Physical activity classification using a single triaxial accelerometer based on HMM
Physical activity classification using a single triaxial accelerometer based on HMM
- Author(s): Aiguang Li ; Lianying Ji ; Shaofeng Wang ; Jiankang Wu
- DOI: 10.1049/cp.2010.1045
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- Author(s): Aiguang Li ; Lianying Ji ; Shaofeng Wang ; Jiankang Wu Source: IET International Conference on Wireless Sensor Network 2010 (IET-WSN 2010), 2010 p. 155 – 160
- Conference: IET International Conference on Wireless Sensor Network 2010 (IET-WSN 2010)
- DOI: 10.1049/cp.2010.1045
- ISBN: 978-1-84919-239-2
- Location: Beijing, China
- Conference date: 15-17 Nov. 2010
- Format: PDF
This study focuses on physical activity classification method using a single triaxial accelerometer attached on chest. With acceleration data acquired by a wearable wireless device, features are extracted using sliding window to describe different activity types. Hidden Markov Model (HMM) is used to recognize physical activity sequence. A modified Viterbi algorithm is used to find the optimal state sequence. The experimental results on 6 subjects have achieved an overall accuracy of 99.59% using our method, which is the best result so far.
Inspec keywords: maximum likelihood estimation; hidden Markov models; accelerometers
Subjects: Probability theory, stochastic processes, and statistics; Markov processes; Velocity, acceleration and rotation measurement; Other topics in statistics; Velocity, acceleration and rotation measurement; Sensing devices and transducers; Sensing and detecting devices
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