Nguyen Tuan Nghia, Su Steven, Celler Branko, Nguyen Hung
Faculty of Engineering and Information Technology, University of Technology, Sydney, 15 Broadway, Ultimo, NSW 2007, Australia.
Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, PO Box 76, Epping, NSW 1710, Australia.
Artif Intell Med. 2014 Jun;61(2):119-26. doi: 10.1016/j.artmed.2014.05.002. Epub 2014 May 23.
This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises.
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.
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 145 bpm to 180 bmp, demonstrate that the proposed system can quickly and accurately regulate exercise intensity of treadmill running exercises with desired performance (no overshoot, settling time Ts ≤ 100 s). 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 10 kg bag then removing it during the exercise.
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.
本研究旨在开发一种先进的便携式远程监测系统，以监督高强度跑步机运动。
所开发的分层系统的监控层在便携式监测设备（iPhone/iPad）上作为客户端应用程序实现，而跑步机运动的实时控制则通过在本地计算机系统中使用在线自适应神经网络控制方案来完成。在训练或康复运动期间，强度（通过心率测量）通过同时操纵跑步机速度和坡度来调节。为了实现自适应跟踪性能，设计并实现了一个神经网络控制器。
进行了六项实时实验来测试所开发监测系统的性能。心率设定点在145次/分钟至180次/分钟之间变化时实时获得的实验结果表明，所提出的系统能够以期望的性能（无超调，调节时间Ts≤100秒）快速准确地调节跑步机跑步运动的运动强度。年龄在29岁至38岁之间的受试者参与了不同设定点的实验，以确认系统对模型间和模型内不确定性的适应性。在最后一项实时实验中也证实了系统在外部干扰下的期望性能，该实验展示了一名用户在运动期间携带10公斤的袋子然后取下它的情况。
与传统控制方法相比，所提出的自适应控制器在模型间和模型内不确定性以及外部干扰下实现了更好的心率跟踪性能。所开发的系统能够自动适应各种个体锻炼者和一系列运动强度。
