title: Acoustic fall detection using one-class classifiers.
abstract: Falling represents a major health concern for the elderly. To address this concern we proposed in a previous paper an acoustic fall detection system, FADE, composed of a microphone array and a motion detector. FADE may help the elderly living alone by alerting a caregiver as soon as a fall is detected. A crucial component of FADE is the classification software that labels an event as a fall or part of the daily routine based on its sound signature. A major challenge in the design of the classifier is that it is almost impossible to obtain realistic fall sound signatures for training purposes. To address this problem we investigate a type of classifier, one-class classifier, that requires only examples from one class (i.e., non-fall sounds) for training. In our experiments we used three one-class (OC) classifiers: nearest neighbor (OCNN), SVM (OCSVM) and Gaussian mixture (OCGM). We compared the results of OC to the regular (two-class) classifiers on two datasets.
authors: Health Management and Informatics Department, University of Missouri, Columbia, MO 65211, USA. popescum@missouri.edu
authors: Mihail
authors: M
authors: Popescu
authors: Abhishek
authors: A
authors: Mahnot
mesh_terms: D000058:Accidental Falls / Q000517:prevention & control*; D000162:Acoustics; D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D017143:Fuzzy Logic; D006801:Humans; D009038:Motion; D016571:Neural Networks, Computer; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D012984:Software