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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

Classification of sporting activities using smartphone accelerometers.

Edmond Mitchell¹, David Monaghan, Noel E O'Connor

¹Centre for Sensor Web Technologies, Dublin City University, Dublin, Ireland. edmond.mitchell3@mail.dcu.ie

Sensors (Basel, Switzerland)

|April 23, 2013

Summary

This study introduces a smartphone framework for automatic sports activity recognition using accelerometer data and Discrete Wavelet Transform (DWT). A fusion of classifiers achieved 87% accuracy, making sports analysis accessible to all athletes.

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Area of Science:

Sports Science
Biomedical Engineering
Machine Learning

Background:

Smartphones are ubiquitous, offering potential for accessible sports activity monitoring.
Accelerometer data quality varies, posing challenges for accurate activity recognition.
Existing classification methods lack a clear advantage in sports activity analysis.

Purpose of the Study:

To develop and evaluate a framework for automatic sports activity identification using smartphone accelerometers.
To investigate the effectiveness of the Discrete Wavelet Transform (DWT) for feature extraction.
To compare different classification approaches, including SVM, optimized models, and classifier fusion.

Main Methods:

Feature extraction from smartphone accelerometer data using the Discrete Wavelet Transform (DWT).
Training and evaluation of various classifiers: Support Vector Machine (SVM), optimized models, and classifier fusion.
Investigation of DWT parameter impacts (mother wavelets, window lengths, decomposition levels).
Creation of a sports activity dataset including soccer and field hockey.

Main Results:

A fusion of classifiers achieved an average maximum F-measure accuracy of 87%.
Classifier fusion outperformed single classifier models by 6% and standard SVM by 23%.
The framework demonstrated robust performance across different DWT parameter settings.

Conclusions:

The proposed framework enables accurate automatic sports activity recognition using readily available smartphones.
Classifier fusion is a highly effective strategy for improving sports activity classification accuracy.
This technology has the potential to benefit both elite and recreational athletes through accessible performance analysis.