Abstract
Human Activity Recognition (HAR) has significant role in various real-life applications such as smart healthcare and ubiquitous computing. Noninvasive property of smartphone makes in-built smartphone sensors useful to identify physical human activities. Due to the noisy signals of the smartphone sensors, a great extent of feature engineering is performed to take out discriminant features. In literature, various state-of-the-art approaches are used to identify physical human activities. In this paper, Extreme Learning Machine (ELM) algorithm is used for classification as ELM overcomes the problem of overfitting and slow learning speed. In our investigation, real physical data is collected using smartphone sensors, keeping the smartphone in the front pant pocket. We also compare our proposed method with some benchmark schemes to establish the output performance of the proposed mechanism.
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Thakur, D., Biswas, S. (2021). A Novel Human Activity Recognition Strategy Using Extreme Learning Machine Algorithm for Smart Health. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9927-9_21
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DOI: https://doi.org/10.1007/978-981-15-9927-9_21
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