Automatic detection of gait events using recurrent neural networks

Clinical gait analysis is a key tool for diagnosis and rehabilitation planning in patients with motor disorders; however, accurate and automatic detection of gait events remains a challenging task in resource-limited settings. Force plates are considered the gold standard for automatic gait event detection, but their application is limited in cases of pathological gait patterns and when patients use assistive rehabilitation devices. This paper presents an approach to automatic detection of gait events in children with pathological gait using recurrent neural networks. The presented methodology effectively identifies key gait events (heel strike and toe off). The study used kinematic data from patients with gait disorders, collected using an optical motion capture system under various conditions: barefoot walking, in orthopedic footwear, with orthoses, and other technical rehabilitation aids. Four models were trained to detect gait events (one for each leg and event type). The models demonstrated high sensitivity with small time delays between predicted and actual events. The proposed method can be used in clinical practice to automate data annotation and reduce processing time for gait analysis results.

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