Incremental diagnosis method for intelligent wearable sensor systems.
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.
Wu
Winston H
WH
Department of Electrical Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA. winston@ee.ucla.edu
Bui
Alex A T
AA
Batalin
Maxim A
MA
Liu
Duo
D
Kaiser
William J
WJ
D000465:Algorithms; D003020:Clothing; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020233:Gait Disorders, Neurologic / Q000175:diagnosis*; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity