Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 22; p. 6479
• Main Authors: Palmerini, Luca, Klenk, Jochen, Becker, Clemens, Chiari, Lorenzo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.11.2020; MDPI
• Subjects: accelerometer; Accelerometry; Accidental Falls; Activities of daily living; Algorithms; fall detection; Falls; Humans; Machine Learning; Monitoring, Ambulatory; Older people; Sensors; Signal processing; smartphone; Support Vector Machine; wearable; wearable; smartphone; machine learning; fall detection; accelerometer
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20226479

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.