PracticalHAR: A Practical Method of Human Activity Recognition Based on Embedded Sensors of Mobile Phone

Human Activity Recognition (HAR) is the process of identifying human activity states based on collected data, with applications in health monitoring, rehabilitation therapy, and smart homes. However, accurate recognition often necessitates the use of multiple sensors, which can cause discomfort and significantly reduce practicality and convenience. Currently, smartphones with embedded sensors have broadened the potential for HAR, enabling healthcare monitoring, life logging, fitness tracking, and more. This paper conducts experiments on the WISDM public dataset and a self-collected dataset, demonstrating that a one-dimensional convolutional neural network structure does not necessarily underperform a two-dimensional one, using only accelerometer data. Moreover, the experimental results on the self-collected dataset suggest that utilizing the barometer built into smartphones can enhance activity recognition rates. Specifically, we first built the corresponding convolutional neural network model and conducted experiments on the WISDM dataset, and the accuracy reached 95.42%. Next, we collected data from the smartphone’s embedded barometer and accelerometer to construct a dataset. Experimental results on our self-collected dataset show that the inclusion of barometer data can improve the accuracy of activity recognition, especially stair-related activities. The F1-score for identifying walking, ascending stairs, and descending stairs increased by 6.7%, 7.39%, and 2.99% respectively.