A Similarity Based Representation for Identifying Healthcare Anomalous Activities
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.
Keywords: DISPLACEMENT FEATURES; HISTOGRAMS OF OPTICAL FLOW (HOF); RELATIVE POSITION FEATURES; SIMILARITY BASED REPRESENTATION; SUPPORT VECTOR MACHINE; VISION BASED HEALTH CARE ANOMALIES; VISION BASED PATIENT MONITORING
Document Type: Research Article
Publication date: 01 April 2020
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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