Improved running gait parameter estimation from single foot-mounted IMU data based on refined event detection

Background Inertial measurement units (IMUs) enable portable gait monitoring, yet their accuracy relies on precise event detection. Conventional algorithms using raw signal peaks often fail during running due to speed variations and diverse foot-strike patterns. Therefore, adaptive detection strategies are required for high precision running gait analysis. Methods This study proposes MFD-GED (multi-sensor fusion with dynamic gait event detection), a refined method for accurate running gait analysis via a single foot-mounted IMU. To enhance event detection, the framework fuses acceleration- and angular-velocity features and employs a parametric strategy to identify initial contact (IC), terminal contact (TC) and mid-stance (MS), respectively. The algorithm then computes a comprehensive set of gait parameters relevant to running biomechanics assessment. Data were collected from 15 healthy male runners (age: 24.1 ± 1.1 years) performing 10-m running trials. The proposed method was benchmarked against a conventional angular-velocity-based gait-segmentation algorithm (AVGS) and validated using a laboratory reference (LAB) comprising an optical motion-capture and force-plate system. Pearson correlation coefficients (Pearson’s r), intraclass correlation coefficients (ICCs), and Bland-Altman analysis were used to assess concurrent validity, while paired t-tests and Cohen’s d were employed to evaluate the performance improvement over the AVGS method. Results The MFD-GED method demonstrated high concurrent validity against the LAB system (r = 0.743–0.991; ICC = 0.741–0.990). Compared to the AVGS method, systematic bias was reduced for spatial parameters ( p > 0.05 ), including stride velocity (−0.023 m/s vs. −0.012 m/s) and stride length (0.018 m vs. 0.009 m). For temporal parameters, bias significantly decreased ( p < 0.01 ; Cohen’s d = 1.62–2.20), specifically for contact time (0.057 s vs. 0.001 s) and flight time (−0.063 s vs. −0.003 s). Peak vGRF bias also decreased from −0.310 BW to 0.159 BW ( p < 0.01 ; Cohen’s d = 1.45). Furthermore, error standard deviations were reduced across all metrics. Conclusion This study validates an IMU framework improving running gait detection. Through sensor fusion, MFD-GED enables high-fidelity parameter estimation. While lab-validated for healthy young males, findings affirm its potential running for future gait monitoring tasks, aiming to offer a reliable tool for professionals in the field.