An Intelligent Approach to Recognizing Human Movements Based on a Wearable Device

Abstract

n this paper, we consider the possibility of distinguishing the movements of a person and people by their gait based on data obtained from the accelerometer of a wearable device. A mobile phone was used as a wearable device. The paper considers the features of recognizing human movements based on a wearable device. A recognition algorithm based on a neural network with preliminary data processing and correlation analysis is proposed. The volume of the training sample consisted of 32 subjects with various physiological characteristics. The sample size in the subgroup of four people ranged from 2000 to 3000 movements. The main motor patterns for classification were the movements performed when walking in a straight line and stairs with a load (a bag with a laptop weighing 3.5 kg) and without it. The direct propagation network is chosen as the basic structure for the neural network. The neural network has 260 input neurons, 100 neurons in one hidden layer, and 4 neurons in the output layer. When training the neural network, the gradient reverse descent function was used. Cross- entropy was used as an optimization criterion. The activation function of the hidden layer was a sigmoid, and the output layer was a normalized exponential function. The presented algorithm makes it possible to distinguish between subjects when performing different movements in more than 90% of cases. The practical application of the results of the work is relevant for automated information systems of the medical, law enforcement and banking sectors.