| Warning System Based on Smart Sensors to Correct Forearm Movement Form |
| Paper ID : 1242-SPORTCONGRESS |
| Authors |
|
Shahin Behzadi1, Mostafa Irannejad *2, Peyman Aghaie Ataabadi3 1Department of Sport Engineering, Faculty of Engineering Sciences, College of Engineering, University of Tehran 2Faculty of Engineering Sciences, College of Engineering, University of Tehran 3Department of Sports Pathology and Biomechanics, Faculty of Sports and Health Sciences, University of Tehran |
| Abstract |
| Introduction: The rapid expansion of resistance training and home-based fitness routines has led to a growing number of injuries caused by poor movement execution. The biceps curl, as a fundamental upper-limb exercise, is highly prone to form deviations, highlighting the need for a reliable, real-time monitoring system. Methods: In this study, a wearable smart warning system was designed and implemented using inertial sensing technology to detect and correct technical errors during the biceps curl movement. A single Inertial Measurement Unit (IMU) was mounted on the forearm, and its accelerometer and gyroscope data were transmitted via Bluetooth Low Energy (BLE) to MATLAB for processing. Custom signal-processing algorithms were developed to filter noise, segment movement phases, and extract biomechanical parameters, including range of motion (ROM), time under tension (TUT), angular velocity, and vibration intensity. The proposed system was validated through experimental tests with 30 participants of varying training experience under controlled exercise conditions. Results: The system demonstrated a repetition detection accuracy exceeding 95% and effectively identified key performance deviations such as reduced ROM, excessive lifting speed, and instability due to fatigue. Real-time visual and auditory feedback enabled immediate correction of form, and statistical analysis revealed strong agreement between system-generated metrics and expert coach evaluations. The fusion of accelerometer and gyroscope data significantly enhanced signal stability, while user feedback confirmed that the device was comfortable, responsive, and practical for everyday training. Conclusion: The developed prototype successfully integrates wearable sensors with intelligent data processing to deliver an accurate, user-friendly, and cost-effective monitoring tool for strength exercises. Its implementation can help minimize technique-related injuries and improve training efficiency without constant supervision. Future research should focus on expanding the algorithm to multi-joint movements and integrating on-board computation for fully autonomous wearable coaching systems. |
| Keywords |
| Wearable sensors, Inertial measurement, Exercise monitoring, Human motion analysis, Smart fitness systems |
| Status: Abstract Accepted (Oral Presentation) |
