Cycle metrics and strategy detection for automated chair sit-to-stand test analysis employing a single smartphone

Sher, A ORCID: https://orcid.org/0000-0002-0650-0335, Rashid, M, Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, Povina, F and Akanyeti, O, 2025. Cycle metrics and strategy detection for automated chair sit-to-stand test analysis employing a single smartphone. Annals of Biomedical Engineering. ISSN 0090-6964

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Abstract

Purpose: The 30-second Chair Sit-to-Stand Test (30 s CST) is widely used to assess lower-limb function and reflects complex motor coordination across neural systems. However, conventional scoring methods are often inconsistent and fail to capture variations in compensatory movement strategies or require invasive instrumentation. This study presents a smartphone-based system that automatically detects rising strategies across repeated CST cycles, providing an automated approach to extract cycle-level biomarkers of motor performance.

Methods: Thirty-five adults 10 younger, 20 older, and 5 with Parkinson’s disease performed supervised 30-s CST trials while wearing a waist-mounted smartphone that recorded accelerometer and gyroscope data at 400 Hz. Cycle detection used amplitude-adaptive thresholds and dominant-frequency intervals for robust segmentation of CST cycles. Rising strategies were classified with rule-based method that uses trunk pitch dynamics and cycle duration. Agreement with video annotations was assessed using Intraclass Correlation Coefficients (ICC (2, 1)), Bland–Altman analysis, and macro F1 scores.

Results: The algorithm detected 660 CST cycles with 99% accuracy, and the average mean absolute error across participants was under 40 ms. Bland–Altman analysis showed negligible bias (− 0.012 s) and narrow limits of agreement (− 0.134 to 0.110 s). Strategy classification achieved macro F1 = 0.94. Flexion cycles were consistently longer than Momentum Transfer cycles (e.g., older adults: 2.63 vs. 1.45 s).

Conclusion: Automated CST analysis reveals movement signatures not captured by standard timing, offering a richer characterization of mobility patterns. While these findings demonstrate technical feasibility and highlight clinically relevant variations, their application for diagnostic or personalized rehabilitation purposes remains preliminary and requires validation in larger cohorts.

Item Type:	Journal article
Publication Title:	Annals of Biomedical Engineering
Creators:	Sher, A., Rashid, M., Lotfi, A., Povina, F. and Akanyeti, O.
Publisher:	Springer
Date:	21 December 2025
ISSN:	0090-6964
Identifiers:	Number Type 10.1007/s10439-025-03943-4 DOI 2551879 Other
Rights:	This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	07 Jan 2026 11:30
Last Modified:	07 Jan 2026 11:30
URI:	https://irep.ntu.ac.uk/id/eprint/54950

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