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Unsupervised classification of smartphone activities signals using wavelet packet transform and half-cosine fuzzy clustering

Activity recognition using smartphone provides a ubiquitous and unobtrusive way for people to realize health monitor and ambient assisted living. Since human activities has characteristics of high complexity and diversity, the accurate identification of activity greatly depends on the appropriate features extracted from limited smartphone signals and the efficiency of pattern recognition approaches. An unsupervised classification scheme based on the wavelet packet transform (WPT) and the half-cosine fuzzy clustering (HFC) is proposed in this paper for the automatic feature extraction and recognition of human activities on smartphone. The wavelet packet coefficient features combined with statistic features describe the sensor signals comprehensively. The novel half-cosine initialization eliminates the sensitivity of the fuzzy clustering to initial center distribution. Experiment results of public datasets reveal that the WP-based hybrid features are more suitable for human activity recognition than statistic features. The performance of proposed half-cosine fuzzy clustering is superior than those of FCM, HAC and K-means for the activity recognition on smartphone.