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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.