ExerSense: Real-Tme Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor
This addresses the challenge of maintaining regular exercise routines by providing a versatile real-time algorithm that works across environments, though it is incremental as it builds on existing segmentation and classification techniques.
The paper tackles the problem of recognizing physical exercises in both indoor and outdoor environments using an IMU sensor, achieving 95% classification accuracy for five exercises with segmentation error, which is similar or better than previous methods limited to indoor settings or vision-based approaches.
Even though it is well known that physical exercises have numerous emotional and physical health benefits, maintaining a regular exercise routine is quite challenging. Fortunately, there exist technologies that promote physical activity. Nonetheless, almost all of these technologies only target a narrow set of physical activities (e.g., either running or walking but not both) and are only applicable either in indoor or in outdoor environments, but do not work well in both environments. This paper introduces a real-time segmentation and classification algorithm that recognizes physical exercises and that works well in both indoor and outdoor environments. The proposed algorithm achieves a 95\% classification accuracy for five indoor and outdoor exercises, including segmentation error. This accuracy is similar or better than previous works that handled only indoor workouts and those use a vision-based approach. Moreover, while comparable machine learning-based approaches need a lot of training data, the proposed correlation-based method needs one sample of motion data of each target exercises.