Data Augmentation by Synthesizing IMU Data of Physiotherapeutic Exercises using Musculoskeletal Models

Abstract This study presents a novel approach to augment physiotherapeutic exercise datasets by synthesizing realistic Inertial Measurement Unit (IMU) data. The augmented dataset is used to improve the performance of a deep learning based exercise evaluation system. The approach is demonstrated using the deep squat exercise from the Functional Movement Screening (FMS) protocol. By integrating musculoskeletal simulation and leveraging knowledge of potential movement errors based on FMS evaluation criteria, we aim to produce synthetic data that closely mimics human movement. Our evaluation demonstrates that training a combination of a Convolutional Neural Network with a Long-Short-Term-Network (CNN-LSTM) with both real and synthesized data significantly improves the model’s performance, especially in generalizing to unknown subjects. However, limitations such as the approach’s specificity to the deep squat exercise suggest the need for a more adaptable method. Future work will focus on refining the synthesis process to ensure a broader applicability to various exercises. This research contributes to advancing automated physiotherapeutic exercise evaluation, highlighting the importance of synthetic data in achieving better performing and more generalizable models.