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1. Characterizing Everyday Locomotion Behaviors in Persons with Lower Limb Loss: A Month-Long Wearable Sensor Study

   https://doi.org/10.3390/app152312757  

   Acasio, Julian C; Wang, Yisen; Fehr, Katherine Heidi; Hendershot, Brad D; Adamczyk, Peter G (December 2025, Applied Sciences)

   Monitoring mobility outcomes in real-world environments can provide a distinct perspective compared to traditional outcome measures obtained in laboratory or clinical settings, which may be limited by environmental factors or behavioral modification. Here, we present an ecologically valid framework for collecting mobility outcomes in everyday life by utilizing prosthesis-mounted wearable sensors. The custom sensor suite, consisting of five inertial measurement units, GPS, and environmental sensors, was worn by 14 individuals with unilateral transtibial amputation for approximately 4 weeks each. Across the monitoring period, 49,577 ± 30,468 (mean ± SD) strides were identified per participant (~10.2 sensor-hours per day). Strides were characterized according to walking bout duration, with most walking observed in relatively short walking bouts (<30 s) at slow walking speeds (~0.5 m/s). Turns were identified and characterized by magnitude, direction, strides, and time taken to complete. The percentage of prosthetic-inside turns was around 50% for less than 90° turns, but higher turn angles showed bias toward prosthetic-outside turns, on average. Most individual participants showed bias toward one direction or the other. Participants also averaged ~28.3 stair-steps per sensor-day. Stair-steps were biased toward upstairs (vs. downstairs) walking and toward step-over-step (vs. step-by-step) strategies. Collectively, these data provide a uniquely detailed evaluation of locomotion behaviors among persons with lower limb loss in everyday living. Future work could utilize the ecological framework described here for establishing functional benchmarks, assisting with device prescription, and otherwise guiding long-term care for optimizing mobility outcomes and quality of life after lower limb loss. 

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2. Ankle-Foot Prosthesis Force and Moment Estimation Using Wireless Strain Gauge Pyramid Adapter

   https://doi.org/10.1115/1.4071784  

   Neira-García, Jorge; Torres, Andres; Soliman, Ahmed; Arabian, Adam K; Rastgaar, Mo (October 2026, Journal of Medical Devices)

   Abstract Force and moment measurements are critical for powered ankle-foot prostheses, with over 73% of prototypes incorporating such sensors. Hardware complexity, compatibility limitations, and cost barriers currently limit widespread clinical adoption. This work presents four contributions using the commercial strain gauge based instrumented pyramid adapter Europa+: (1) hardware integration compatible with standard prosthetic components and minimal added bulk (275 g, 37.5 mm height); (2) an adaptive zero drift compensation algorithm achieving stable calibration within 2–3 steps for continuous long-term operation; (3) physics-informed linear regression models for real-time ankle force and moment estimation; and (4) experimental validation with 8 nonamputee participants using a passive prosthesis, and a preliminary single-participant evaluation with a two-degree-of-freedom (2DOF) powered ankle-foot prosthesis in variable impedance closed-loop operation. Results demonstrate exceptional axial force estimation (RMSE=47 ± 20 N, R2=0.97 ± 0.03) and strong dorsi-plantar (DP) moment estimation (RMSE=5.7 ± 2.3 Nm, R2=0.87 ± 0.06) across passive prosthesis participants, and these remain valid for the powered prosthesis. Inversion-eversion (IE) moment estimation achieves RMSE=0.8 ± 0.2 Nm and R2=0.44 ± 0.16 on passive data, but performance improves when model parameters are derived from powered prosthesis gait with larger active IE range. This approach establishes a generalizable methodology applicable to strain gauge based instrumented pyramid adapters, offering a practical alternative to custom sensors while significantly reducing barriers to clinical implementation of ankle dynamics estimation in prosthetic applications. 

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3. A Lightweight Robotic Lower-Limb Prosthesis With Unified Knee and Ankle Actuators

   https://doi.org/10.1115/1.4070781  

   Haque, Md Rejwanul; Hong, Woolim; Shetty, Vikas Vasanth; Dodson, Albert; Nalam, Varun; Huang, He Helen; Shen, Xiangrong (April 2026, Journal of Medical Devices)

   Abstract Recent advancements in robotic (powered) lower-limb prostheses have significantly improved joint function for amputees, enabling them to perform a range of energetically demanding daily activities that exceed the capabilities of passive prostheses. While robotic knee and ankle prostheses share the common goal of restoring joint function, they are typically treated as standalone devices with little commonality. This paper presents a unified design framework for both the prosthetic knee and ankle actuators, namely, Common Core Components Knee–Ankle Prosthesis (C3KAP). While enabling the use of a common set of core components and device structure, the C3KAP also incorporates sufficient flexibility in the transmission design for joint-specific customization. Through its lightweight design, the C3KAP prototype is only about 1.4 kg in weight but provides a peak torque of 80 N·m to support user locomotion. Through benchtop testing, the prototype has shown high back-drivability (back drive torque of 2.27 N·m) and large control bandwidth (20.7 Hz in 10 deg sinusoidal tracking). Human testing further validated the effectiveness and functionality of the C3KAP, demonstrated by the participant's natural walking gait, the prosthesis's well-coordinated movement with the sound leg, as well as the prosthesis motion's close match to the able-bodied (AB) biomechanics. 

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4. Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds

   https://doi.org/10.1186/s12984-022-01061-z  

   Zhang, Qiang; Fragnito, Natalie; Franz, Jason R.; Sharma, Nitin (December 2022, Journal of NeuroEngineering and Rehabilitation)

   Abstract Background Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. Objective The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. Methods Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. Results On average, the normalized moment prediction root mean square error was reduced by 14.58 % ( $$p=0.012$$ p = 0.012 ) and 36.79 % ( $$p<0.001$$ p < 0.001 ) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. Conclusions The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction. 

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5. Characterization of Hip Abduction Exoskeleton for Assistance During Gait Perturbations

   https://doi.org/10.1109/AIM55361.2024.10637061  

   Varma, Vaibhavsingh; Patel, Sujay N; Wilson, Nicholas P; Trkov, Mitja (July 2024, IEEE)

   Robotic lower limb exoskeletons have been shown to successfully provide joint torques to assist human subjects during walking. Assisting the wearer during gait perturbations to prevent falls still poses a challenge due to specific requirements of the device, and complex bipedal dynamics of recovery. In this study, we present a hip exoskeleton device with pneumatically actuated abduction/adduction motion to provide hip torque for assisting with lateral balance. The device was designed to be wearable, allow integration with previously developed wearable gait perturbation detection system and knee exoskeleton, and produce fast actuation to provide assistive joint torque during gait perturbations. We present the results of the experimental benchtop tests of the device. The maximum torque output and rate of torque development were characterized using a load cell. The maximum angular displacement, with added weights to simulate the leg inertia, was recorded using an inertial measurement unit sensor. Lastly, a preliminary test on a human subject demonstrated that the device, when exerting instantaneous hip abduction torque during swing walking gait, can effectively modify foot placement in the lateral direction. This work contributes towards developing exoskeleton control strategies for assistance during gait perturbations to prevent falls. 

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