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1. 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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2. A Lightweight Exoskeleton-Based Portable Gait Data Collection System

   https://doi.org/10.3390/s21030781  

   Haque, Md Rejwanul; Imtiaz, Masudul H.; Kwak, Samuel T.; Sazonov, Edward; Chang, Young-Hui; Shen, Xiangrong (February 2021, Sensors)

   null (Ed.)

   For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system. 

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3. Design of a customizable, modular pediatric exoskeleton for rehabilitation and mobility

   https://doi.org/10.1109/SMC.2019.8914629  

   Eguren, David; Cestari, Manuel; Luu, Trieu Phat; Kilicarslan, Atilla; Steele, Alexander; Contreras-Vidal, Jose L. (October 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC))

   Powered exoskeletons for gait rehabilitation and mobility assistance are currently available for the adult population and hold great promise for children with mobility limiting conditions. Described here is the development and key features of a modular, lightweight and customizable powered exoskeleton for assist-as-needed overground walking and gait rehabilitation. The pediatric lower-extremity gait system (PLEGS) exoskeleton contains bilaterally active hip, knee and ankle joints and assist-as-needed shared control for young children with lower-limb disabilities such as those present in the Cerebral Palsy, Spina Bifida and Spinal Cord Injured populations. The system is comprised of six joint control modules, one at each hip, knee and ankle joint. The joint control module, features an actuator and motor driver, microcontroller, torque sensor to enable assist-as-needed control, inertial measurement unit and system monitoring sensors. Bench-testing results for the proposed joint control module are also presented and discussed. 

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4. Automated gait event detection for exoskeleton-assisted walking using a long short-term memory model with ground reaction force and heel marker data

   https://doi.org/10.1371/journal.pone.0315186  

   Chen, Xiaowen; Martin, Anne E (February 2025, PLOS ONE)

   Gu, Yaodong (Ed.)

   Traditional gait event detection methods for heel strike and toe-off utilize thresholding with ground reaction force (GRF) or kinematic data, while recent methods tend to use neural networks. However, when subjects’ walking behaviors are significantly altered by an assistive walking device, these detection methods tend to fail. Therefore, this paper introduces a new long short-term memory (LSTM)-based model for detecting gait events in subjects walking with a pair of custom ankle exoskeletons. This new model was developed by multiplying the weighted output of two LSTM models, one with GRF data as the input and one with heel marker height as input. The gait events were found using peak detection on the final model output. Compared to other machine learning algorithms, which use roughly 8:1 training-to-testing data ratio, this new model required only a 1:79 training-to-testing data ratio. The algorithm successfully detected over 98% of events within 16ms of manually identified events, which is greater than the 65% to 98% detection rate of previous LSTM algorithms. The high robustness and low training requirements of the model makes it an excellent tool for automated gait event detection for both exoskeleton-assisted and unassisted walking of healthy human subjects. 

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5. Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable

   https://doi.org/10.1017/wtc.2023.3  

   Davarzani, Samaneh; Saucier, David; Talegaonkar, Purva; Parker, Erin; Turner, Alana; Middleton, Carver; Carroll, Will; Ball, John E.; Gurbuz, Ali; Chander, Harish; et al (January 2023, Wearable Technologies)

   Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system. 

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