Automated freezing of gait assessment with deep learning and data augmentation from simulated inertial measurement unit data

Freezing of gait (FOG) is a common and severe symptom of Parkinson’s disease (PD). Due to the complex underlying pathophysiology, FOG is difficult to assess, hampering further insight into this phenomenon. Inertial measurement units (IMUs) may enable FOG assessment during everyday life, but lack of standardization, e.g., the number and position of the IMUs, complicates an objective comparison of automatic FOG assessment algorithms. We propose a multi-stage temporal dilated convolutional model to automatically assess FOG based on IMU data. We collected simultaneous optical motion capture (MoCap) and IMU data of ten people with PD and FOG. We devised a simulation pipeline, i.e., generating IMU data from MoCap data, to objectively compare our approach to two state-of-the-art FOG assessment models. The comparison was performed for five simulated IMU configurations, ranging from 1 to 7 IMUs. The results show that our approach outperforms the two state-of-the-art methods on most of the simulated IMU configurations. The complete lower-body IMU setup of 7 IMUs (pelvis and both sides of the talus, tibia, and femur) enables the best FOG detection performance. Lastly, we show that our model trained by incorporating simulated IMU data enabled significantly improved FOG detection performance than our model trained only with real IMU data. In doing so, we demonstrate that retrospective MoCap datasets can be re-used to train expressive IMU-based FOG assessment models, reducing the required amount of dedicated and labor-intensive IMU data collection experiments.