C2FHAR: Coarse-to-Fine Human Activity Recognition with Behavioral Context Modeling using Smart Inertial Sensors

Muhammad Ehatisham-ul-Haq, Muhammad Awais Azam, Yasar Amin, Usman Naeem
2020 IEEE Access  
Smart sensing devices are furnished with an array of sensors, including locomotion sensors, which enable continuous and passive monitoring of human activities for the ambient assisted living. As a result, sensor-based human activity recognition has earned significant popularity in the past few years. A lot of successful research studies have been conducted in this regard. However, the accurate recognition of in-the-wild human activities in real-time is still a fundamental challenge to be
more » ... ed as human physical activity patterns are adversely affected by their behavioral contexts. Moreover, it is essential to infer a user's behavioral context along with the physical activity to enable context-aware and knowledge-driven applications in real-time. Therefore, this research work presents "C2FHAR", a novel approach for coarseto-fine human activity recognition in-the-wild, which explicitly models the user's behavioral contexts with activities of daily living to learn and recognize the fine-grained human activities. For addressing realtime activity recognition challenges, the proposed scheme utilizes a multi-label classification model for identifying in-the-wild human activities at two different levels, i.e., coarse or fine-grained, depending upon the real-time use-cases. The proposed scheme is validated with extensive experiments using heterogeneous sensors, which demonstrate its efficacy. INDEX TERMS Activity recognition, behavioral context, context-aware, machine learning, smart sensing.
doi:10.1109/access.2020.2964237 fatcat:zxmbpn3elrbelowwfldib3vhyi