Improving wearable-based fall detection with unsupervised learning. (11th December 2020)

• Record Type: Journal Article
• Authors: Fáñez, Mirko; Villar, José R; de la Cal, Enrique; González, Víctor M; Sedano, Javier
• Is Part Of: Logic journal of the IGPL. Volume 30:Number 2(2022)
• Page Start: 314
• Page End: 325
• Publication Date: 2020-12-11
• Subjects: Fall detection -- unsupervised learning -- clustering; one-class classifier; Logic, Symbolic and mathematical -- Periodicals; 511.3
• DOI: 10.1093/jigpal/jzaa064
• Languages: English
• ISSNs: 1367-0751

Abstract

Abstract: Fall detection (FD) is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers (3DACC), developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: (i) an analysis of the event detection stage, comparing several alternatives, (ii) an evaluation of features to extract for each detected event and (iii) an appraisal of up to 6 different clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices.