In this paper, we present an implementation of a real time activity recognizer running on a cellphone. First, simple activity recognition from accelerometer data is performed and then, this information is fused with data from Wi-Fi Access Points to classify the activity being performed by the user. The training set consisted of 8 activities performed in an academic environment and the classification accuracy was 89.7% using a supervised learning approach.
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