Name
Abstract | ||
|---|---|---|
Exercise intensity monitoring of physical activities has drawn increasingly attention as the awareness of the exercise intensity is of great importance for a person to achieve optimal training outcomes. For example, over-training could lead to excessive fatigue and loss of motivation for exercise. Traditional exercise intensity monitoring systems utilize GPS data to track the user’s intensity of cardio activities through his/her position and speed. Such systems however become invalid for indoor exercises on stationary fitness equipments such as the treadmill or exercise bike. Recent work in using body-worn sensors to track the user’s heart rate for exercise intensity monitoring usually involves additional wearable sensors which are only available on some particular fitness equipments, and thus are hard to be used in all occasions. This work presents an exercise intensity monitoring system which is capable of detecting a person’s exercise intensity via smartphones. Our system exploits the off-the-shelf smartphone and its headphone to capture the user’s breathing sound. Given the captured acoustic data, our system performs data pre-processing to remove the environmental noise and identify the non-silent acoustic frames based on the signal energy. Our system then conducts breathing event detection for non-silent frames, and further calibrates the detection results by utilizing the high correlation between breathing cycles to improve the detection accuracy. Moreover, our system can estimate the person’s exercise intensity based on features extracted from the frames which contain breathing sound. Our experiments involving 9 subjects over four-month time period demonstrate that our proposed exercise intensity monitoring system is robust and accurate in both indoor and outdoor environments. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCCN52240.2021.9522176 | 2021 International Conference on Computer Communications and Networks (ICCCN) |
Keywords | DocType | ISSN |
exercise intensity monitoring system,person,breathing sound,traditional exercise intensity,user,indoor exercises | Conference | 1095-2055 |
ISBN | Citations | PageRank |
978-1-6654-4835-2 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanzhi Ren | 1 | 8 | 2.39 |
| Zhourong Zheng | 2 | 0 | 0.34 |
| Hongbo Liu | 3 | 12 | 2.88 |
| Yingying Chen | 4 | 2495 | 193.14 |
| Hongwei Li | 5 | 154 | 12.60 |
| Chen Wang | 6 | 41 | 11.11 |