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Even though it is well known that physical exercises have numerous emotionaland physical health benefits, maintaining a regular exercise routine is quitechallenging. Fortunately, there exist technologies that promote physicalactivity. Nonetheless, almost all of these technologies only target a narrowset of physical activities (e.g., either running or walking but not both) andare only applicable either in indoor or in outdoor environments, but do notwork well in both environments. This paper introduces a real-time segmentationand classification algorithm that recognizes physical exercises and that workswell in both indoor and outdoor environments. The proposed algorithm achieves a95\% classification accuracy for five indoor and outdoor exercises, includingsegmentation error. This accuracy is similar or better than previous works thathandled 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 eachtarget exercises.

ExerSense: Real-Tme Physical Exercise Segmentation, Classification, and Counting Algorithm Using an IMU Sensor