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Related Experiment Video

Updated: Aug 22, 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

Incremental Learning of Human Activities in Smart Homes.

Sook-Ling Chua¹, Lee Kien Foo¹, Hans W Guesgen²

¹Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This study introduces a novel compression-based method for continuous human activity recognition. The system incrementally learns new behaviors while retaining prior knowledge and identifying novel, potentially risky activities.

Keywords:

activity recognition incremental learning novelty detection prediction by partial matching smart homes

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Sensor-based human activity recognition (HAR) is crucial for monitoring and understanding human behavior.
Current HAR systems often struggle with adapting to evolving behaviors and identifying novel activities.
Continuous learning and adaptation are essential for robust HAR systems.

Purpose of the Study:

To propose a compression-based method for incremental learning in human activity recognition.
To enable HAR systems to continuously adapt to new behaviors while retaining past knowledge.
To develop a system capable of detecting novel and potentially risky human behaviors.

Main Methods:

A novel compression-based approach for incremental learning was developed.
The method allows for the continuous assimilation of new behavioral data.
Prior knowledge is retained, and novel activities are identified.

Main Results:

The proposed method demonstrated effective incremental learning on three public smart home datasets.
The system successfully retained knowledge of previously learned activities.
The approach showed promise in highlighting novel behaviors.

Conclusions:

Compression-based incremental learning offers a viable solution for adaptive human activity recognition.
This method enhances the ability of HAR systems to handle evolving human behaviors.
The approach has significant implications for identifying emerging and potentially risky activities.