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

Sensors

WesllenSousa Lima¹  Hendrio Bragança¹  JuanG. Colonna¹  Eduardo Souto¹ 

[1] Instituto de Computação, Universidade Federal do Amazonas, Manaus CEP 69067-005, Brazil;

关键词: human activity recognition;    symbolic representation;    discrete domain;    time series classification;    information theory;    inertial sensors;   

DOI  :  10.3390/s20071856

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown