Abstract
A method for human activity recognition using mobile phones is introduced. Using the accelerometer and gyroscope typically found in modern smartphones, a system that uses the proposed method is able to recognize low level activities, including athletic exercises, with high accuracy. A Hebbian learning preprocessing stage is used to render accelerometer and gyroscope signals independent to the orientation of the smartphone inside the user’s pocket. After preprocessing, a selected set of features are obtained and used for classification by a k-nearest neighbor or a multilayer perceptron. The trained algorithm achieves an accuracy of 95.3 percent when using the multilayer perceptron and tested on unknown users who are asked to perform the exercises after placing the mobile device in their pocket without any constraints on the orientation. Comparison of performance with respect to other popular methods is provided.
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