Human Activity Discovery With Automatic Multi-Objective Particle Swarm Optimization Clustering With Gaussian Mutation and Game Theory

Despite many advances in Human Activity Recognition (HAR), most existing works are conducted with supervision. Supervised methods rely on labeled training data. However, obtaining labeled data is difficult, costly, and time-consuming. In this paper, we introduce an automatic multi-objective particle swarm optimization clustering based on Gaussian mutation and game theory (MOPGMGT) to provide fully unsupervised human activity discovery. Furthermore, we map the multi-objective clustering problem to game theory to get the best optimal solution. The proposed algorithm can accurately find the number of activities without any prior knowledge. Multi-objective optimization problems typically cannot have a single optimal solution. We solve this problem by applying, Nash Equilibrium (NE) to the pareto front as the decision-making for choosing the best solution. NE does not just look for the best solution but tries to optimize the final solution by considering the effect of choosing each of the solutions as the best solution on the other solutions and one with the best impact is chosen. Moreover, a Gaussian mutation is applied on the pareto front to avoid premature convergence. As far as we know, this is the first time that human activity discovery is performed fully unsupervised, and a multi-objective PSO is mapped to the game theory space for finding the best solution. Experiments on six challenging human activity datasets demonstrate the capability of the proposed approach in achieving the best accuracy in human activity discovery and determining the optimal number of clusters. In comparison to well-known multi-objective algorithms, the MOPGMGT significantly improves the clustering outcomes on six benchmark clustering datasets.