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Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Smartphone Instrumented 30-Second Chair Stand Recovers Body Composition and Strength Biomarkers.

Colin Barry¹, Edward Wang¹, David Wing^2,3

¹Electrical and Computer Engineering Department University of California San Diego, La Jolla, CA, USA.

Research Square

|July 10, 2026

Summary

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A smartphone can analyze body composition and strength during the 30-second chair stand test. This technology captures movement data beyond simple repetition counts, offering valuable insights for geriatric assessment.

Area of Science:

Geriatric Medicine
Biomedical Engineering
Kinesiology

Background:

The 30-second chair stand test is a common geriatric assessment tool.
Current practice primarily uses repetition count, ignoring valuable movement data.

Purpose of the Study:

To investigate smartphone inertial measurement unit (IMU) data for assessing body composition and strength.
To determine if IMU data can predict clinical metrics beyond simple repetition counts.

Main Methods:

78 adults performed the 30-second chair stand test with a smartphone at shoulder level.
Movement features from the smartphone IMU signal were extracted.
Simple regression models predicted grip strength, body fat, and lean mass using IMU data, with and without height and weight.

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Related Experiment Videos

Last Updated: Jul 12, 2026

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Published on: March 14, 2017

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Main Results:

Smartphone IMU features accurately predicted dominant grip strength (R=0.83), percent body fat (R=0.62), and total lean mass (R=0.92).
IMU-only features significantly outperformed repetition count alone for all predicted outcomes (p ≤ 0.02).

Conclusions:

Smartphone IMU analysis during the chair stand test offers a novel, data-rich approach to geriatric assessment.
This method provides valuable insights into body composition and strength, surpassing traditional repetition counting.