Advanced classification of ambulatory activities using spectral density distances and heart rate

As motion sensors are getting light-weighted and low-priced, there is a growing appetite for the accelerometer-based approaches for efficiently monitoring human activities. This paper proposes an original feature selection approach based on the spectral distances between a given signal and an activity model. This new technique is evaluated and compared to existing techniques in literature. This study also investigates the improvement of classification performances brought by the heart rate (HR) data in addition to the accelerometer data. The experimental dataset is composed of both acceleration and HR recordings from eight volunteers performing five ambulation activities. Four wearable sensor units, including an ECG node are employed. The response of the system to three widely used classifiers, the K-nearest neighbors K-NN, the Naïve Bayes NB and the decision Tree C4.5 is reported along with the classification rates. The results reached up to 99% of overall recognition accuracy and higher than 98% using a single-sensor acceleration data and the HR data. These results demonstrate that the spectral distances approach can be adopted to accurately classify activities and that the joint processing of acceleration signals together with the HR signals can increase the classification accuracy compared to the case when processing the acceleration signals alone.