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Related Experiment Video

Updated: Jul 9, 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

The performance of a machine learning model in predicting accelerometer-derived walking speed.

Aleksej Logacjov¹, Tonje Pedersen Ludvigsen², Kerstin Bach¹

¹Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

|February 10, 2025

Summary

This study developed a machine learning classifier to accurately predict walking speeds using accelerometers. The model effectively distinguishes between slow, moderate, and brisk walking, offering a new tool for large-scale studies.

Keywords:

Epidemiology Physical activity Validity

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Last Updated: Jul 9, 2026

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

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Published on: December 11, 2015

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Published on: February 21, 2025

Area of Science:

Biomechanics
Machine Learning
Wearable Technology

Background:

Accurate long-term measurement of walking speed in large studies is difficult.
Walking speed is a key indicator of health and mobility.
Current methods for measuring walking speed are often impractical for large-scale, long-term studies.

Purpose of the Study:

To develop and evaluate a machine learning classifier for predicting walking speeds.
To assess the performance of the classifier using dual and single accelerometer setups.
To determine the accuracy of classifying slow, moderate, and brisk walking speeds.

Main Methods:

Trained an eXtreme Gradient Boosting (XGBoost) machine learning classifier.
Used data from 24 adults with tri-axial accelerometers on the thigh and low back.
Validated the classifier using leave-one-out cross-validation with 1, 3, and 5-second windows.

Main Results:

The machine learning classifier achieved high accuracy in predicting walking speeds (slow, moderate, brisk) and jogging.
Performance was comparable between dual and single accelerometer setups and across different window lengths.
Highest accuracy reached 91% with a dual accelerometer setup and a 5-second window.

Conclusions:

A machine learning classifier can accurately predict walking speeds using accelerometer data.
Both dual and single accelerometer setups are effective for this prediction.
This approach offers a feasible method for assessing walking speed in large-scale studies.