Automatic gait event detection in pathologic gait using an auto-selection approach among concurrent methods

Background: Accurate gait event detection is crucial to analyze pathological gait data. Existing methods relying on marker trajectories were reported to be sensitive to different gait patterns, which is an inherent characteristic of pathologic gait. Research question: We propose a new approach based on auto-selection among different methods, original and taken from the literature. Methods: The auto-selection approach evaluates the accuracy of the implemented methods for both foot-strike and foot-off on all available events detected by the force platforms, independently, and automatically selects the most accurate one to be used on the whole gait session. Pathological gait data from 272 patients with cerebral palsy and idiopathic toe walking were used retrospectively to evaluate the accuracy of this approach. Three methods previously reported in literature together with original methods developed based on auto-correlation were implemented and constituted our auto-selection approach. The accuracy and precision were compared to a recently reported method based on deep events as it is the method that showed the best performance in literature. Results: Results showed that the proposed approach outperformed all implemented methods used alone, with an accuracy of - 2.0 ms and - 0.9 ms for foot strike and foot-off, respectively. Additionally, more than 99% and 93% of events detected were detected within 20 ms and 10 ms of accuracy, respectively. Significance: The proposed methodology has demonstrated to improve the accuracy and precision of gait event detection in gait analysis.