Ambulatory physical activity representation and classification using spectral distances approach

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Methods for monitoring the human physical activity are recently investigated in order to assess the health status of the individuals and thus promote a healthier lifestyle. This paper proposes ‘dist-colorimetrics’, a methodology that aims to represent and classify ambulatory activities based on the spectral distances measures. A data collection platform including four accelerometer sensors mounted on the chest, ankle, wrist and hip, is used to record five activities: running, walking, cycling, resting and car riding. The proposed approach converts raw acceleration data into relevant spectral distances parameters. A 2D colored illustration of these parameters provides efficient visual representation as to the similarity and the variation among activities. For a further validation in terms of recognition performance, the ‘dist-colorimetrics’ model was trained and tested by implementing three classification techniques, namely the Naive Bayes, the K-nearest neighbors and the decision tree. The results showed that the system reached up to 98.12% of overall recognition accuracy. With further improvement in the modeling of each activity, we have reason to believe that the spectral distances are a promising approach to distinguish between different physical activities.