  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.
