Incremental Diagnosis Method for Intelligent Wearable Sensor Systems

• Published in: IEEE journal of biomedical and health informatics 2007-09, Vol.11 (5), p.553-562
• Main Authors: Wu, W.H., Bui, A.A.T., Batalin, M.A., Liu, D., Kaiser, W.J.
• Format: Article
• Language: English
• Subjects: Algorithms; Ambulatory physiologic monitoring; Biomedical monitoring; Computerized monitoring; Costs; Diagnosis, Computer-Assisted - methods; gait assessment; Gait Disorders, Neurologic - diagnosis; Humans; incremental diagnosis; inference engine; Instruments; Intelligent sensors; Intelligent systems; Medical conditions; Medical services; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; naÏve Bayes classifier; Reproducibility of Results; Sensitivity and Specificity; Sensor systems; Studies; utility function; Utility functions; wearable sensor system; Wearable sensors
• ISSN: 1089-7771; 2168-2194; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2007.897579

This paper presents an incremental diagnosis method (IDM) to detect a medical condition with the minimum wearable sensor usage by dynamically adjusting the sensor set based on the patient's state in his/her natural environment. The IDM, comprised of a naive Bayes classifier generated by supervised training with Gaussian clustering, is developed to classify patient motion in- context (due to a medical condition) and in real-time using a wearable sensor system. The IDM also incorporates a utility function, which is a simple form of expert knowledge and user preferences in sensor selection. Upon initial in-context detection, the utility function decides which sensor is to be activated next. High-resolution in-context detection with minimum sensor usage is possible because the necessary sensor can be activated or requested at the appropriate time. As a case study, the IDM is demonstrated in detecting different severity levels of a limp with minimum usage of high diagnostic resolution sensors.