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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.

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.

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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 non-rhythmic 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.

Alongside promising advances in biomechatronics, the following research presents the first documented investigation reviewing the electromechanical system designs of energetically-powered (i.e., robotic) prostheses for patients with transfemoral amputations. The technical review begins with examining the material and mechanical designs, and electrical batteries incorporated into robotic transfemoral prostheses. The actuation systems have encompassed electromagnetic actuators (i.e., occasionally featuring series elastic elements), pneumatic actuators (i.e., pneumatic cylinders and pneumatic artificial muscles), and hydraulic actuators. Various wearable sensors have been utilized to provide closed-loop feedback control, including electromechanical sensors, surface electromyography, and bioinspired machine vision systems. The Össur Power Knee (i.e., the only commercially-available powered transfemoral prosthesis) is additionally discussed. The technical review concludes with suggesting prospective future directions for innovation, specifically lower-limb prostheses capability of electrical energy regeneration.

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