Real-Time Detection of Gait Events by Recurrent Neural Networks

This paper proposes a gait detection model that can recognize important gait events in real time. Human gaits are periodic, with each gait cycle containing the important gait events of mid-swing, heel-strike (HS), and toe-off. The correct identification of different gait patterns caused by stroke or progressive neurodegenerative Parkinson's disease could ensure that the patients receive appropriate treatment and rehabilitation strategies. However, online detection of gait events can be challenging because each person has their own walking patterns and speeds. This paper applies recurrent neural networks (RNNs) to develop a model that can instantly detect important gait events in any subject. We collected clinical gait data and used them to develop an RNN model for real-time detection of HS. The model correctly recognized HS events with an average success rate of 98.84% and an average delay of 0.024 s in the laboratory environment. We then applied the model to three different groups of subjects: healthy elderly subjects, stroke patients, and patients with Parkinson's disease. The developed RNN model also correctly recognized HS events in all three groups with an average accuracy of more than 99.65%, even though the subjects had very different walking patterns. In the future, the developed gait detection model can be integrated with real-time rehabilitation systems to provide patients with repetitive guidance using clinician-determined cures for enhanced clinical gait training.