Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review

By Elangovan Ramanujam, Thinagaran Perumal, S. Padmavathi. IEEE Sensors Journal, 2021

362 citations recorded by the source corpus.

Human Activity Recognition (HAR) is a field that infers human activities from raw time-series signals acquired through embedded sensors of smartphones and wearable devices. It has gained much attraction in various smart home environments, especially to continuously monitor human behaviors in ambient assisted living to provide elderly care and rehabilitation. The system follows various operation modules such as data acquisition, pre-processing to eliminate noise and distortions, feature extraction, feature selection, and classification. Recently, various state-of-the-art techniques have proposed feature extraction and selection techniques classified using traditional Machine learning classifiers. However, most of the techniques use rustic feature extraction processes that are incapable of recognizing complex activities. With the emergence and advancement of high computational resources, Deep Learning techniques are widely used in various HAR systems to retrieve features and classification efficiently. Thus, this review paper focuses on providing profound concise of deep learning techniques used in smartphone and wearable sensor-based recognition systems. The proposed techniques are categorized into conventional and hybrid deep learning models described with its uniqueness, merits, and limitations. The paper also discusses various benchmark datasets used in existing techniques. Finally, the paper lists certain challenges and issues that require future research and improvements.

Subfields: Biomedical Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ab4bfecfe4fd6bf43bcdccd850a52d988ddff7bb

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In Computer Vision and Pattern Recognition

1. Shen Zhang
2. Yuanbei Deng
3. Yaxuan Li
4. Farzad Shahabi
5. Nabil Alshurafa
6. Stephen Xia
7. Shibo Zhang

Citations in the corpus

• Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “There are some existing review papers in the literature for deep learning approaches for sensor-based human activity recognition [63, 204, 224, 291].”