Search

Walking in real-world environments involves constant decision-making, e.g., when approaching a staircase, an individual decides whether to engage (climbing the stairs) or avoid. For the control of assistive robots (e.g., robotic lower-limb prostheses), recognizing such motion intent is an important but challenging task, primarily due to the lack of available information. This paper presents a novel vision-based method to recognize an individual's motion intent when approaching a staircase before the potential transition of motion mode (walking to stair climbing) occurs. Leveraging the egocentric images from a head-mounted camera, the authors trained a YOLOv5 object detection model to detect staircases. Subsequently, an AdaBoost and gradient boost (GB) classifier was developed to recognize the individual's intention of engaging or avoiding the upcoming stairway. This novel method has been demonstrated to provide reliable (97.69%) recognition at least 2 steps before the potential mode transition, which is expected to provide ample time for the controller mode transition in an assistive robot in real-world use. [ABSTRACT FROM AUTHOR]

This paper introduces a novel wearable shoe sensor named the Smart Lacelock Sensor. The sensor can be securely attached to the top of a shoe with laces and incorporates a loadcell to measure the force applied by the shoelace, providing valuable information related to ankle movement and foot loading. As the first step towards the automated balance assessment, this paper investigates the correlations between various levels of physical performance measured by the wearable Smart Lacelock Sensor and the SPPB clinical method in community-living older persons. 19 adults (age 76.84 ± 3.45 years), including those with and without recent fall history and SPPB score ranging from 4 to 12, participated in the study. The Smart Lacelock Sensor was attached to both shoes of each participant by skilled research staff, who then led them through the SPPB evaluation. The data obtained from the Smart Lacelock Sensors after the SPPB assessment were used to evaluate the deviation between the SPPB scores assigned by the research staff and the signals generated by the sensors for various participants. Results demonstrate that the standard deviation of the Smart Lacelock Sensor's loadcell response (both feet) for the side-by-side balance testing is significantly correlated (R 2 = 0.68) with the SPPB score, demonstrating the capability of the Smart Lacelock Sensor in balance assessment.

Mobility impairment is becoming a challenging issue around the world with a rapid increase on aging population. Existing tools of walking assistance for mobility-impaired people include passive canes or wheeled rollators which increase energy consumption on the users and disturb the users' walking rhythm, and powered wheeled chairs which could preclude the muscle activities and accelerate the degeneration of the lower limbs. The research in this paper aiming at helping mobility-impaired people proposes a novel robotic platform with quadrupedal locomotion. With motorized actuation, the quadruped robotic platform could accompany the user at the center and provide protection and possible walking assistance if needed. As the robotic platform is equipped with a leg locomotion, it can enlarge the user's activity environments, such as both indoor flat floor and outdoor uneven terrain. It can even assist the user to involve in some mobility challenging activities, such as climbing stairs. In this paper, we illustrate the mechanical design of the robotic platform. A continuous gait planning is proposed to create a smooth locomotion for the robot. To quantify the performance, a system-level walking experimentation was conducted, and the results showed that quadruped robotic platform can maintain a statically stability which demonstrate the feasibility and capability of the robotic application for walking assistance.