Real-time daily activity classification with wireless sensor networks using Hidden Markov Model.
This paper presents a Hidden Markov Model (HMM) approach for real-time activity classification using signals from wearable wireless sensor networks. A wearable wireless sensor network can be used to continuously monitor the daily activities of a subject in real time. However, the wireless sensor nodes are constrained by limited battery and computing resources. The proposed HMM framework has been applied to find the most probable activity states series with low data transmission rate, which makes it highly suitable for daily activity classification applications. The performance was evaluated using a small sensor network consisting of three accelerometers. The activity detection rate is 95.82%, using a test set of 5 subjects with 11 activity series.
He
Jin
J
Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, MI 49931, USA.
Li
Huaming
H
Tan
Jindong
J
D000054:Acceleration; D000203:Activities of Daily Living; D000465:Algorithms; D003195:Computer Communication Networks / Q000295:instrumentation*; D003198:Computer Simulation; D003199:Computer Systems; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods; D009043:Motor Activity / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D013686:Telemetry / Q000295:instrumentation* / Q000379:methods; D014159:Transducers