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Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals.

Guanlong Teng1, Yue Xu1, Feng Hong1

  • 1Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.

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Summary

MobiFit offers contactless fitness tracking using only ground-based cellular signals. This system accurately counts repetitions and recognizes exercise types for ubiquitous, device-free physical fitness monitoring.

Keywords:
cellular sensingcellular signalfreehand exercisemobile sensingwireless sensing

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Area of Science:

  • Human-Computer Interaction
  • Wearable Technology
  • Signal Processing

Background:

  • Freehand exercises enhance physical fitness but lack accessible, ubiquitous tracking systems.
  • Current fitness assistants often require specific devices or locations, limiting exercise options.

Purpose of the Study:

  • To develop MobiFit, a contactless freehand exercise assistant system.
  • To enable accurate repetition counting, exercise type recognition, and workout quality assessment without body attachments.

Main Methods:

  • Utilized a single cellular signal receiver on the ground to passively monitor base station signals.
  • Developed an analytic model based on experimental signal observations to address signal uncertainties.
  • Employed spectrogram analysis for repetition segmentation and low-frequency feature extraction for exercise type recognition.

Main Results:

  • Achieved 98.6% accuracy in repetition counting.
  • Demonstrated 94.1% accuracy in exercise type recognition.
  • Maintained a low repetition duration estimation error of under 0.3 seconds.

Conclusions:

  • MobiFit provides an accurate, contactless solution for freehand exercise monitoring.
  • The system is effective in diverse indoor and outdoor environments and supports multiple users.
  • MobiFit overcomes space constraints and deployment overheads associated with traditional fitness systems.