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1. 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 ( August 2022 , Journal of NeuroEngineering and Rehabilitation)

   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.

   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.

   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.

   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.

   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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2. Design and clinical implementation of an open-source bionic leg

   https://doi.org/10.1038/s41551-020-00619-3  

   Azocar, Alejandro F. ; Mooney, Luke M. ; Duval, Jean-François ; Simon, Ann M. ; Hargrove, Levi J. ; Rouse, Elliott J. ( October 2020 , Nature Biomedical Engineering)

   In individuals with lower-limb amputations, robotic prostheses can increase walking speed, and reduce energy use, the incidence of falls and the development of secondary complications. However, safe and reliable prosthetic-limb control strategies for robust ambulation in real-world settings remain out of reach, partly because control strategies have been tested with different robotic hardware in constrained laboratory settings. Here, we report the design and clinical implementation of an integrated robotic knee–ankle prosthesis that facilitates the real-world testing of its biomechanics and control strategies. The bionic leg is open source, it includes software for low-level control and for communication with control systems, and its hardware design is customizable, enabling reduction in its mass and cost, improvement in its ease of use and independent operation of the knee and ankle joints. We characterized the electromechanical and thermal performance of the bionic leg in benchtop testing, as well as its kinematics and kinetics in three individuals during walking on level ground, ramps and stairs. The open-source integrated-hardware solution and benchmark data that we provide should help with research and clinical testing of knee–ankle prostheses in real-world environments.

    

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3. 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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4. Design and Preliminary Testing of an Instrumented Exoskeleton for Walking Gait Measurement

   Haque, M.R. ; Imtiaz, M.H. ; Chou, C. ; Sazonov, E. ; Shen, X. ( April 2019 , IEEE SoutheastCon)

   This paper presents the design and preliminary testing of an instrumented exoskeleton system, which is targeted at collecting gait data of the human locomotion to support the controller development of lower-limb wearable robots. This compact and lightweight device features a unique two-degree-of-freedom joint to minimize the interference to the user movement and a simple yet effective adjustment mechanism to fit subjects at different heights. For the gait measurement, the device incorporates embedded joint goniometers to obtain the knee and ankle positions, and inertial measurement units to obtain three-dimensional kinematic information. Force-sensing resistors are also incorporated into the shoe insole for plantar pressure measurement. Sensor signals are routed to an onboard microcontroller system for data storage and transfer, and the system is fully self-contained with onboard battery to facilitate data collection in various environments. A prototype of the exoskeleton was fabricated, and preliminary testing was conducted on two healthy subjects in various postures and modes of movement (walking, sitting, standing, stair climbing, etc.). The evaluation of a temporal event detection test showed no more than 5.5% mean variation in the measure of step counts by the sensory system and video annotation. These results indicate that the exoskeleton can provide an accurate measurement of gait information, using measurements taken from external video recordings as the benchmark in this preliminary validation study. 

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5. Adaptive load feedback robustly signals force dynamics in robotic model of Carausius morosus stepping

   https://doi.org/10.3389/fnbot.2023.1125171  

   Zyhowski, William P. ; Zill, Sasha N. ; Szczecinski, Nicholas S. ( January 2023 , Frontiers in Neurorobotics)

   Animals utilize a number of neuronal systems to produce locomotion. One type of sensory organ that contributes in insects is the campaniform sensillum (CS) that measures the load on their legs. Groups of the receptors are found on high stress regions of the leg exoskeleton and they have significant effects in adapting walking behavior. Recording from these sensors in freely moving animals is limited by technical constraints. To better understand the load feedback signaled by CS to the nervous system, we have constructed a dynamically scaled robotic model of the Carausius morosus stick insect middle leg. The leg steps on a treadmill and supports weight during stance to simulate body weight. Strain gauges were mounted in the same positions and orientations as four key CS groups (Groups 3, 4, 6B, and 6A). Continuous data from the strain gauges were processed through a previously published dynamic computational model of CS discharge. Our experiments suggest that under different stepping conditions (e.g., changing “body” weight, phasic load stimuli, slipping foot), the CS sensory discharge robustly signals increases in force, such as at the beginning of stance, and decreases in force, such as at the end of stance or when the foot slips. Such signals would be crucial for an insect or robot to maintain intra- and inter-leg coordination while walking over extreme terrain. 

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