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Related Experiment Video

Updated: Dec 22, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Discovering Human Activities from Binary Data in Smart Homes.

Mohamed Eldib¹, Wilfried Philips¹, Hamid Aghajan¹

¹imec-TELIN-IPI, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium.

Sensors (Basel, Switzerland)

|May 6, 2020

Summary

This summary is machine-generated.

This study introduces an unsupervised method for activity discovery in smart homes using motion detector data. It helps monitor functional health by identifying daily routines without needing labeled data.

Keywords:

clustering frequent patterns health monitoring human activity discovery sequence mining smart homes unsupervised learning

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Area of Science:

Computer Science
Artificial Intelligence
Health Informatics

Background:

Advancements in sensing, data mining, and machine learning enable unobtrusive human health monitoring.
Acquiring sufficient labeled data for model training is a significant challenge in human activity recognition.
Activity discovery addresses the lack of labeled data using sequence mining and clustering.

Purpose of the Study:

To develop an unsupervised method for discovering activities from motion detector networks in smart homes.
To enable independent living assistance and functional health monitoring through routine analysis.
To overcome the limitations of labeled data dependency in human activity recognition.

Main Methods:

An intra-day clustering algorithm to identify frequent sequential patterns within a single day.
An inter-day clustering algorithm to detect common patterns across multiple days.
Pattern refinement for compressed and defined cluster characterizations.

Main Results:

The proposed method effectively discovers activities and routines from unlabeled sensor data.
The approach allows for tracking regular routines to monitor an individual's functional health and lifestyle patterns.
Evaluation on two real-life datasets demonstrated the method's applicability over extended periods.

Conclusions:

Unsupervised activity discovery is feasible and valuable for smart home health monitoring.
The developed clustering approach provides a robust way to analyze sensor data for routine identification.
This method supports unobtrusive health monitoring and aids individuals living independently.