Enhancing Freezing of Gait Detection in Parkinson's Through Fine-Tuned Deep Learning Models

Freezing of Gait (FoG) is a common and disabling symptom in Parkinson's Disease (PD), characterized by a sudden and temporary inability to initiate or continue walking. FoG arises from various factors such as environmental triggers, or physiological status of people with Parkinson's. Traditional methods for preventing or alleviating FoG have limitations, prompting exploration into new technologies, such as the combination of sensing technologies and Deep Learning (DL) and Machine Learning (ML) algorithms. However, recognizing FoG with sensors and ML/DL poses challenges, such as the generalizability of the FoG recognition models over different individuals. Moreover, current approaches often require extensive time and effort to personalize the FoG recognition models. To mitigate these challenges, we propose a system that reduces the workload for creating personalized models through a fine-tuning approach. Our methodology has undergone rigorous testing in a subject-independent setup on a self-collected dataset of 22 subjects. Through the fine-tuning phase, we observed a remarkable average increase of up to 20.9% in F1-score performance compared to the training and testing approach without fine-tuning.