Gait Phase Subdivision and Leg Stiffness Estimation during Stair Climbing

Leg stiffness is considered a prevalent parameter used in data analysis of leg locomotion during different gaits, such as walking, running, and hopping. Quantification of the change in support leg stiffness during stair ascent and descent will enhance our understanding of complex stair climbing gait dynamics. The purpose of this study is to investigate a methodology to estimate leg stiffness during stair climbing and subdivide the stair climbing gait cycle. Leg stiffness was determined as the ratio of changes in ground reaction force in the direction of the support leg Fl (leg force) to the respective changes in length Ll during the entire stance phase. Eight subjects ascended and descended an instrumented staircase at different cadences. In this study, the changes of leg force and length (force-length curve) are described as the leg stiffness curve, the slope of which represents the normalized stiffness during stair climbing. The stair ascent and descent gait cycles were subdivided based on the negative and positive work fluctuations of the center-of-mass (CoM) work rate curve and the characteristics of leg stiffness. We found that the leg stiffness curve consists of several segments in which the force-length relationship was similarly linear and the stiffness value was relatively constant; the phase divided by the leg stiffness curve corresponds to the phase divided by the CoM work rate curve. The results of this study may guide biomimetic control strategies for a wearable lower-extremity robot for the entire stance phase during stair climbing.

Exoskeleton robots for lower limb assistance: A review of materials, actuation, and manufacturing methods

The field of robot-assisted physical rehabilitation and robotics technology for providing support to the elderly population is rapidly evolving. Lower limb robot aided rehabilitation and assistive technology have been a focus for the engineering community during the last three decades as several robotic lower limb exoskeletons have been proposed in the literature as well as some being commercially available. Numerous manufacturing techniques and materials have been developed for lower limb exoskeletons during the last two decades, resulting in the design of a variety of robot exoskeletons for gait assistance for elderly and disabled people. One of the most important aspects of developing exoskeletons is the selection of the most appropriate proper material. The material selection strongly influences the overall weight and performance of the exoskeleton robot. The most suitable fabrication method for material is also an important parameter for the development of lower limb robot exoskeletons. In addition to the materials and manufacturing methods, the actuation method plays a vital role in the development of these robot exoskeletons. Even though various materials, manufacturing methods and actuators are reported in the literature for these lower limb robot exoskeletons, there are still avenues of improvement in these three domains. In this review, we have examined various lower limb robotic exoskeletons, concentrating on the three main aspects of material, manufacturing, and actuation. We have focused on the advantages and drawbacks of various materials and manufacturing practices as well as actuation methods. A discussion on future directions of research is provided for the engineering community covering the material, manufacturing and actuation methods.

Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons

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.

An adaptive stair-ascending gait generation approach based on depth camera for lower limb exoskeleton

The mobility on stairways is a daily challenge for seniors and people with dyskinesia. Lower limb exoskeletons can be effective assistants to improve their life quality. In this paper, we present an adaptive stair-ascending gait generation algorithm based on a depth camera for lower limb exoskeletons. We first construct a linked-list-based stairway model with the point cloud captured from the depth camera. Then, an optimal foothold point is calculated based on the linked-list stair model for gait generation. Finally, the exoskeleton takes the stair-ascending gait of healthy people as a reference and generates appropriate gait for the stair. The proposed gait generation algorithm is initially validated through holistic simulation analyses. We tested the stairway modeling algorithm on varieties of indoor and outdoor stairways and evaluated the gait generation algorithm on stairs of different height. The subjects' stair walking tests with lower limb exoskeletons show the effectiveness of the proposed stairway modeling and gait generation approaches.