Transformer-based Human Activity Recognition Using Wearable Sensors for Health Monitoring

In modern society, the prevalence of sedentary lifestyles, driven by office work and the widespread use of technology such as smartphones, televisions, and computers, has led to insufficient physical activity and associated health issues like obesity. According to the World Health Organization (WHO), 31% of adults and 80% of adolescents fail to meet the recommended levels of physical activity. Many individuals also lack awareness of their daily exercise levels.To address this problem, we propose a transformer-based model that leverages wearable sensor data to monitor and classify whole-body movements, helping users determine whether they are physically inactive. The model was evaluated on four benchmark datasets: MobiAct, UniMiB SHAR, USC-HAD, and UCI HAR, achieving F1 Scores of 92.01%, 93.40%, 89.00%, and 87.89%, respectively, outperforming baseline models. These results demonstrate the model’s effectiveness in promoting physical activity awareness and addressing sedentary behaviors.