Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control.

Robotic lower-limb prostheses, with their actively powered joints, may significantly improve amputee users' mobility and enable them to obtain healthy-like gait in various modes of locomotion in daily life. However, timely recognition of the amputee users' locomotive mode and mode transition still remains a major challenge in robotic lower-limb prosthesis control. In the paper, the authors present a new multi-dimensional dynamic time warping (mDTW)-based intent recognizer to provide high-accuracy recognition of the locomotion mode/mode transition sufficiently early in the swing phase, such that the prosthesis' joint-level motion controller can operate in the correct locomotive mode and assist the user to complete the desired (and often power-demanding) motion in the stance phase. To support the intent recognizer development, the authors conducted a multi-modal gait data collection study to obtain the related sensor signal data in various modes of locomotion. The collected data were then segmented into individual cycles, generating the templates used in the mDTW classifier. Considering the large number of sensor signals available, we conducted feature selection to identify the most useful sensor signals as the input to the mDTW classifier. We also augmented the standard mDTW algorithm with a voting mechanism to make full use of the data generated from the multiple subjects. To validate the proposed intent recognizer, we characterized its performance using the data cumulated at different percentages of progression into the gait cycle (starting from the beginning of the swing phase). It was shown that the mDTW classifier was able to recognize three locomotive mode/mode transitions (walking, walking to stair climbing, and walking to stair descending) with 99.08% accuracy at 30% progression into the gait cycle, well before the stance phase starts. With its high performance, low computational load, and easy personalization (through individual template generation), the proposed mDTW intent recognizer may become a highly useful building block of a prosthesis control system to facilitate the robotic prostheses' real-world use among lower-limb amputees.

Smart Lacelock Sensor for the Balance Assessment of Community-Dwelling Older People.

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 (R2 = 0.68) with the SPPB score, demonstrating the capability of the Smart Lacelock Sensor in balance assessment.

Vision-Based Recognition of Human Motion Intent during Staircase Approaching.

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.