EFM: evolutionary fuzzy model for dynamic activities recognition using a smartphone accelerometer

Activity recognition is an emerging field of research that enables a large number of human-centric applications in the u-healthcare domain. Currently, there are major challenges facing this field, including creating devices that are unobtrusive and handling uncertainties associated with dynamic activities. In this paper, we propose a novel Evolutionary Fuzzy Model (EFM) to measure the uncertainties associated with dynamic activities and relax the domain knowledge constraints which are imposed by domain experts during the development of fuzzy systems. Based on the time and frequency domain features, we define the fuzzy sets and estimate the natural grouping of data through expectation maximization of the likelihoods. A Genetic Algorithm (GA) is investigated and designed to determine the optimal fuzzy rules. To evaluate the EFM, we performed experiments on seven daily life activities of ten human subjects. Our experiments show significant improvement of 9 % in class-accuracy and 11 % in the F-measures of recognized activities compared to existing counterparts. The practical solution to dynamic activity recognition problems is expected to be an EFM, due to EFM's utilization of smartphones and natural way of handling uncertainties.