Clustering of Physical Activities for Quantified Self and mHealth Applications

The explosion of smaller and more powerful wearable sensing devices has allowed us to continually record and quantify our lives. Undertaking such activities is becoming very popular and has grown into a community called the Quantified Self (QS). Utilizing this outlet has the potential to benefit many aspects of our lives and is gaining momentum within the health sector. However, whilst we can easily collect data, interpreting this information is more challenging. Without extensive data analysis, this information is essentially meaningless in its raw form. This paper posits an approach to quantify and cluster levels of physical activity from accelerometer and heartrate data, which has been obtained from four wearable devices (3 accelerometers and 1 hear rate monitor). The approach details our method for pre-processing the data, extracting and selecting the features and a comparison between hierarchical clustering analysis (HCA) and k-means. The results illustrate that, whilst both methods are capable of successfully separating the data, the k-means approach outperformed the HCA method at clustering the data