Real-time daily activity classification with wireless sensor networks using hidden Markov model
Document Type
Conference Proceeding
Publication Date
12-1-2007
Abstract
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. © 2007 IEEE.
Publication Title
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Recommended Citation
Jin, H.,
Huaming, L.,
&
Jindong, T.
(2007).
Real-time daily activity classification with wireless sensor networks using hidden Markov model.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 3192-3195.
http://doi.org/10.1109/IEMBS.2007.4353008
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10619