title: Advanced portable remote monitoring system for the regulation of treadmill running exercises.
abstract: OBJECTIVE
  This study aims to develop an advanced portable remote monitoring system to supervise high intensity treadmill exercises.
  
  
  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 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology, Sydney, 15 Broadway, Ultimo, NSW 2007, Australia. Electronic address: tuannghia.nguyen@uts.edu.au.
authors.forename: Tuan Nghia
authors.initials: TN
authors.lastname: Nguyen
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology, Sydney, 15 Broadway, Ultimo, NSW 2007, Australia. Electronic address: steven.su@uts.edu.au.
authors.forename: Steven
authors.initials: S
authors.lastname: Su
authors.affiliation: Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, PO Box 76, Epping, NSW 1710, Australia. Electronic address: branko.celler@csiro.au.
authors.forename: Branko
authors.initials: B
authors.lastname: Celler
authors.affiliation: Faculty of Engineering and Information Technology, University of Technology, Sydney, 15 Broadway, Ultimo, NSW 2007, Australia. Electronic address: hung.nguyen@uts.edu.au.
authors.forename: Hung
authors.initials: H
authors.lastname: Nguyen
mesh_terms: D000328:Adult; D000465:Algorithms; D040421:Cell Phone; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D020407:Internet; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012420:Running / Q000502:physiology*
keywords: Artificial neural network; Feedback error learning; Heart rate regulation; On-line adaptive control