Parkinson's Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data
This work addresses real-time symptom detection for Parkinson's disease patients, though it appears incremental as it applies a hybrid deep learning method to an existing dataset.
The paper tackled detecting Freezing of Gait (FoG) in Parkinson's disease patients using wearable sensor data, achieving 92.6% accuracy and 80.9% F1 score with a Transformer Encoder-Bi-LSTM fusion model.
Freezing of gait (FoG) is a special symptom found in patients with Parkinson's disease (PD). Patients who have FoG abruptly lose the capacity to walk as they normally would. Accelerometers worn by patients can record movement data during these episodes, and machine learning algorithms can be useful to categorize this information. Thus, the combination may be able to identify FoG in real time. In order to identify FoG events in accelerometer data, we introduce the Transformer Encoder-Bi-LSTM fusion model in this paper. The model's capability to differentiate between FoG episodes and normal movement was used to evaluate its performance, and on the Kaggle Parkinson's Freezing of Gait dataset, the proposed Transformer Encoder-Bi-LSTM fusion model produced 92.6% accuracy, 80.9% F1 score, and 52.06% in terms of mean average precision. The findings highlight how Deep Learning-based approaches may progress the field of FoG identification and help PD patients receive better treatments and management plans.