Advanced portable remote monitoring system for the regulation of treadmill running exercises
- Publication Type:
- Journal Article
- Citation:
- Artificial Intelligence in Medicine, 2014, 61 (2), pp. 119 - 126
- Issue Date:
- 2014-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | Advanced portable remote monitoring system for the regulation oftreadmill running exercises.pdf | Submitted Version | 2.18 MB |
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Objective: This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises. Materials and methods: The supervisory level of the developed hierarchical system is implemented on a portable monitoring device (iPhone/iPad) as a client application, while the real-time control of treadmill exercises is accomplished by using an on-line adaptive neural network control scheme in a local computer system. During training or rehabilitation exercises, the intensity (measured by heart rate) is regulated by simultaneously manipulating both treadmill speed and gradient. In order to achieve adaptive tracking performance, a neural network controller has been designed and implemented. Results: Six real-time experiments have been conducted to test the performance of the developed monitoring system. Experimental results obtained in real-time with heart-rate set-point varying from 145bpm to 180bmp, demonstrate that the proposed system can quickly and accurately regulate exercise intensity of treadmill running exercises with desired performance (no overshoot, settling time Ts≤100s). Subjects aged from 29 to 38 years old participated in different set-point experiments to confirm the system's adaptability to inter- and intra-model uncertainty. The desired system performance under external disturbances has also been confirmed in a final real-time experiment demonstrating a user carrying the 10kg bag then removing it during the exercise. Conclusion: In contrast with conventional control approaches, the proposed adaptive controller achieves better heart rate tracking performance under inter- and intra-model uncertainty and external disturbances. The developed system can automatically adapt to various individual exercisers and a range of exercise intensity. © 2014 Elsevier B.V.
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