2020/01/01 by Florenc Demrozi, Graziano Pravadelli, Azra Bihorac +1
Computer Science · Engineering · #Activity recognition #Artificial intelligence #Computer science #Context-Aware Activity Recognition Systems #Deep learning #Embedded system #Human Pose and Action Recognition #Human–computer interaction #Inertial measurement unit #IoT and Edge/Fog Computing #Multimedia #Smartwatch #Wearable computer #cs.HC #cs.LG #eess.SP
paper · pdf · doi:10.1109/access.2020.3037715
Accepted for Publication in IEEE Access DOI: 10.1109/ACCESS.2020.3037715
openalex publication_date 2020/01/01 · arxiv created 2020/11/19 · arxiv updated 2020/11/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.