How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?
Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject's daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data).
Department of Intelligent Systems, Jožef Stefan International Postgraduate School, Jožef Stefan Institute, Ljubljana 1000, Slovenia. martin.gjoreski@ijs.si.
Martin
M
Gjoreski
Department of Intelligent Systems, Jožef Stefan International Postgraduate School, Jožef Stefan Institute, Ljubljana 1000, Slovenia. hristijan.gjoreski@ijs.si.
Hristijan
H
Gjoreski
Department of Intelligent Systems, Jožef Stefan International Postgraduate School, Jožef Stefan Institute, Ljubljana 1000, Slovenia. mitja.lustrek@ijs.si.
Mitja
M
Luštrek
Department of Intelligent Systems, Jožef Stefan International Postgraduate School, Jožef Stefan Institute, Ljubljana 1000, Slovenia. matjaz.gams@ijs.si.
Matjaž
M
Gams
D061725:Accelerometry / Q000295:instrumentation*; D000058:Accidental Falls / Q000517:prevention & control*; D000203:Activities of Daily Living; D000465:Algorithms; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D000076251:Wearable Electronic Devices; D014953:Wrist / Q000502:physiology
accelerometer; activity recognition; classification; fall detection; feature extraction; machine learning; wrist