A novel approach to identify the fingerprint of stroke gait using deep unsupervised learning

BackgroundThe gait pattern results from a complex interaction of several body parts, orchestrated by the (central) nervous system that controls the active and passive systems of the body. An impairment of gait due to a stroke results in a decline in quality of life and independence. Setting up efficient gait training requires an objective and wholesome assessment of the patients movement pattern to target individual gait alterations. However, current assessment tools are limited in their ability to capture the complexity of the movement and the amount of data acquired during gait analysis. AimsIn this study, we explore the potential of variational autoencoders (VAE) to learn and recognise different gait patterns within both, pathologic and healthy gait. MethodsFor this purpose, the lower-limb joint angles of 71 participants (29 stroke survivors, 42 healthy controls) were used to train and test a VAE. ResultsThe good reconstruction results (range r = 0.52 - 0.91, average normalized RMSE 23.36 % {+/-} 4.13) indicate that VAEs extract meaningful information from the gait pattern. Furthermore, the extracted latent features are sensitive enough to distinguish between the gait patterns of stroke survivors and a healthy cohort (p<0.001). ConclusionsThe presented approach allows the assessment of gait data in an objective and wholesome manner, thereby integrating the individual characteristics of each persons gait, making it a suitable tool for monitoring the progress of rehabilitation efforts.