Leveraging a Smartwatch for Activity Recognition in Salat.

Salat, the most important worship of Muslims and the second pillar of Islam, is an integral part of the Muslim community. Being a complex activity, Salat involves a series of steady and transitional activities to be performed in a specific sequence. On top of that, Salat has variations based on time, priority, school of thought, etc., making activity recognition in Salat more challenging. Existing research studies related to recognizing individual activities in Salat either demand capturing images by a camera or carrying a smartphone (sometimes in inconvenient places) while praying. Both of the demands are not convenient or applicable in real cases. Besides, the existing studies lack user-independent accuracy analysis and fine-grained prediction. To address these gaps, in this study, we first assess the requirement and acceptability of technological solutions for activity recognition in Salat by conducting an exploratory study. Upon establishing the requirement, we propose an activity recognition methodology using a smartwatch to recognize different activities in Salat. We prepare a Salat activity dataset using a smartwatch and propose a new methodology using semantic rules and Dynamic Time Warping (DTW) that achieves a near-perfect accuracy (99.3%) in recognizing activities in Salat. Besides, our proposed methodology offers fine-grained recognition of the individual activities in Salat and is robust enough to overlook the extra transitional activities a person performs while praying, which does not nullify Salat. Therefore, this research is expected to lead to a comprehensive solution for monitoring Salat.