Transfer learning and information retrieval applied to fall detection. Issue 6 (9th January 2020)

• Record Type: Journal Article
• Authors: Fañez, Mirko; Villar, José R.; de la Cal, Enrique; Sedano, Javier; González, Victor M.
• Other Names: Gonzalez‐Pardo Antonio guestEditor.; Tallón‐Ballesteros Antonio J. guestEditor.; Yin Hujun guestEditor.; Cortez Paulo guestEditor.; Bifet Albert guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-09
• Subjects: fall detection -- machine learning -- time series -- transfer learning; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12522
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite‐state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dictionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimentation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed similarly for all the individuals tested. Furthermore, the positive effects of TL in this context are clearly remarked.