiguing tasks. Here, we present a task-adaptive knee exoskeleton controller that automatically modulates virtual springs, dampers, and gravity and inertia compensation to assist squatting, level walking, and ramp and stairs ascent/descent. Unlike end-to-end neural networks, the controller is composed of predictable, bounded components with interpretable parameters that are amenable to data-driven optimization for biomimetic assistance and subsequent application-specific tuning, for example, maximizing quadriceps assistance over multiterrain LLC. When deployed on a backdrivable knee exoskeleton, the assistance torques holistically reduced quadriceps effort across multiterrain LLC tasks (significantly except for level walking) in 10 human users without user-specific calibration. The exoskeleton also significantly improved fatigue-induced deficits in time-based performance and posture during repetitive lifting-lowering. Last, the system facilitated seamless task transitions and garnered a high effectiveness rating postfatigue over a multiterrain circuit. These findings indicate that this versatile control framework can target critical muscles across multiple tasks, specifically mitigating quadriceps fatigue and its deleterious effects.

Towards a Unified Approach for Continuously-Variable Impedance Control of Powered Prosthetic Legs over Walking Speeds and Inclines.

Research in powered prosthesis control has explored the use of impedance-based control algorithms due to their biomimetic capabilities and intuitive structure. Modern impedance controllers feature parameters that smoothly vary over gait phase and task according to a data-driven model. However, these recent efforts only use continuous impedance control during stance and instead utilize discrete transition logic to switch to kinematic control during swing, necessitating two separate models for the different parts of the stride. In contrast, this paper presents a controller that uses smooth impedance parameter trajectories throughout the gait, unifying the stance and swing periods under a single, continuous model. Furthermore, this paper proposes a basis model to represent intertask relationships in the impedance parameters-a strategy that has previously been shown to improve model accuracy over classic linear interpolation methods. In the proposed controller, a weighted sum of Fourier series is used to model the impedance parameters of each joint as continuous functions of gait cycle progression and task. Fourier series coefficients are determined via convex optimization such that the controller best reproduces the joint torques and kinematics in a reference able-bodied dataset. Experiments with a powered knee-ankle prosthesis show that this simpler, unified model produces competitive results when compared to a more complex hybrid impedance-kinematic model over varying walking speeds and inclines.

Improving Task-Agnostic Energy Shaping Control of Powered Exoskeletons with Task/Gait Classification.

Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users. The classifier detects transitions between multiple tasks and gait patterns in order to employ a more optimal, task-agnostic controller based on the weighted sum of multiple optimized energy-shaping controllers. To demonstrate the efficacy of this integrated control strategy, an in-silico assessment is conducted over a range of gait patterns and tasks, including incline walking, stairs ascent/descent, and stand-to-sit transitions. The proposed method surpasses benchmark approaches in 5-fold cross-validation ( p < 0.05 ), yielding 93.17 ± 7.39% cosine similarity and 77.92 ± 19.76% variance-accounted-for across tasks and users. These findings highlight the control approach's adaptability in aligning with human joint moments across various tasks.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Data-Driven Phase-Based Control of a Powered Knee-Ankle Prosthesis for Variable-Incline Stair Ascent and Descent.

Powered knee-ankle prostheses can offer benefits over conventional passive devices during stair locomotion by providing biomimetic net-positive work and active control of joint angles. However, many modern control approaches for stair ascent and descent are often limited by time-consuming hand-tuning of user/task-specific parameters, predefined trajectories that remove user volition, or heuristic approaches that cannot be applied to both stair ascent and descent. This work presents a phase-based hybrid kinematic and impedance controller (HKIC) that allows for semi-volitional, biomimetic stair ascent and descent at a variety of step heights. We define a unified phase variable for both stair ascent and descent that utilizes lower-limb geometry to adjust to different users and step heights. We extend our prior data-driven impedance model for variable-incline walking, modifying the cost function and constraints to create a continuously-varying impedance parameter model for stair ascent and descent over a continuum of step heights. Experiments with above-knee amputee participants (N=2) validate that our HKIC controller produces biomimetic ascent and descent joint kinematics, kinetics, and work across four step height configurations. We also show improved kinematic performance with our HKIC controller in comparison to a passive microprocessor-controlled device during stair locomotion.

Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots.

Lower-limb wearable robots designed to assist people in everyday activities must reliably recover from any momentary confusion about what the user is doing. Such confusion might arise from momentary sensor failure, collision with an obstacle, losing track of gait due to an out-of-distribution stride, etc. Systems that infer a user's walking condition from angle measurements using Bayesian filters (e.g., extended Kalman filters) have been shown to accurately track gait across a range of activities. However, due to the fundamental problem structure and assumptions of Bayesian filter implementations, such estimators risk becoming 'lost' with little hope of a quick recovery. In this paper, we 1) introduce a Monte Carlo-based metric to quantify the robustness of pattern-tracking gait estimators, 2) propose strategies for improving tracking robustness, and 3) systematically evaluate them against this new metric using a publicly available gait biomechanics dataset. Our results, aggregating 2,700 trials of simulated walking of 10 able-bodied subjects under random perturbations, suggest that drastic improvements in robustness (from 8.9% to 99%) are possible using relatively simple modifications to the estimation process without noticeably degrading estimator accuracy.

Automatic Stub Avoidance for a Powered Prosthetic Leg over Stairs and Obstacles.

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This paper presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.5% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.

Controlling Powered Prosthesis Kinematics over Continuous Transitions Between Walk and Stair Ascent.

One of the primary benefits of emerging powered prosthetic legs is their ability to facilitate step-over-step stair ascent by providing positive mechanical work. Existing control methods typically have distinct steady-state activity modes for walking and stair ascent, where activity transitions involve discretely switching between controllers and often must be initiated with a particular leg. However, these discrete transitions do not necessarily replicate able-bodied joint biomechanics, which have been shown to continuously adjust over a transition stride. This paper presents a phase-based kinematic controller for a powered knee-ankle prosthesis that enables continuous, biomimetic transitions between walking and stair ascent. The controller tracks joint angles from a data-driven kinematic model that continuously interpolates between the steady-state kinematic models, and it allows both the prosthetic and intact leg to lead the transitions. Results from experiments with two transfemoral amputee participants indicate that knee and ankle kinematics smoothly transition between walking and stair ascent, with comparable or lower root mean square errors compared to variations from able-bodied data.

Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.

Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with pathological gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.g., stride length and ground inclination) to parameterize the joint torques. Using kinematic models that depend on the gait-state, prior work has used an Extended Kalman filter (EKF) to estimate the gait-state online. However, this EKF suffered from kinematic errors since it used a subject-independent measurement model, and it is still unknown how personalization of this measurement model would reduce gait-state tracking error. This paper quantifies how much gait-state tracking improvement a personalized measurement model can have over a subject-independent measurement model when using an EKF-based gait-state estimator. Since the EKF performance depends on the measurement model covariance matrix, we tested on multiple different tuning parameters. Across reasonable values of tuning parameters that resulted in good performance, personalization improved estimation error on average by 8.5 ± 13.8% for phase (mean ± standard deviation), 27.2 ± 8.1% for stride length, and 10.5 ± 13.5% for ground inclination. These findings support the hypothesis that personalization of the measurement model significantly improves gait-state estimation performance in EKF based gait-state tracking (P≪0.05), which could ultimately enable reliable responses to faster human gait changes.

An Energetic Approach to Task-Invariant Ankle Exoskeleton Control.

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle exoskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.

Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study.

Robotic knee-ankle prostheses have often fallen short relative to passive microprocessor prostheses in time-based clinical outcome tests. User ambulation endurance is an alternative clinical outcome metric that may better highlight the benefits of robotic prostheses. However, previous studies were unable to show endurance benefits due to inaccurate high-level classification, discretized mid-level control, and insufficiently difficult ambulation tasks. In this case study, we present a phase-based mid-level prosthesis controller which yields biomimetic joint kinematics and kinetics that adjust to suit a continuum of tasks. We enrolled an individual with an above-knee amputation and challenged him to perform repeated, rapid laps of a circuit comprising activities of daily living with both his passive prosthesis and a robotic prosthesis. The participant demonstrated improved endurance with the robotic prosthesis and our mid-level controller compared to his passive prosthesis, completing over twice as many total laps before fatigue and muscle discomfort required him to stop. We also show that time-based outcome metrics fail to capture this endurance improvement, suggesting that alternative metrics related to endurance and fatigue may better highlight the clinical benefits of robotic prostheses.

Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton.

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks.

Improving Sit/Stand Loading Symmetry and Timing Through Unified Variable Impedance Control of a Powered Knee-Ankle Prosthesis.

Individuals using passive prostheses typically rely heavily on their biological limb to complete sitting and standing tasks, leading to slower completion times and increased rates of osteoarthritis and lower back pain. Powered prostheses can address these challenges, but have control methods that divide sit-stand transitions into discrete phases, limiting user synchronization across the motion and requiring long manual tuning times. This paper extends our preliminary work using a thigh-based phase variable to parameterize optimized data-driven impedance parameter trajectories for sitting, standing, and walking, with only two classification modes. We decouple the stand-to-sit and sit-to-stand equilibrium angles through a knee velocity-dependent scaling term, reducing the model fitting error by approximately half compared to our previous results. We then experimentally validate the controller with three individuals with above-knee amputation performing sitting and standing transitions to/from three different chair heights. We show that our controller implemented on a powered knee-ankle prosthesis produced biomimetic joint mechanics, resulting in significantly reduced sit/stand loading symmetry and time to complete a 5x sit-to-stand task compared to participants' passive prostheses. Integration with a previously developed walking controller also allowed sit/walk transitions between different chair heights. The controller's biomimetic assistance may reduce the overreliance on the biological limb caused by inadequate passive prostheses, helping improve mobility for people with above-knee amputations.

Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes.

Many powered prosthetic devices use load cells to detect ground interaction forces and gait events. These sensors introduce additional weight and cost in the device. Recent proprioceptive actuators enable an algebraic relationship between actuator torques and ground contact forces. This paper presents a proprioceptive force sensing paradigm which estimates ground reaction forces as a solution to detect gait events without a load cell. A floating body dynamic model is obtained with constraints at the center of pressure representing foot-ground interaction. Constraint forces are derived to estimate ground reaction forces and subsequently timing of gait events. A treadmill experiment is conducted with a powered knee-ankle prosthesis used by an able-bodied subject walking at various speeds and slopes. Results show accurate gait event timing, with pooled data showing heel strike detection lagging by only 6.7 ± 7.2 ms and toe off detection leading by 30.4 ± 11.0 ms compared to values obtained from the load cell. These results establish proof of concept for predicting gait events without a load cell in powered prostheses with proprioceptive actuators.

Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain.

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%).

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking.

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 paper 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 (N=2) 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.

Stair Ascent Phase-Variable Control of a Powered Knee-Ankle Prosthesis.

Passive prostheses cannot provide the net positive work required at the knee and ankle for step-over stair ascent. Powered prostheses can provide this net positive work, but user synchronization of joint motion and power input are critical to enabling natural stair ascent gaits. In this work, we build on previous phase variable-based control methods for walking and propose a stair ascent controller driven by the motion of the user's residual thigh. We use reference kinematics from an able-bodied dataset to produce knee and ankle joint trajectories parameterized by gait phase. We redefine the gait cycle to begin at the point of maximum hip flexion instead of heel strike to improve the phase estimate. Able-bodied bypass adapter experiments demonstrate that the phase variable controller replicates normative able-bodied kinematic trajectories with a root mean squared error of 12.66° and 2.64° for the knee and ankle, respectively. The knee and ankle joints provided on average 0.39 J/kg and 0.21 J/kg per stride, compared to the normative averages of 0.34 J/kg and 0.21 J/kg, respectively. Thus, this controller allows powered knee-ankle prostheses to perform net positive mechanical work to assist stair ascent.

Enhancing Voluntary Motion with Modular, Backdrivable, Powered Hip and Knee Orthoses.

Mobility disabilities are prominent in society with wide-ranging deficits, motivating modular, partial-assist, lower-limb exoskeletons for this heterogeneous population. This paper introduces the Modular Backdrivable Lower-limb Unloading Exoskeleton (M-BLUE), which implements high torque, low mechanical impedance actuators on commercial orthoses with sheet metal modifications to produce a variety of hip- and/or knee-assisting configurations. Benchtop system identification verifies the desirable backdrive properties of the actuator, and allows for torque prediction within ±0.4 Nm. An able-bodied human subject experiment demonstrates that three unilateral configurations of M-BLUE (hip only, knee only, and hip-knee) with a simple gravity compensation controller can reduce muscle EMG readings in a lifting and lowering task relative to the bare condition. Reductions in mean muscular effort and peak muscle activation were seen across the primary squat musculature (excluding biceps femoris), demonstrating the potential to reduce fatigue leading to poor lifting posture. These promising results motivate applications of M-BLUE to additional populations, and the expansion of M-BLUE to bilateral and ankle configurations.

Nonholonomic Virtual Constraints for Control of Powered Prostheses Across Walking Speeds.

This paper presents a method to design a nonholonomic virtual constraint (NHVC) controller that produces multiple distinct stance-phase trajectories for corresponding walking speeds. NHVCs encode velocity-dependent joint trajectories via momenta conjugate to the unactuated degree(s)-of-freedom of the system. We recently introduced a method for designing NHVCs that allow for stable bipedal robotic walking across variable terrain slopes. This work extends the notion of NHVCs for application to variable-cadence powered prostheses. Using the segmental conjugate momentum for the prosthesis, an optimization problem is used to design a single stance-phase NHVC for three distinct walking speed trajectories (slow, normal, and fast). This stance-phase controller is implemented with a holonomic swing phase controller on a powered knee-ankle prosthesis, and experiments are conducted with an able-bodied user walking in steady and non-steady velocity conditions. The control scheme is capable of representing 1) multiple, task-dependent reference trajectories, and 2) walking gait variance due to both temporal and kinematic changes in user motion.

Modeling the Transitional Kinematics Between Variable-Incline Walking and Stair Climbing.

Although emerging powered prostheses can enable people with lower-limb amputation to walk and climb stairs over different task conditions (e.g., speeds and inclines), the control architecture typically uses a finite-state machine to switch between activity-specific controllers. Because these controllers focus on steady-state locomotion, powered prostheses abruptly switch between controllers during gait transitions rather than continuously adjusting leg biomechanics in synchrony with the users. This paper introduces a new framework for powered prosthesis control by modeling the lower-limb joint kinematics over a continuum of variable-incline walking and stair climbing, including steady-state and transitional gaits. Steady-state models for walking and stair climbing represent joint kinematics as continuous functions of gait phase, forward speed, and incline. Transition models interpolate kinematics as convex combinations of the two steady-state models, with an additional term to account for kinematics that fall outside their convex hull. The coefficients of this convex combination denote the similarity of the transitional kinematics to each steady-state mode, providing insight into how able-bodied individuals continuously transition between ambulation modes. Cross-validation demonstrates that the model predictions of untrained kinematics have errors within the range of physiological variability for all joints. Simulation results demonstrate the model's robustness to incline estimation and mode classification errors.