An ensemble dynamic time warping classifier with application to activity recognition

This paper proposes a new ensemble classifier based on Dynamic Time Warping (DTW), and demonstrates how it can be used to combine information from multiple time-series sensors, to relate them to the activities of the person wearing them. The training data for the system comprises a set of short time samples for each sensor and each activity, which are used as templates for DTW, and time series for each sensor are classified by assessing their similarity to these templates. To arrive at a final classification, results from separate classifiers are combined using a voting ensemble. The approach is evaluated on data relating to six different activities of daily living (ADLs) from the MIT Placelab dataset, using hip, thigh and wrist sensors. It is found that the overall average accuracy in recognising all six activities ranges from 45.5% to 57.2% when using individual sensors, but this increases to 84.3% when all three sensors are used together in the ensemble. The results compare well with other published results in which different classification algorithms were used, indicating that the ensemble DTW classification approach is a promising one.