@article {908, title = {Simulation of Stand-to-Sit Biomechanics for Robotic Exoskeletons and Prostheses with Energy Regeneration}, journal = {IEEE Transactions on Medical Robotics and Bionics}, volume = {3}, year = {2021}, month = {05/2021}, chapter = {455 - 462}, abstract = {

Previous studies of robotic exoskeletons and prostheses with regenerative actuators have focused on level-ground walking. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification. Joint mechanical power was calculated from joint torques and rotational velocities and numerically integrated over time to estimate the joint biomechanical energy. The hip absorbed the largest peak negative mechanical power (1.8 \± 0.5 W/kg), followed by the knee (0.8 \± 0.3 W/kg) and ankle (0.2 \± 0.1 W/kg). Negative mechanical work on the hip, knee, and ankle joints per stand-to-sit movement were 0.35 \± 0.06 J/kg, 0.15 \± 0.08 J/kg, and 0.02 \± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63\%), robotic exoskeletons and prostheses could regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should include seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration.

}, keywords = {Biomechanics, Exoskeletons, Prostheses, Rehabilitation Engineering, Robotics}, issn = {2576-3202}, doi = {DOI: 10.1109/TMRB.2021.3058323}, url = {https://ieeexplore.ieee.org/document/9351558}, author = {Brokoslaw Laschowski and Reza Sharif Razavian and John McPhee} } @inproceedings {893, title = {Comparative Analysis of Environment Recognition Systems for Control of Lower-Limb Exoskeletons and Prostheses}, booktitle = {IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)}, year = {2020}, month = {10/2020}, publisher = {IEEE}, organization = {IEEE}, address = {New York City, NY, USA}, abstract = {

Environment recognition systems can facilitate the predictive control of lower-limb exoskeletons and prostheses by recognizing the oncoming walking environment prior to physical interactions. While many environment recognition systems have been developed using different wearable technology and classification algorithms, their relative operational performances have not been evaluated. Motivated to determine the state-of-the-science and propose future directions for research innovation, we conducted an extensive comparative analysis of the wearable technology, training datasets, and classification algorithms used for vision-based environment recognition. The advantages and drawbacks of different wearable cameras and training datasets were reviewed. Environment recognition systems using pattern recognition, machine learning, and convolutional neural networks for image classification were compared. We evaluated the performances of different deep learning networks using a novel balanced metric called \“NetScore\”, which considers the image classification accuracy, and computational and memory storage requirements. Based on our analysis, future research in environment recognition systems for lower-limb exoskeletons and prostheses should consider developing 1) efficient deep convolutional neural networks for onboard classification, and 2) large-scale open-source datasets for training and comparing image classification algorithms from different researchers.\ 

}, keywords = {Artificial Intelligence, Biomechatronics, Computer Vision, Environment Recognition, Exoskeletons, Image Classification, Prostheses, Rehabilitation, Wearable Technology}, doi = {10.1109/BioRob49111.2020.9224364}, url = {https://ieeexplore.ieee.org/document/9224364}, author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee} } @article {863, title = {Lower-Limb Prostheses and Exoskeletons with Energy Regeneration: Mechatronic Design and Optimization Review}, journal = {ASME Journal of Mechanisms and Robotics}, volume = {11}, year = {2019}, abstract = {

Lower-limb biomechatronic devices (i.e., prostheses and exoskeletons) depend upon onboard batteries to power wearable sensors, actuators, and microprocessors, therein inherently limiting their operating durations. Regenerative braking, also termed electrical energy regeneration, represents a promising solution to the aforementioned shortcomings. Regenerative braking converts the otherwise dissipated mechanical energy during locomotion into electrical energy for recharging the onboard batteries, while simultaneously providing negative mechanical work for controlled system deceleration. This paper reviewed the electromechanical design and optimization of lower-limb biomechatronic devices with electrical energy regeneration. The technical review starts by examining human walking biomechanics (i.e., mechanical work, power, and torque about the hip, knee, and ankle joints) and proposes general design principles for regenerative braking prostheses and exoskeletons. Analogous to electric and hybrid electric vehicle powertrains, there are numerous mechatronic design components that could be optimized to maximize electrical energy regeneration, including the mechanical power transmission, electromagnetic machine, electrical drive, device mass and moment of inertia, and energy storage devices. Design optimization of these system components is individually discussed while referencing the latest advancements in robotics and automotive engineering. The technical review demonstrated that existing systems (1) are limited to level-ground walking applications and (2) have maximum energy regeneration efficiencies between 30\% and 37\%. Accordingly, potential future directions for research and innovation include (1) regenerative braking during dynamic movements like sitting down and slope and staircase descent and (2) utilizing high-torque-density electromagnetic machines and low-impedance mechanical power transmissions to maximize energy regeneration efficiencies.

}, keywords = {Assistive Devices, Biomechanics, Biomechatronics, Exoskeletons, Prostheses, Regenerative Braking, Rehabilitation Engineering, Rehabilitation Robotics}, doi = {10.1115/1.4043460}, url = {http://mechanismsrobotics.asmedigitalcollection.asme.org/article.aspx?articleid=2730917\&resultClick=1}, author = {Brokoslaw Laschowski and John McPhee and Jan Andrysek} } @inproceedings {874, title = {Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons}, booktitle = {IEEE International Conference on Rehabilitation Robotics (ICORR) }, year = {2019}, month = {07/2019}, publisher = {IEEE }, organization = {IEEE }, address = {Toronto, Canada}, abstract = {

Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85\% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.\ 

}, keywords = {Artificial Intelligence, Autonomous Control, Deep Learning, Exoskeleton, Machine Vision, Prostheses, Robotics}, doi = {10.1109/ICORR.2019.8779540}, url = {https://ieeexplore.ieee.org/document/8779540}, author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee} }