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Design and Analysis for Fall Detection System Simplification

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Indoor localisation through object detection within multiple environments utilising a single wearable camera.

Colin Shewell¹, Chris Nugent¹, Mark Donnelly¹

¹Ulster University, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB UK.

Health and Technology

|March 28, 2017

Summary

This summary is machine-generated.

This study uses Google Glass and machine vision for occupant localization in Ambient Assisted Living, achieving high accuracy without requiring sensor interaction. The method offers a novel, first-person perspective for tracking individuals within their environment.

Keywords:

Ageing in place Ambient assisted living Context-aware services Machine vision Wearable computing

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Area of Science:

Computer Science
Artificial Intelligence
Ubiquitous Computing

Background:

The growing wearable sensor market presents opportunities for Ambient Assisted Living (AAL).
Existing AAL systems often require extensive sensor deployment.
A first-person perspective offers unique occupant monitoring capabilities.

Purpose of the Study:

To develop and validate a novel occupant localization method using wearable technology and machine vision.
To assess the performance and practical viability of the system in diverse environments.
To provide a non-intrusive method for tracking individuals in AAL settings.

Main Methods:

Utilizing Google Glass for a first-person environmental view.
Employing machine vision techniques for object detection and recognition.
Implementing the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features (ORB) algorithm with a K-Nearest Neighbour (KNN) matcher.
Validating the approach through experiments involving daily living activities and benchmarking against dense sensor placement.

Main Results:

The proposed method achieved high performance metrics: recall of 0.82, precision of 0.96, and F-measure of 0.88.
Demonstrated effective occupant localization through environmental object detection.
Showcased benefits including first-person tracking and no required sensor interaction.

Conclusions:

The developed approach is a viable and effective method for occupant localization in AAL.
The system offers a practical, non-intrusive solution for monitoring individuals.
Further assessment of performance and practical installation in different environments is warranted.