Abstract Salat learning is one of the most important processes for every Muslim. The current learning approach requires the teacher or expert to be present in the learning session, which is time and resource-consuming. Previous researchers use both wearable and non-wearable sensors to recognize salat movement. They focus on specific salat movement rather than complete salat movement cycle. This paper present a motion recognition system to enhance salat learning experience. The system helps users recognize the complete salat movements cycle based on salat law by using a multisensor setup for better tracking capability. Three evaluations are conducted to validate the system's performance and its contribution. The first evaluation is to measure success score in recognition accuracy and identify user error. The second evaluation is conducted to compare the proposed system with the traditional-based methodology, and the last evaluation focuses on the user experience and acceptance of the proposed system. The result from performance evaluation shows the system has high accuracy in recognizing salat movement. There is a significant difference in the error rate and success score when comparing the learning methodology. However, users provide positive feedback based on the survey conducted after using the proposed system.

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[31] Yamato J., Ohya J., and Ishii K., “Recognizing Human Action in Time-Sequential Images Using Hidden Markov Model,” in CVPR, vol. 92, pp. 379-385. 1992. Nor Azrini Jaafar received her bachelor’s degree in computer science from Universiti Teknologi Malaysia (UTM) in 2012. Currently, she is doing a Ph.D. in Human-Computer Interaction (HCI). Her research interests include Human-Computer Interaction, motion recognition, and machine learning. Nor Azman Ismail received his BSc from Universiti Teknologi Malaysia (UTM), Master of Information Technology from Universiti Kebangsaan Malaysia (UKM), and Ph.D. in the field of Human- Computer Interaction (HCI) from Loughborough University. He has been a lecturer at the Faculty of Computing, Universiti Teknologi Malaysia for more than twenty years. He has made various contributions to the field of Human- Computer Interaction including research, practice, and education. Yusman Azimi Yusoff received his bachelor’s degree in computer science in 2013 and Ph.D. in scientific visualization in 2020 from Universiti Teknologi Malaysia. His research interests include machine learning, data visualization, and Internet-of- Things. He has been a Data Scientist at PPG Coating (M) Sdn Bhd since 2019.