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A Similarity Based Representation for Identifying Healthcare Anomalous Activities

Publication Type : Journal Article

Publisher : Ingenta Connect

Source : Journal of Medical Imaging and Health Informatics, 10(4), pp.787-794

Url : https://www.ingentaconnect.com/contentone/asp/jmihi/2020/00000010/00000004/art00001

Campus : Chennai

School : School of Computing

Department : Computer Science and Engineering

Year : 2020

Abstract : A Vision Based Patient Monitoring system focuses on detecting abnormal activities of a patient. In real-world, factors like occlusion and view point variations make the activity recognition task challenging. This work proposes a similarity-based representation for healthcare activities including abnormal patient activities such as coughing, sneezing, vomiting, falling, etc. Global and depth-based representations such as histogram of optical flow, displacement between skeletal sequences and relative position of skeletal joints are used to represent the spatio-temporal dynamics of activities. A benchmark data namely "NTU RGB + D Action Recognition dataset" is used for testing the performance of the proposed approach. A comparison of the proposed methodology against other state-of-the-art approaches has proved the discrimination of the proposed approach.

Cite this Research Publication : Deepak, K., Sikkandar, M.Y., Siddharth, S. and Chandrakala, S., 2020. A Similarity Based Representation for Identifying Healthcare Anomalous Activities. Journal of Medical Imaging and Health Informatics, 10(4), pp.787-794

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