Development and Technical Feasibility Evaluation of VertoTrack: An IoT-Based Wearable Head-Orientation Tracker for Rehabilitation Monitoring

Objective motion monitoring is frequently absent from unsupervised home-based vestibular and balance rehabilitation programs, which makes it challenging for patients to adhere to recommended movement boundaries and for clinicians to assess performance. This research introduces VertoTrack, a low-cost wearable Internet of Medical Things (IoMT) prototype that can provide threshold-based aural alarms and quantify angular head displacement in real-time. The system combines an ESP32 microcontroller on a headband with an MPU6050 inertial measurement unit (IMU) to provide pitch and roll data via Wi-Fi to a Blynk mobile dashboard. Using 60 paired pitch and 60 paired roll measurements compared to a digital inclinometer application, sensor performance was verified under three movement circumstances (safe: <15°, warning: 15°–25°, danger: ≥25°). In every testing zone, Mean Absolute Errors (MAE) stayed below 0.90°. Both roll (r = 0.9987, p < 0.001) and pitch (r = 0.9995, p < 0.001) showed strong linear agreement. There was little systematic bias, according to Bland-Altman analysis (roll: -0.07°, 95% Pitch: 0.26°, 95%; LoA: -2.10° to 1.97° LoA: -1.06° to 1.58°). During benchtop testing, the threshold classification system produced two-tier buzzer alerts with 100% accuracy. These results confirm the technical feasibility of VertoTrack for objective head-orientation monitoring during controlled exercise routines.