A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 7; p. 1856
• Main Authors: Bragança, Hendrio, Colonna, Juan G., Lima, Wesllen Sousa, Souto, Eduardo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 27.03.2020; MDPI
• Subjects: Accelerometry; Algorithms; Classification; Databases, Factual; discrete domain; Human Activities; human activity recognition; Humans; inertial sensors; Information Theory; Machine Learning; Monitoring, Physiologic; Neural networks; Sensors; Smartphone; Smartphones; Support vector machines; symbolic representation; Time series; time series classification; inertial sensors; discrete domain; information theory; symbolic representation; human activity recognition; time series classification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20071856

Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models, increasing in this way the efficiency in terms of computational cost. Three public databases (SHOAIB, UCI, WISDM) are used in the evaluation process. The results have shown that HAR-SR can classify activities with 93% accuracy when using a leave-one-subject-out cross-validation procedure (LOSO).