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1. 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)

   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 routedmore »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.« less
2. Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

   https://doi.org/10.1186/s12984-023-01147-2  

   Luo, Shuzhen ; Androwis, Ghaith ; Adamovich, Sergei ; Nunez, Erick ; Su, Hao ; Zhou, Xianlian ( December 2023 , Journal of NeuroEngineering and Rehabilitation)

   Abstract Background Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and musclemore »coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. Results and conclusion A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.« less
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.
4. Functional Evaluation of a Personalized Orthosis for Knee Osteoarthritis: A Motion Capture Analysis

   https://doi.org/10.1115/1.4051626  

   Huber, Martin ; Eschbach, Matthew ; Kazerounian, Kazem ; Ilies, Horea ( December 2021 , Journal of Medical Devices)

   Abstract Orthotic treatments for knee osteoarthritis (OA) typically rely on simple mechanisms such as three-point bending straps and single-pin hinges. These commonly prescribed braces cannot treat bicompartmental knee OA, do not consider the muscle weakness that typically accompanies the condition, and employ hinges that restrict the knee's natural biomechanics. Utilizing a novel, personalized joint mechanism in conjunction with magnetorheological dampers, we have developed and evaluated a brace which attempts to address these shortcomings. This process has respected three principal design goals: reducing the load experienced across the entire knee joint, generating a supportive moment to aid the thigh muscles in shock absorption, and interfering minimally with gait kinematics. Two healthy volunteers were chosen to test the system's basic functionality through gait analysis in a motion capture laboratory. Combining the collected kinematic and force-plate data with data taken from sensors onboard the brace, we integrated the brace and leg system into a single inverse dynamics analysis, from which we were able to evaluate the effect of the brace design on the subjects' knee loads and moments. Of the three design goals: a reduction in knee contact forces was demonstrated; increased shock absorption was observed, but not to statistical significance; and naturalmore »gait was largely preserved. Taken in total, the outcome of this study supports additional investigation into the system's clinical effectiveness, and suggests that further refinement of the techniques presented in this paper could open the doors to more effective OA treatment through patient specific braces.« less
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 themore »need for a lab-constricted motion capture system.« less