We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

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The efficient and effective Anomaly detection system essentially requires identifying the behavior analysis for each activity. For this purpose unsupervised techniques are used but the accuracy and reliability of them results depend on the data set which have used for modeling. It is essential to identify important input features, missing values, redundancy, feature exploration etc… So for the data preprocessing different statistical analytical methods are used. In this paper, a statistical feature construction scheme is proposed based on Factor analysis. The proposed Feature construction model provides the way to remove redundancy, identify missing values and co-linearity between the initial data set. Experimental result shows the related good features are factorized using statistical measures. So it will improve the performance of the unsupervised algorithm results for the effective anomaly detection system.