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Related Experiment Video

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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Cycle Metrics and Strategy Detection for Automated Chair Sit-to-Stand Test Analysis Employing a Single Smartphone.

Arshad Sher1, Muntazir Rashid2, Ahmad Lotfi3

  • 1Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, Nottinghamshire, UK. arshad.sher@ntu.ac.uk.

Annals of Biomedical Engineering
|December 21, 2025
PubMed
Summary

A smartphone system accurately analyzes the 30-second Chair Sit-to-Stand Test (CST), detecting distinct movement strategies. This automated approach offers richer mobility data than traditional methods for assessing lower-limb function.

Keywords:
Chair sit-to-stand (CST)Functional mobilityParkinson’s diseaseRehabilitation engineeringRising strategy classificationWearable sensors

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Area of Science:

  • Biomechanics and Motor Control
  • Wearable Technology in Health
  • Digital Health and Rehabilitation

Background:

  • The 30-second Chair Sit-to-Stand Test (CST) is a standard measure of lower-limb function.
  • Conventional CST scoring lacks consistency and fails to capture compensatory movement strategies.
  • Existing methods often require invasive instrumentation for detailed analysis.

Purpose of the Study:

  • To develop and validate a smartphone-based system for automated detection of rising strategies during the CST.
  • To extract cycle-level biomarkers of motor performance from repeated CST trials.
  • To provide an objective and non-invasive method for assessing complex motor coordination.

Main Methods:

  • Thirty-five adults (younger, older, Parkinson's disease) performed the 30-s CST wearing a smartphone.
  • Smartphone accelerometer and gyroscope data were recorded at 400 Hz.
  • An algorithm segmented CST cycles and classified rising strategies (e.g., flexion vs. momentum transfer) using trunk pitch and cycle duration.

Main Results:

  • The algorithm achieved 99% accuracy in detecting 660 CST cycles with minimal error (<40 ms).
  • Strategy classification yielded a macro F1 score of 0.94.
  • Distinct differences in cycle duration were observed between strategies and participant groups (e.g., older adults' flexion vs. momentum transfer cycles).

Conclusions:

  • Automated CST analysis using smartphone data reveals detailed movement signatures beyond standard timing.
  • The system provides a richer characterization of mobility patterns and highlights clinically relevant variations.
  • Further validation in larger cohorts is needed to establish diagnostic and personalized rehabilitation applications.