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

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Published on: December 11, 2015

Improving activity recognition using temporal coherence.

Abbas Ataya, Pierre Jallon, Pascal Bianchi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|October 11, 2013

Summary

This study introduces a novel accelerometer-based system for recognizing daily physical activities. The hierarchical classifier with temporal coherence modeling significantly improves activity detection accuracy for multiple subjects and activities.

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Area of Science:

Biomedical Engineering
Pattern Recognition
Human Activity Recognition

Background:

Wearable sensors are increasingly used for assessing daily physical activity.
Accurate activity recognition is crucial for health monitoring and biomedical applications.

Purpose of the Study:

To develop an advanced accelerometer-based activity recognition scheme.
To enhance activity detection accuracy by incorporating temporal coherence.

Main Methods:

A hierarchical classifier distinguishing static and dynamic activities.
Feature extraction tailored to activity types.
A directed graph Markov chain to model inter-activity transitions.
Integration of confidence measures with temporal information.

Main Results:

The system accurately recognized 9 distinct activities.
Significant improvements in activity detection were achieved.
The approach demonstrated effectiveness across 48 subjects.

Conclusions:

The proposed hierarchical classifier with temporal coherence modeling offers a robust solution for human activity recognition.
This method enhances the reliability of physical activity assessment using wearable sensor data.