Knowledge management system in falling risk for physiotherapy care of elderly

• Main Authors: Worasak Rueangsirarak, Nopasit Chakpitak, Komsak Meksamoot, Prapas Pothongsunun
• Format: Conference Proceeding
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84949925963&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/53380
• Institution: Chiang Mai University

© 2014 Asia-Pacific Signal and Information Processing Ass. This paper describes the elderly healthcare research project affected by a fall. The decision support system is proposed as knowledge management method, including knowledge engineering to acquiring the expert's heuristically diagnostic knowledge and sharing this knowledge to the physiotherapist in the form of tool and application at the right time. This paper outlines a Knowledge Management System (KMS) to diagnose falling patterns in elderly people using Motion Capture Technology. The idea is to integrate an appropriate procedure including case based reasoning and motion capture to provide a decision support system. The diagnosis information derived from the process of KMS helps support the physiotherapist to determine serious falling risks in the elderly and recommend guidelines for medical treatment. The evaluation result shows an efficient performance with 80.95% of precision when using the Assumption Attribute category criteria with KNNR=3. Furthermore, the result of KMS-EUCS shows a high satisfaction from the users with 97.50% of satisfaction in a community of practice scenario. This can confirm the successful of KMS approach within the falling risk screening procedure.