Improving Freezing of Gait Detection and Prediction Using ML and Transformers

Detecting Freezing of Gait (FOG) is crucial for Parkinson's disease (PD). Several research works have been published on FOG detection and prediction using machine learning (ML) and limited accelerometer data. FOG data collection is challenging and generally conducted on a limited number of persons as freezing occurs randomly, unlike walking which is a recurrent activity. This makes directly applying ML inefficient. In this paper, to improve previously published results, we apply three different ML algorithms: Linear Discriminant Analysis (LDA), Extreme Gradient Boosting (XGB), and Extra Trees (ET), in addition to a Transformer model to detect and predict FOG. This is achieved by modelling the publicly available DAPHNet dataset, which contains accelerometer readings from three wearable sensors. The data quality is first improved by using data augmentation, data balancing, and feature extraction. Our results show that accuracy (ACC), along with other metrics like sensitivity (SEN), specificity (SPE), and F1-score (F1) is important to quantify the performance of our results. From FOG detection results, we can see that XGB and ET algorithms provide 100% ACC, and slightly lower performance, 99.96% with LDA. For FOG prediction, our results prove we can achieve 99.21%, 96.40%, 96.21%, and 94.70% ACC with LDA, XGB, ET, and Transformer, respectively. These results outperform previously published results on the same dataset.