| Literature DB >> 33344100 |
Florenc Demrozi1, Graziano Pravadelli1, Azra Bihorac2, Parisa Rashidi3.
Abstract
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly's daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.Entities:
Keywords: Accelerometer; Available Datasets; Deep Learning (DL); Human Activity Recognition (HAR); Machine Learning (ML); Sensors
Year: 2020 PMID： 33344100 PMCID： PMC7748247 DOI： 10.1109/access.2020.3037715
Source DB: PubMed Journal: IEEE Access ISSN： 2169-3536 Impact factor: 3.367