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Related Experiment Video

Updated: May 12, 2026

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

Validity and Reliability of an Integrated Smartphone Measurement Approach for Balance.

Thomas A Prato¹, Robert C Lynall¹, David R Howell²

¹UGA Biomechanics Laboratory, Department of Kinesiology, University of Georgia, Athens, GA, USA.

Journal of Sport Rehabilitation

|November 18, 2024

Summary

Smartphone balance assessments are reliable and valid for tracking patient recovery. This objective method offers potential for remote monitoring and improved clinical insights into balance.

Keywords:

assessment clinical testing postural control

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Last Updated: May 12, 2026

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

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Biomechanics
Digital Health
Human Movement Analysis

Background:

Clinical balance assessments often lack reliability due to subjective scoring.
Objective, accelerometer-based smartphone evaluations present a promising alternative for patients, clinicians, and researchers.

Purpose of the Study:

To evaluate the validity and reliability of standing balance measurements using smartphone technology.

Main Methods:

A repeated-measures study involved 23 healthy adults across two sessions.
Participants performed static balance tasks (tandem stance, single leg) with eyes open and closed.
Smartphone accelerometers and motion capture system data were used to calculate sway path, range, and area.

Main Results:

Smartphone assessments showed good to excellent reliability for eyes-open conditions (ICC = .82–.98).
Reliability for eyes-closed conditions varied from poor to good (ICC = .31–.87).
Correlations between smartphone and marker-based data were moderate to very high (r = .56–.97).

Conclusions:

Smartphone-based balance assessment is a valid and reliable method.
This technology enables remote administration and enhanced patient tracking during recovery.
Clinicians and researchers can adopt this approach for objective balance measurement.