title: Classification of continuously executed early morning activities using wearable wireless sensors.
abstract: In this paper, we study the personal monitoring system that classifies the continuously executed early morning activities of daily living. The system is intended to assist those with cognitive impairments due to traumatic brain injuries. The system can be used to help therapists in hospitals or could be deployed in one's home to track and monitor the activities executed by the recovering patients. We begin by briefly describing the infrastructure of our cost-effective system which uses fixed and wearable wireless sensors and show results related to the detection of activities continuously executed in the morning. Both frequency and time domain features from an accelerometer attached to the right wrist were extracted and used for classification using Gaussian mixture models, followed by a finite state machine. We show promising classification results obtained from 5 subjects. Overall classification rate is 88.3 % for 4 activities of interests.
authors.affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis 55455, USA. cmin@ umn.edu
authors.forename: Cheol-Hong
authors.initials: CH
authors.lastname: Min
authors.forename: Nuri F
authors.initials: NF
authors.lastname: Ince
authors.forename: Ahmed H
authors.initials: AH
authors.lastname: Tewfik
mesh_terms: D000203:Activities of Daily Living; D003020:Clothing; D003072:Cognition Disorders / Q000451:nursing*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013686:Telemetry / Q000295:instrumentation*; D014159:Transducers