Abstract Human activity recognition has been the popular area of research among the computer vision researchers. The proposed work is focused on patient activity recognition in hospital room environment. We have investigated the optimum supportive features for the patient activity recognition problem. Exploiting the strength of space-time template approaches for activity analysis, Motion-Density Image (MDI) is proposed for patient’s activities when used supportively with Motion-History Image (MHI). The final feature vector is created by combining the description of MHI and MDI using Motion Orientation Histograms (MOH) and then applying Linear Discriminant Analysis (LDA) for dimensionality reduction. The LDA technique not only reduced the complexity cost required for classification but also played vital role to get best recognition results by increasing between-class separation and decreasing the with-in class variance. To validate the proposed approach, we recorded a video dataset containing 8 activities of patients performed in hospital room environment under indoor conditions. We have successfully validated the results of the proposed approach on our dataset by training the SVM classifier and achieved 97.9% average recognition accuracy.

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[20] Yu M., Yu Y., Rhuma A., Naqvi S., Wang L., and Chambers J., “An Online One-Class Support Vector Machine-Based Person-Specific Fall Detection System for Monitoring an Elderly Individual in a Room Environment,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 6, pp. 1002-1014, 2013. 512 The International Arab Journal of Information Technology, Vol. 18, No. 4, July 2021 Muhammad Adeel Abbas received the B.S. degree in Electronics Engineering from International Islamic University Islamabad, Pakistan in 2014 and received the M.S. degree in Electronics Engineering from University of Engineering and Technology Taxila, Pakistan in 2017. His research interests are in image processing and human activity recognition. Fiza Murtaza is currently serving at Sino-Pak Center for Artificial Intelligence (SPCAI), PAF-IAST, Pakistan. She got her BS degree in Electrical (Computer) Engineering from COMSATS Institute of Information Technology, Abbottabad, Pakistan in 2013. She completed her MS in Computer Engineering with specialization in Signal and Image Processing from the University of Engineering and Technology (UET), Taxila, Pakistan, in 2015. She completed her PhD. in Computer Engineering with specialization in Computer Vision from UET, Taxila, Pakistan. Her PhD research title is “Temporal human action detection in long and untrimmed videos”. Her area of research includes Artificial Intelligence, Computer Vision, Machine Learning, Human Action Recognition and Temporal Human Action Detection. Muhammad Obaid Ullah received the B.Sc. (Hons.) and M.Sc. degrees in Electrical engineering from the University of Engineering and Technology (UET) at Taxila, Pakistan, in 2000 and 2006, respectively. He received the Ph.D. degree in Electrical engineering from the University of Manchester, Manchester, U.K., in 2012. He is currently working as a Professor with the University of Engineering and Technology, at Taxila, Pakistan. His current research interests include applied signal processing, sensor networks, and machine learning. Muhammad Haroon Yousaf is currently serving as a Professor in Computer Engineering Department, University of Engineering and Technology Taxila, Pakistan. His research interests are image processing/computer vision. He is heading Swarm Robotics Lab under National Centre for Robotics and Automation. He has published more than fifty papers in International Conferences and Journals. He has been the recipient of Best University Teacher Award (2012-2013) given by Higher Education Commission (HEC) of Pakistan. He is serving as a TPC member for many conferences and reviewer for renowned journals. He is the Senior Member of IEEE and Professional Member of Pakistan Engineering Council.