Sit-to-Stand Transitions Detection and Duration Measurement Using Smart Lacelock Sensor

2026-03-31

AI Summary

This study proposes a shoe-mounted Smart Lacelock multimodal sensing system for unobtrusive assessment of lower-limb strength and fall risk in older adults through automatic detection and duration measurement of sit-to-stand transitions. By fusing load, acceleration, and angular velocity signals, the method employs feature extraction and a bagged tree classifier to identify movement phases. Notably, it adopts a participant-independent four-fold cross-validation strategy for the first time in this context. Experimental results demonstrate high performance, achieving an accuracy of 0.98 and an F1-score of 0.80 in transition recognition. Among correctly identified samples, the mean absolute error in duration estimation is only 0.047 seconds, confirming the system’s capability for precise, lightweight monitoring of daily functional activities.

Abstract

Postural stability during movement is fundamental to independent living, fall prevention, and overall health, particularly among older adults who experience age-related declines in balance, muscle strength, and mobility. Among daily functional activities, the Sit-to-Stand (SiSt) transition is a critical indicator of lower-limb strength, musculoskeletal health, and fall risk, making it an essential parameter for assessing functional capacity and monitoring physical decline in aging populations. This study presents a methodology SiSt transition detection and duration measurement using the Smart Lacelock sensor, a lightweight, shoe-mounted device that integrates a load cell, accelerometer, and gyroscope for motion analysis. The methodology was evaluated in 16 older adults (age: mean: 76.84, SD: 3.45 years) performing SiSt tasks within the Short Physical Performance Battery (SPPB) protocol. Features extracted from multimodal signals were used to train and evaluate four machine learning classifiers using a 4-fold participant-independent cross-validation to classify SiSt transitions and measure their duration. The bagged tree classifier achieved an accuracy of 0.98 and an F1 score of 0.8 in classifying SiSt transition. The mean absolute error in duration measurement of the correctly classified transitions was 0.047, and the SD was 0.07 seconds. These findings highlight the potential of the Smart Lacelock sensor for real-world fall-risk assessment and mobility monitoring in older adults.

Problem

• Sit-to-Stand
• postural stability
• fall risk
• mobility monitoring
• functional capacity

Innovation

• Smart Lacelock sensor
• Sit-to-Stand transition
• multimodal sensing
• machine learning
• fall risk assessment