Hyper-Parameter Optimization by Using the Genetic Algorithm for Upper Limb Activities Recognition Based on Neural Networks

A method of Upper Limb Activities Recognition (UPLA) based on Neural Networks is presented. The accuracy of activity recognition will be influenced by the size of sliding window, the overlapping of adjacent sequences and the number of neurons for neural networks. Whereas, there is less work in hyper parameters optimization of neural networks automatically. It is very time-consuming to optimize hyper parameters by experts through an experience and error approach. In the paper, Genetic algorithm is used to find the best hyper parameters automatically: the size of sliding window, the overlapping of adjacent sequences and the number of neurons for neural networks. The basic genetic algorithm has a slow convergence problem and it is very easy to fall into a local optimum. To solve the problem, the population selection mechanism is improved. A comparison is made for the improving method with seven traditional classification algorithms and convolutional neural network, an accuracy of 97.9% is reached by using the new method. Finally, an App is developed that can collect and recognize upper limb activity in real time.