@article {970, title = {Energy-Efficient Actuator Design Principles for Robotic Leg Prostheses and Exoskeletons: A Review of Series Elasticity and Backdrivability}, journal = {ASME Journal of Computational and Nonlinear Dynamics}, year = {2023}, abstract = {

Robotic leg prostheses and exoskeletons have traditionally been designed using highly-geared motor-transmission systems that minimally exploit the passive dynamics of human locomotion, resulting in inefficient actuators that require significant energy consumption and thus provide limited battery-powered operation or require large onboard batteries. Here we review two of the leading energy-efficient actuator design principles for legged and wearable robotic systems: series elasticity and backdrivability. As shown by inverse dynamic simulations of walking, there are periods of negative joint mechanical work that can be used to increase efficiency by recycling some of the otherwise dissipated energy using series elastic actuators and/or backdriveable actuators with energy regeneration. Series elastic actuators can improve shock tolerance during foot-ground impacts and reduce the peak power and energy consumption of the electric motor via mechanical energy storage and return. However, actuators with series elasticity tend to have lower output torque, increased mass and architecture complexity due to the added physical spring, and limited force and torque control bandwidth. High torque density motors with low-ratio transmissions, known as quasi-direct drives, can likewise achieve low output impedance and high backdrivability, allowing for safe and compliant human-robot physical interactions, in addition to energy regeneration. However, torque-dense motors tend to have higher Joule heating losses, greater motor mass and inertia, and require specialized motor drivers for real-time control. While each actuator design has advantages and drawbacks, designers should consider the energy-efficiency of robotic leg prostheses and exoskeletons beyond steady-state level-ground walking.

}, keywords = {Actuators, Energy-Efficiency, Exoskeletons, Legged Locomotion, Prosthetics, Wearable Robotics}, doi = {10.1115/1.4056919}, url = {https://asmedigitalcollection.asme.org/computationalnonlinear/article-abstract/doi/10.1115/1.4056919/1159612/Energy-Efficient-Actuator-Design-Principles-For?redirectedFrom=fulltext}, author = {Brokoslaw Laschowski and John McPhee} } @article {1001, title = {Evaluation of a machine-learning-driven active-passive upper-limb exoskeleton robot: Experimental human-in-the-loop study}, journal = {Wearable Technologies}, volume = {4}, year = {2023}, month = {05/2023}, chapter = {e13}, keywords = {Active-passive, Assistive control, Exoskeletons, human-in-the-loop control, Wearable robots}, doi = {https://doi.org/10.1017/wtc.2023.9}, author = {Ali Nasr and Jason Hunter and Clark R. Dickerson and John McPhee} } @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 {961, title = {Optimal design of active-passive shoulder exoskeletons: a computational modeling of human-robot interaction}, journal = {Multibody System Dynamics}, year = {2022}, month = {11/2022}, keywords = {Active-passive, Assistive control, Closed kinematic chain, Exoskeletons, Human-robot interaction, Wearable robots}, doi = {https://doi.org/10.1007/s11044-022-09855-8}, author = {Ali Nasr and Sydney Bell and John McPhee} } @inproceedings {959, title = {Simulation of Energy Regeneration in Human Locomotion for Efficient Exoskeleton Actuation}, booktitle = {IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)}, year = {2022}, month = {11/2022}, publisher = {IEEE}, organization = {IEEE}, abstract = {

Backdriveable actuators with energy regeneration can improve the efficiency and extend the battery-powered operating times of robotic lower-limb exoskeletons by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, previous related studies have focused on steady-state level-ground walking. To better encompass real-world community mobility, here we developed a feedforward human-exoskeleton energy regeneration system model to simulate energy regeneration and storage during other daily locomotor activities. Data from inverse dynamics analyses of 10 healthy young adults walking at variable speeds and slopes were used to calculate the negative joint mechanical power and work (i.e., the mechanical energy theoretically available for electrical energy regeneration). These human joint mechanical energetics were then used to simulate backdriving a robotic exoskeleton and regenerating energy. An empirical characterization of the exoskeleton device was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration. Our performance calculations showed that regenerating energy at slower walking speeds and decline slopes could significantly extend the battery-powered operating times of robotic lower-limb exoskeletons (i.e., up to 99\% increase in total number of steps), therein improving locomotor efficiency.

}, keywords = {Actuators, Biomechatronics, Energy-Efficiency, Exoskeletons}, doi = {10.1109/BioRob52689.2022.9925349}, url = {https://ieeexplore.ieee.org/document/9925349}, author = {Brokoslaw Laschowski and Keaton A. Inkol and Alex Mihailidis and John McPhee} } @inproceedings {915, title = {Computer Vision and Deep Learning for Environment-Adaptive Control of Robotic Lower-Limb Exoskeletons}, booktitle = {Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, year = {2021}, month = {04/2021}, publisher = {IEEE }, organization = {IEEE }, abstract = {

Robotic exoskeletons require human control and decision making to switch between different locomotion modes, which can be inconvenient and cognitively demanding. To support the development of automated locomotion mode recognition systems (i.e., high-level controllers), we designed an environment recognition system using computer vision and deep learning. We collected over 5.6 million images of indoor and outdoor real-world walking environments using a wearable camera system, of which ~923,000 images were annotated using a 12-class hierarchical labelling architecture (called the ExoNet database). We then trained and tested the EfficientNetB0 convolutional neural network, designed for efficiency using neural architecture search, to predict the different walking environments. Our environment recognition system achieved ~73\% image classification accuracy. While these preliminary results benchmark EfficientNetB0 on the ExoNet database, further research is needed to compare different image classification algorithms to develop an accurate and real-time environment-adaptive locomotion mode recognition system for robotic exoskeleton control.

}, keywords = {Biomechatronics, Computer Vision, Deep Learning, Exoskeletons, Rehabilitation, Robotics}, doi = {10.1109/EMBC46164.2021.9630064}, url = {https://ieeexplore.ieee.org/document/9630064}, author = {Brokoslaw Laschowski and William McNally and Alexander Wong and John McPhee} } @inproceedings {921, title = {Model-based design and optimization of passive shoulder exoskeletons}, booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, year = {2021}, publisher = {ASME}, organization = {ASME}, abstract = {

To physically assist workers in reducing musculoskeletal strain or to develop motor skills for patients with neuromuscular disabilities, recent research has focused on Exoskeletons (Exos). Designing active Exos is challenging due to the complex human geometric structure, the human-Exoskeleton wrench interaction, the kinematic constraints, and the selection of power source characteristics. Because of the portable advantages of passive Exos, designing a passive shoulder mechanism has been studied here. The study concentrates on modeling a 3D multibody upper-limb human-Exoskeleton, developing a procedure of analyzing optimal assistive torque profiles, and optimizing the passive mechanism features for desired tasks. The optimization objective is minimizing the human joint torques. For simulating the complex closed-loop multibody dynamics, differential-algebraic equations (DAE)s of motion have been generated and solved. Three different tasks have been considered, which are common in industrial environments: object manipulation, over-head work, and static pointing. The resulting assistive Exoskeleton\’s elevation joint torque profile could decrease the specific task\’s human shoulder torque. Since the passive mechanism produces a specific torque for a given elevation angle, the Exoskeleton is not versatile or optimal for different dynamic tasks. We concluded that designing a passive Exoskeleton for a wide range of dynamic applications is impossible. We hypothesize that augmenting an actuator to the mechanism can provide the necessary adjustment torque and versatility for multiple tasks.

}, keywords = {Exoskeletons, Multibody System Dynamics, Optimization, Wearable Robotics}, author = {Ali Nasr and Spencer Ferguson and John McPhee} } @inproceedings {918, title = {Myoelectric control of robotic leg prostheses and exoskeletons: A review}, booktitle = {ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference}, year = {2021}, publisher = {ASME}, organization = {ASME}, abstract = {

Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and nonrhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

}, keywords = {Exoskeletons, Machine Learning, Neural Control, Neural Interfaces, Prosthetics, Wearable Robotics}, author = {Ali Nasr and Brokoslaw Laschowski and John McPhee} } @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 {905, title = {Assessing Control of Fixed-Support Balance Recovery in Wearable Lower-Limb Exoskeletons Using Multibody Dynamic Modelling}, booktitle = {International Conference on Biomedical Robotics and Biomechatronics (BIOROB)}, year = {2020}, abstract = {

Despite many lower-limb exoskeletons requiring the use of crutches to maintain upright postures, limited research has assessed control of standing balance recovery in these systems. Using a model-based approach, the current simulation study investigated the performance of impedance controllers designed to assist with standing fixed-support balance recovery. A novel multibody dynamic model of the integrated humanexoskeleton system was designed to move in the sagittal plane. Development of the exoskeleton model (Technaid Exo-H3) was accompanied by parameter identification. The balancing torques produced by the human in the model were derived from offline linear control methods and saturated to approximate the torque-production of a young individual either with or without incomplete spinal cord injury. Without intervention, the injured user was not able to recover upright posture following a forward push of specific magnitude. Thus, three feedback control laws, inspired by robotics research (exoskeletons and humanoids), were implemented in the simulated exoskeleton. Each law assisted with balance recovery via reference tracking within the joint and/or whole-body center of mass space. Following optimization of control parameters, all proposed exoskeleton control laws were successful in assisting the injured user return to an upright posture. Joint space control yielded the best jointlevel reference tracking during recovery, while center of mass control better reduced forward center of mass excursions \— albeit at the cost of joint-level tracking accuracy.

}, keywords = {Balance, Biomechanics, Biomechatronics, Control-Oriented Model, Exoskeletons}, author = {Keaton A. Inkol 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 {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 {883, title = {Integration of Machine Learning with Dynamics and Control: From Autonomous Cars to Biomechatronics}, number = {7-8}, year = {2019}, chapter = {Fall 2019}, abstract = {

Researchers at the University of Waterloo are strategically combining artificial intelligence (AI) methods with dynamics and model-based control approaches, to solve real-world problems that are too difficult or computationally intensive to tackle with either AI or conventional methods on their own. Our goal is to combine the best features of machine learning and model-based control theories, in contrast to computer scientists that are seeking purely AI solutions to dynamics and control problems. This article presents our recent work towards this goal, with an emphasis on two very active research applications: Autonomous Cars and Biomechatronics.

}, keywords = {Artificial Intelligence, Autonomous Cars, Biomechatronics, Deep Learning, Exoskeletons, Robotics}, url = {http://csme-scgm.ca/sites/default/files/BULLETIN-FALL-2019.pdf}, author = {Arash Hashemi and Yuan Lin and William McNally and Brokoslaw Laschowski and Bryce Hosking 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} }