Accurate and quantitative assessment of a patient’s muscle recovery is essential for effective rehabilitation. Traditional methods often rely on subjective evaluation, which can lead to inconsistent results. In this study, authors present the design and development of a low-cost, sensor-based device to assess upper limb muscle recovery through push-force measurement. The system utilizes a Load Cell integrated with a mechanical frame to quantify muscle strength during arm flexion and extension exercises. Three key parameters—maximum force, time to reach maximum force, and endurance—are recorded to evaluate recovery progress. Data is transmitted in real time to a computer for analysis and visualization. Initial testing on healthy individuals demonstrated reliable performance and high accuracy, with a calibration error of only 0.01197%. The device provides a simple, effective, and economical tool for both patients and clinicians to monitor and adjust rehabilitation plans. Future improvements will focus on wireless connectivity, enhanced data processing, and broader clinical validation.

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Design and Validation of a Low-Cost System for Monitoring Hand Rehabilitation Progress

  • Nguyen Phan Kien,
  • Bui Viet Anh,
  • Nguyen Trong Hoang Anh,
  • Vi Tu Anh,
  • Do Minh Khoa,
  • Duc-Tan Tran,
  • Lam Sinh Cong,
  • Nguyen Canh Minh

摘要

Accurate and quantitative assessment of a patient’s muscle recovery is essential for effective rehabilitation. Traditional methods often rely on subjective evaluation, which can lead to inconsistent results. In this study, authors present the design and development of a low-cost, sensor-based device to assess upper limb muscle recovery through push-force measurement. The system utilizes a Load Cell integrated with a mechanical frame to quantify muscle strength during arm flexion and extension exercises. Three key parameters—maximum force, time to reach maximum force, and endurance—are recorded to evaluate recovery progress. Data is transmitted in real time to a computer for analysis and visualization. Initial testing on healthy individuals demonstrated reliable performance and high accuracy, with a calibration error of only 0.01197%. The device provides a simple, effective, and economical tool for both patients and clinicians to monitor and adjust rehabilitation plans. Future improvements will focus on wireless connectivity, enhanced data processing, and broader clinical validation.