| Resumo: | The development of robust algorithms for human gait analysis are essential to evaluate the gait performance, and in many cases, crucial for diagnosing gait pathologies. This work proposes a new adaptive tool for human gait event detection in real-time, based on the angular velocity recorded from one gyroscope placed on the instep of the foot and in a finite state machine with adaptive decision rules. The signal was segmented to detect 6 events: Heel Strike (HS), Foot Flat (FF), Middle Mid-Stance (MMST), Heel-Off (HO), Toe-Off (TO), and Middle Mid-Swing (MMSW). The tool was validated with healthy subjects in ground-level walking using a treadmill, for different speeds (1.5 to 4.5 km/h) and slopes (0 to 10%). The results show that the tool is highly accurate and versatile for the detection of all events, as indicated by the values of accuracy, average delays and advances (HS: 99.96%,-7.95 ms, and 9.85 ms; FF: 99.48%,-4.95 ms, and 9.35 ms; MMST: 98.26%, 36.54 ms, and 16.38 ms; HO: 98.87%,-22.71 ms, and 18.62 ms; TO: 95.95%,-6.80 ms, 14.38 ms; MMSW: 96.06%,-3.45 ms; 0.15 ms, respectively). These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.
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