Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs

Justin Amadeus Albert, Pawel Glöckner, Bjarne Pfitzner, Bert Arnrich

Abstract: Machine learning, especially deep learning, offers great potential for medical applications. However, deep learning algorithms need a vast amount of training data. Especially in the medical domain, it is challenging to collect larger datasets. Access to patients can be limited, and data recording is mainly bound to laboratory settings requiring expertise from medical professionals. When involving a healthy control group, datasets are often unbalanced, with most data belonging to the control group. This paper proposes a data augmentation method to generate pose data of repetitive rehabilitation exercises trained on a specific population, e.g., a specific neurological disease. Our method is based on a generative adversarial network (GAN) that uses convolutional and long short-term memory (LSTM) layers. We evaluated our method using a dataset that contains rehabilitation exercises from stroke and Parkinson’s disease patients and a healthy control group. We demonstrated that a classifier trained using our augmentation method could distinguish between healthy, stroke, and Parkinson’s disease patients with an accuracy of 81%. In contrast, the same classifier achieved only 75% when using a standard resampling technique.