Currently, ubiquitous smartphones embedded with various sensors provide a convenient way to collect raw sequence data. These data bridges the gap between human activity and multiple sensors. Human activity recognition has been widely used in quite a lot of aspects in our daily life, such as medical security, personal safety, living assistance and so on.
To provide an overview, the authors survey and summarize some important technologies and involved key issues of human activity recognition, including activity categorization, feature engineering as well as typical algorithms presented in recent years. In this paper, the authors first introduce the character of embedded sensors and dsiscuss their features, as well as survey some data labeling strategies to get ground truth label. Then, following the process of human activity recognition, the authors discuss the methods and techniques of raw data preprocessing and feature extraction, and summarize some popular algorithms used in model training and activity recognizing. Third, they introduce some interesting application scenarios of human activity recognition and provide some available data sets as ground truth data to validate proposed algorithms.
The authors summarize their viewpoints on human activity recognition, discuss the main challenges and point out some potential research directions.
It is hoped that this work will serve as the steppingstone for those interested in advancing human activity recognition.
This work was supported by the National Natural Science Foundation of China (with grants of 71774159 and U1610124), the State’s Key Project of Research and Development Plan (with grant of 2016YFC0600908), the Fundamental Research Funds for the Central Universities, China (with grant of 2015XKMS085). Guan Yuan and Zhaohui Wang have contributed equally to this paper.
Disclosure statement: No potential conflict of interest was reported by the authors.
Yuan, G., Wang, Z., Meng, F., Yan, Q. and Xia, S. (2019), "An overview of human activity recognition based on smartphone", Sensor Review, Vol. 39 No. 2, pp. 288-306. https://doi.org/10.1108/SR-11-2017-0245Download as .RIS
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