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Related Experiment Video

Updated: Sep 21, 2025

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

Carrying Position-Independent Ensemble Machine Learning Step-Counting Algorithm for Smartphones.

Zihan Song¹, Hye-Jin Park², Ngeemasara Thapa²

¹Department of Management Information Systems, Graduate School, Dong-A University, Busan 49315, Korea.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces a new smartphone pedometer algorithm that accurately counts steps regardless of carrying position. This machine learning approach enhances step-counting accuracy for mobile health applications.

Keywords:

acceleration signal processing machine learning pedometer smartphone step-count algorithm wearable position

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Last Updated: Sep 21, 2025

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Area of Science:

Wearable technology
Mobile health
Machine learning applications

Background:

Current step-counting methods using accelerometers or gyroscopes lack accuracy due to smartphones' variable placement.
Smartphone carrying position significantly impacts pedometer algorithm reliability.

Purpose of the Study:

To develop a carrying-position independent ensemble step-counting algorithm for unconstrained smartphones.
To improve the accuracy of step estimation across diverse smartphone carrying locations.

Main Methods:

Proposed an ensemble algorithm combining a classification model for carrying position identification and a regression model for step calculation.
Developed a data acquisition system to collect Force Sensitive Resistor (FSR) sensor step data and smartphone tri-axial acceleration data.
Utilized machine learning algorithms, including a random forest classifier and a regression model, trained on collected data.

Main Results:

The proposed ensemble algorithm achieved high accuracy, ranging from 98.1% to 98.8%, during self-paced walking.
Demonstrated superior performance compared to a commercial pedometer application in step-counting accuracy.
Validated the effectiveness of machine learning in accurately predicting step counts across various smartphone carrying positions.

Conclusions:

The developed ensemble algorithm offers a robust solution for accurate step counting irrespective of smartphone placement.
Machine learning-based ensemble methods provide a reliable and accurate approach for mobile health step tracking.