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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 addressed 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 &#x201C;C2FHAR&#x201D;, a novel approach for coarse-to-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 real-time 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.

C2FHAR: Coarse-to-Fine Human Activity Recognition With Behavioral Context Modeling Using Smart Inertial Sensors