Human Activity-Based Machine Learning and Deep Learning Techniques
DOI:
https://doi.org/10.22399/ijcesen.1368Keywords:
Human Activity Recognition, Machine learning, Deep Learning, Sensors, Healthcare, Activity MonitoringAbstract
Human activity recognition (HAR) has been hot research issues in recent years. The studies have differences in data types, data processing, feature description, etc. HAR constitutes a fundamental component of intelligent health monitoring systems, wherein the underlying intelligence of the services is derived from and enhanced by sensor data. Researchers have proposed multiple HAR systems designed to convert smartphone readings into other forms of physical activity. This review synthesizes the current methodologies for smartphone-based Human Activity Recognition (HAR) with focusing on healthcare application. For this purpose, we systematically searched for peer-reviewed articles regarding the utilization of cell phones for Human Activity Recognition (HAR). We collect information regarding smartphone body placement, sensors, types of physical activities examined, as well as the data transformation methodologies and classification frameworks employed for activity recognition. Thus, we selected these articles and delineated the diverse methodologies employed for data gathering, preprocessing, extraction of features, and activity classification, highlighting the predominant practices and their alternatives. We determine that cell phones are very appropriate for HAR research within the health sciences. Future studies should prioritize enhancing the quality of data gathered, addressing data gaps, incorporating a more diverse array of participants and activities, relaxing phone placement requirements, providing comprehensive documentation for study participants, and sharing the source code of the employed methods and algorithms to achieve population-level impact.
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