Performance evaluation of multivariate statistical techniques using edge-enabled optimisation for change detection in activity monitoring

The monitoring of human activities using simple body worn sensors is an important and emerging area of research in machine learning. The sensors capture a large amount of data in a short period of Time in a relatively un-obtrusive manner. The sensor data might have different transitions to be used for deification of different user activities. Therefore, change point detection can be used to classify the transition from one underlying distribution to another. The automatic and accurate change point detection is not only used for different events, however, can also be used for generating real world datasets and responding to changes in patient vital signs in critical situation. Moreover, the huge amount of data can use the current state-of-the-art cloud and edge computing platforms to process the change detection locally and more efficiently. In this paper, we used multivariate exponentially weighted moving Average (MEWMA) for online change point detection. Additionally, genetic algorithm (GA) and particle swarm optimization (PSO) is used to automatically identify an optimal parameter set by maximizing the F-measure. The optimisation approach is implemented over an edge cloud platform so that the data can be processed locally and more accurately. Furthermore, we evaluate our approach against multivariate cumulative sum (MCUSUM) from state-of the-art in terms of different metric measures such as accuracy, precision, sensitivity, G-means and F-measure. Results have been evaluated based on real data set collected using accelerometer for a set of 9 distinct activities performed by 10 users for total period of 35 minutes with achieving high accuracy from 99.3% to 99.9% and F-measure up to 62.94%.