Quantitative Effort Tracking and Post-Impact Concussion Screening Using a Bar-Mounted Inertial Sensor
Faculty Mentor Information
Dr. Zhangxian (Dan) Deng, Boise State University
Presentation Date
7-15-2026
Abstract
Across fitness training, competitive strength sports, and physical rehabilitation, regulating effort is important, but it is rarely measured objectively in resistance training. Lifters rely on subjective judgment to decide how hard to train, even though effort can change with fatigue, recovery, motivation, and pain tolerance. This project developed a bar-mounted motion-sensing system that uses inertial sensors to measure bar movement across three axes. The firmware was programmed to process motion data from an Arduino-based board attached to a bar clip, measuring bar speed, rep duration, velocity loss, load, rep position, and movement stability. It then calculates a weighted function of these variables and outputs a simple effort score. The app displays set summaries and stores session history. Across 270 sets of bench press, squat, and deadlift, the system missed only 2 reps. The same sensing approach was extended to post-impact concussion screening through controlled squat trials, since concussion research has shown that side-to-side sway can increase under cognitive load. By comparing side-to-side movement against a baseline, the system can flag unusual movement patterns. In simulated-instability testing across foam, air, and water-based unstable surfaces, 40 normal and 40 unstable squat trials were compared, and unstable trials triggered side-to-side movement flags.
Quantitative Effort Tracking and Post-Impact Concussion Screening Using a Bar-Mounted Inertial Sensor
Across fitness training, competitive strength sports, and physical rehabilitation, regulating effort is important, but it is rarely measured objectively in resistance training. Lifters rely on subjective judgment to decide how hard to train, even though effort can change with fatigue, recovery, motivation, and pain tolerance. This project developed a bar-mounted motion-sensing system that uses inertial sensors to measure bar movement across three axes. The firmware was programmed to process motion data from an Arduino-based board attached to a bar clip, measuring bar speed, rep duration, velocity loss, load, rep position, and movement stability. It then calculates a weighted function of these variables and outputs a simple effort score. The app displays set summaries and stores session history. Across 270 sets of bench press, squat, and deadlift, the system missed only 2 reps. The same sensing approach was extended to post-impact concussion screening through controlled squat trials, since concussion research has shown that side-to-side sway can increase under cognitive load. By comparing side-to-side movement against a baseline, the system can flag unusual movement patterns. In simulated-instability testing across foam, air, and water-based unstable surfaces, 40 normal and 40 unstable squat trials were compared, and unstable trials triggered side-to-side movement flags.