Improvement of Performance in Freezing of Gait detection in Parkinson's Disease using Transformer networks and a single waist-worn triaxial accelerometer

Freezing of gait (FOG) is one of the most incapacitating symptoms in Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. The presence of FOG may lead to falls and a loss of independence with a consequent reduction in the quality of life. Wearable technology and artificial intelligence have been used for automatic FOG detection to optimize monitoring. However, differences between laboratory and daily-life conditions present challenges for the implementation of reliable detection systems. Consequently, improvement of FOG detection methods remains important to provide accurate monitoring mechanisms intended for free-living and real-time use. This paper presents advances in automatic FOG detection using a single body-worn triaxial accelerometer and a novel classification algorithm based on Transformers and convolutional networks. This study was performed with data from 21 patients who manifested FOG episodes while performing activities of daily living in a home setting. Results indicate that the proposed FOG -Transformer can bring a significant improvement in FOG detection over the reproduction of related approaches based on machine and deep learning (i.e., from 0.916 to 0.957 in the AUC metric compared with the baseline, with a corresponding sensitivity, specificity, and precision of 0.842, 0.939 and 0.617, respectively) using a leave-one-subject-out cross-validation (LOSO CV). These results present opportunities for the implementation of accurate monitoring systems for use in ambulatory or home settings.

Automated summary

This study presents advances in automatic FOG detection using wearable technology and artificial intelligence, which can accurately monitor freezing of gait in Parkinson's disease patients in real-time daily-life settings. The proposed FOG-Transformer classification algorithm showed significant improvement in FOG detection compared to existing machine and deep learning approaches.