Data Augmentation in Mobility Rehab Exercises of PD Patients
Parsa Riazi Bakhshayesh, Mehdi Ejtehadi, Saeed Behzadipour · 2021
Human activity recognition (HAR) systems are used to monitor Parkinson’s disease (PD) patients’ mobility progress. Machine learning methods are commonly used for the development of the HAR. These methods, however, require large amount of data collected from the human subjects. Data augmentation is an affordable alternative for facilitating the development of such systems by producing similar data from actual data collected from the human subjects. In this work, three methods of data augmentation: time warping, amplitude warping and linear combination were carried out on acceleration and gyroscope signals of 6 Inertial Measurement Unit (IMU) sensors. Data was collected from a subject performing 21 therapeutic activities. To evaluate and compare the methods, a 3D human body model was utilized to visualize the motions. Then, a group of 18 individuals familiar with human motion simulation, graded the activities produced by the augmented data. The results showed that overall, time warping is the best in maintaining the structure of the activity while creating minor variations. However, it almost fails in walking activities which are more dynamic. Additionally, amplitude warping was seen to be a better choice for walking down the stairs, passing obstacle with left foot, as well as standing posture activities.