A probabilistic data-driven method for human activity recognition

Abstract

This paper proposes a probabilistic, time efficient, data-driven method for human low and medium level activity recognition and indoor tracking. The obtained results can be applied to a probabilistic reasoner for high level activity recognition. The proposed method is tested on Opportunity, a dataset consisting of daily morning activities in a highly sensor-rich environment. The main objective of this research is to suggest and apply methods suitable for batch processing of big data. In this case, performance in terms of CPU time and efficiency in storage usage are the top priorities. We applied fast signal processing methods to compute proper features from different collections of sensor signals. The relevant collections of features are selected and fed into a classifier to obtain results in the form of probability for each instance belonging to available classes. Additionally, the most probable locations of each subject in the room are calculated by processing noisy data from location tags on the subjects' body. Afterwards, the proposed probabilistic data smoothing method is applied to further increase accuracy. To evaluate the methods, the most probable recognitions are benchmarked against the results of the Opportunity Challenge competitions as well as provided results by the Opportunity group. We also implemented a couple of well-known methods on the current dataset and compared them with ours. Moreover, the performance of different sensors assemblies is investigated. Our proposed method could obtain very close results in terms of accuracy while it is more optimal in terms of number of features and required time.