Gait Event Detection in Controlled and Real-Life Situations: Repeated Measures From Healthy Subjects

A benchmark and time-effective computational method is needed to assess human gait events in real-life walking situations using few sensors to be easily reproducible. This paper fosters a reliable gait event detection system that can operate at diverse gait speeds and on diverse real-life terrains by detecting several gait events in real time. This detection only relies on the foot angular velocity measured by a wearable gyroscope mounted in the foot to facilitate its integration for daily and repeated use. To operate as a benchmark tool, the proposed detection system endows an adaptive computational method by applying a finite-state machine based on heuristic decision rules dependent on adaptive thresholds. Repeated measurements from 11 healthy subjects (28.27 ± 4.17 years) were acquired in controlled situations through a treadmill at different speeds (from 1.5 to 4.5 km/h) and slopes (from 0% to 10%). This validation also includes heterogeneous gait patterns from nine healthy subjects (27 ± 7.35 years) monitored at three self-selected paces (from 1 ± 0.2 to 2 ± 0.18 m/s) during forward walking on flat, rough, and inclined surfaces and climbing staircases. The proposed method was significantly more accurate ( ${p}>\textsf {0.9925}$ ) and time effective (< 30.53 ± 9.88 ms, ${p}>\textsf {0.9314}$ ) in a benchmarking analysis with a state-of-the-art method during 5657 steps. Heel strike was the gait event most accurately detected under controlled (accuracy of 100%) and real-life situations (accuracy > 96.98%). Misdetection was more pronounced in middle mid swing (accuracy > 90.12%). The lower computational load, together with an improved performance, makes this detection system suitable for quantitative benchmarking in the locomotor rehabilitation field.