[2604.00175] Sit-to-Stand Transitions Detection and Duration Measurement Using Smart Lacelock Sensor

Computer Science > Machine Learning arXiv:2604.00175 (cs) [Submitted on 31 Mar 2026] Title:Sit-to-Stand Transitions Detection and Duration Measurement Using Smart Lacelock Sensor Authors:Md Rafi Islam, Md Rejwanul Haque, Elizabeth Choma, Shannon Hayes, Siobhan McMahon, Xiangrong Shen, Edward Sazonov View a PDF of the paper titled Sit-to-Stand Transitions Detection and Duration Measurement Using Smart Lacelock Sensor, by Md Rafi Islam and 5 other authors View PDF 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-fo...