Human Activity Recognition and Embedded Application Based on Convolutional Neural Network

Abstract

With the improvement of people’s living standards, the demand for health monitoring and exercise detection is increasing. It is of great significance to study human activity recognition (HAR) methods that are different from traditional feature extraction methods. This article uses convolutional neural network (CNN) algorithms in deep learning to automatically extract features of activities related to human life. We used a stochastic gradient descent algorithm to optimize the parameters of the CNN. The trained network model is compressed on STM32CubeMX-AI. Finally, this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life, such as sitting, standing, walking, jogging, upstairs, and downstairs. The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity, thereby solving the HAR problem. By drawing the accuracy curve, loss function curve, and confusion matrix diagram of the training model, the recognition effect of the convolutional neural network can be seen more intuitively. After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it, the best model is then selected.