Using Genetic Algorithms for Optimal Change Point Detection in Activity Monitoring

Activity Monitoring is a key feature of health and well-being assessment that has received increased consideration from the research community over the last few decades. Body worn sensors and smart devices are widely used in Activity Monitoring in order to capture and classify large amounts of data over short periods of time, in a relatively un-obtrusive manner. Change point detection is a technique at the core of the data processing of the sensory data recorded used to identify the transition from one underlying time series generation model to another. The sudden change in mean, variance or both may represent change point in time series data. Accurate and automatic change point detection in data is not only used to identify events (transition from one activity to another), however, can also be used for labelling activities to generate real world annotated datasets. This paper proposes a genetic algorithm (GA) that identifies the optimal set of parameters for a Multivariate Exponentially Weighted Moving Average (MEWMA) approach to change point detection. The proposed technique optimizes different parameters of the MEWMA in an effort to find the maximum F-measure, which subsequently identifies the exact location of the change point from an existing activity to a new one. Results have been evaluated based on real and synthetic datasets collected from accelerometer data during a set of 8 different activities for two users with a high degree of accuracy form 99.4% to 99.8% and F-measure to 66.7%.