@article {919, title = {Environment Classification for Robotic Leg Prostheses and Exoskeletons using Deep Convolutional Neural Networks}, journal = {Frontiers in Neurorobotics}, volume = {15}, year = {2022}, month = {02/2022}, publisher = {bioRxiv}, abstract = {

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, the current locomotion mode recognition systems being developed for automated high-level control and decision-making rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, we developed an environment classification system powered by computer vision and deep learning to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. In this study, we first reviewed the development of our \“ExoNet\” database\—the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a hierarchical labeling architecture. We then trained and tested over a dozen state-of-the-art deep convolutional neural networks (CNNs) on the ExoNet database for image classification and automatic feature engineering, including: EfficientNetB0, InceptionV3, MobileNet, MobileNetV2, VGG16, VGG19, Xception, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet169, and DenseNet201. Finally, we quantitatively compared the benchmarked CNN architectures and their environment classification predictions using an operational metric called \“NetScore,\” which balances the image classification accuracy with the computational and memory storage requirements (i.e., important for onboard real-time inference with mobile computing devices). Our comparative analyses showed that the EfficientNetB0 network achieves the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore, which can inform the optimal architecture design or selection depending on the desired performance. Overall, this study provides a large-scale benchmark and reference for next-generation environment classification systems for robotic leg prostheses and exoskeletons.

}, keywords = {Artificial Intelligence, Biomechatronics, Computer Vision, Deep Learning, ExoNet, Exoskeletons, Prosthetics, Rehabilitation Robotics, wearables}, doi = {10.3389/fnbot.2021.730965}, url = {https://www.frontiersin.org/articles/10.3389/fnbot.2021.730965/full}, author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee} } @article {906, title = {ExoNet Database: Wearable Camera Images of Human Locomotion Environments}, journal = {Frontiers in Robotics and Artificial Intelligence}, volume = {7}, year = {2020}, month = {12/2020}, abstract = {

Advances in computer vision and deep learning are allowing researchers to develop environment recognition systems for robotic exoskeletons and prostheses. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed ExoNet - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million images of indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented shared platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged\ robots.\ 

}, keywords = {Artificial Intelligence, Biomechatronics, Computer Vision, Deep Learning, Environment Recognition, Exoskeletons, Prosthetics, Rehabilitation Robotics, Wearable Technology}, doi = {10.3389/frobt.2020.562061}, url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.562061/full}, 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} }