A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Applications of sensing platforms with wearable computers
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
BSN '12 Proceedings of the 2012 Ninth International Conference on Wearable and Implantable Body Sensor Networks
A feature selection-based framework for human activity recognition using wearable multimodal sensors
Proceedings of the 6th International Conference on Body Area Networks
On the selection of an optimal wavelet basis for texture characterization
IEEE Transactions on Image Processing
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Activity recognition using motion sensors, which denotes a person's posture, has become one of the most important research topics in body sensor network. With the rapid development of monitoring and sensing applications, activity recognition on mobile devices or portable platforms has drawn lots of attentions. Constrained by computing capability and energy budget, activity recognition on mobile devices faces the challenge of hungry energy consumption. Most of the existing work focus on modeling and recognizing activities accurately, however, without computational cost consideration. In this paper, we present WCF: Wavelet Coefficients based Features for activity recognition, which are cost-effective on feature extraction. WCF are extracted from wavelet domain of the sensory raw data and describes the timbre and rhythm properties of activities. Feature space in WCF is hierarchical. Compared with other features, WCF are extracted with less computational cost and redundancy. And WCF have better classification accuracy on activity recognition task. Experiments are conducted on the large public data set USC-HAD to recognize 11 kinds of activities, and our approach outperforms others by reducing 55% ~ 75% computational cost as well as achieving 96.23% classification accuracy.