Falls as anomalies? An experimental evaluation using smartphone accelerometer data

TL;DR

This paper evaluates fall detection methods using smartphone accelerometer data, demonstrating that fall detection as anomaly detection can be effective without relying on fall data for training.

Contribution

It compares traditional and anomaly detection approaches, like kNN and SVM, on real-world accelerometer data, highlighting the viability of anomaly-based fall detection.

Findings

Anomaly detection methods perform well in fall detection.

Fall data is not always necessary for effective detection.

Different data representations influence detection performance.

Abstract

Life expectancy keeps growing and, among elderly people, accidental falls occur frequently. A system able to promptly detect falls would help in reducing the injuries that a fall could cause. Such a system should meet the needs of the people to which is designed, so that it is actually used. In particular, the system should be minimally invasive and inexpensive. Thanks to the fact that most of the smartphones embed accelerometers and powerful processing unit, they are good candidates both as data acquisition devices and as platforms to host fall detection systems. For this reason, in the last years several fall detection methods have been experimented on smartphone accelerometer data. Most of them have been tuned with simulated falls because, to date, datasets of real-world falls are not available. This article evaluates the effectiveness of methods that detect falls as anomalies. To this end, we compared traditional approaches with anomaly detectors. In particular, we experienced the kNN and the SVM methods using both the one-class and two-classes configurations. The comparison involved three different collections of accelerometer data, and four different data representations. Empirical results demonstrated that, in most of the cases, falls are not required to design an effective fall detector.