Behavior Analysis Based on Coordinates of Body Tags

Authors:
Mitja Luštrek;Boštjan Kaluža;Erik Dovgan;Bogdan Pogorelc;Matjaž Gams
Affiliations:
Dept. of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia 1000;Dept. of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia 1000;Dept. of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia 1000;Špica International d. o. o., Ljubljana, Slovenia 1231 and Dept. of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia 1000;Dept. of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia 1000 and Špica International d. o. o., Ljubljana, Slovenia 1231
Venue:
AmI '09 Proceedings of the European Conference on Ambient Intelligence
Year:
2009

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Abstract

This paper describes fall detection, activity recognition and the detection of anomalous gait in the Confidence project. The project aims to prolong the independence of the elderly by detecting falls and other types of behavior indicating a health problem. The behavior will be analyzed based on the coordinates of tags worn on the body. The coordinates will be detected with radio sensors. We describe two Confidence modules. The first one classifies the user's activity into one of six classes, including falling. The second one detects walking anomalies, such as limping, dizziness and hemiplegia. The walking analysis can automatically adapt to each person by using only the examples of normal walking of that person. Both modules employ machine learning: the paper focuses on the features they use and the effect of tag placement and sensor noise on the classification accuracy. Four tags were enough for activity recognition accuracy of over 93% at moderate sensor noise, while six were needed to detect walking anomalies with the accuracy of over 90%.