Research on recognition of physical activity types based on a single triaxial acceleration sensor

• Main Authors: Li, Yan, Yang, Wen-li
• Format: Conference Proceeding
• Language: English
• Published: SPIE 28.03.2024
• ISBN: 1510675051; 9781510675056
• ISSN: 0277-786X
• DOI: 10.1117/12.3025022

Single triaxial acceleration sensor was used to acquire acceleration information generated by physical activities, and classify physical activities with widely varying degrees of intensity into three types: resting (including sitting, lying down and standing), walking (including running, walking up and down stairs), and running and jumping (including running and jumping in place), so as to provide an objective basis for assessing the level of physical activity. Methods: Forty-four participants wearing accelerometers performed eight physical activities, namely meditation, lying down, standing, running, going upstairs, going downstairs, sprinting, and jumping in place. The accelerometer data were collected, cleaned, and preprocessed using a band-pass filter. The signal amplitude region, mean, standard deviation, correlation coefficient of triaxial, frequency domain entropy and other characteristic quantities are then extracted from the filtered signal. Lastly, a radial basis kernel function support vector machine recognition model is used for physical activity recognition and validated using a leave-one-subject-cross-validation method. The results show that The highest recognition rate of 98.50% was achieved using the frequency domain composite feature subset, whereas the time domain feature subset achieved a slightly lower recognition rate of 98.3%. The time taken was calculated to be 223ms and 146ms. The window size was found to have the highest recognition time of 5.12s. In conclusion: A single triaxial acceleration sensor can collect substantial data about human activities for identification purposes. Creating a physical activity type recognition model that uses the radial basis function kernel and support vector machine (SVM) can allow for real-time and accurate identification of physical activity types in natural environments.