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Related Experiment Video

Updated: Jun 26, 2026

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

Learning under uncertainty in smart home environments.

Shuai Zhang¹, Sally McClean, Bryan Scotney

¹School of Computing and Information Engineering, University of Ulster at Coleraine, Northern Ireland, BT52 1SA, UK. zhang-s1@ulster.ac.uk

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

|January 24, 2009

Summary

This summary is machine-generated.

This study introduces a smart home system that learns elderly individuals' daily patterns from sensor data, even with missing information. This enables accurate activity recognition and prediction for enhanced independence.

Related Experiment Videos

Last Updated: Jun 26, 2026

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

Area of Science:

Computer Science
Artificial Intelligence
Gerontology

Background:

Smart home technologies support independent living for the elderly ('ageing in place').
Activity recognition from sensor data is key, but faces challenges with incomplete or unreliable information.

Purpose of the Study:

To develop a method for learning and predicting inhabitant activities in smart homes despite incomplete sensor data.
To assess the impact of different sensors on activity recognition accuracy.

Main Methods:

Utilized machine learning algorithms to handle missing data in sensor event streams.
Developed an approach for identifying and predicting inhabitant activities under uncertainty.
Implemented a system to evaluate sensor contribution to activity recognition.

Main Results:

The proposed approach effectively learns inhabitant patterns and predicts activities even with incomplete data.
The system can quantify the influence of individual sensors on the accuracy of activity recognition.
Evaluation demonstrated the robustness of the method in handling data uncertainty.

Conclusions:

The developed method enables reliable activity recognition and prediction in smart home environments with imperfect data.
This work facilitates the creation of proactive intervention mechanisms for elderly care.
Assessing sensor impact allows for optimization of smart home systems for 'ageing in place'.