Multi-Modal Feature Set-Based Detection of Freezing of Gait in Parkinson’s Disease Patients Using SVM

Parkinson’s disease (PD) ranks as the second most prevalent neurodegenerative disorder, leading to motor complications, particularly in the elderly. Among its many symptoms, Freezing of Gait (FoG) affects nearly 50% of PD patients globally, leading to an increased risk of falls. Despite the widespread use of uni-modal FoG detection, it does not produce satisfactory results. This work aims to detect FoG using a multi-modal feature set. A publicly available multi-modal dataset was used, comprising data collected from 12 patients using various sensors, including Electroencephalogram (EEG), Electromyography (EMG), tri-axial ACC (accelerometers and gyroscopes), and Skin Conductance (SC) sensors. First, the data were pre-processed and segmented into 3-second windows with 90% overlap. The segmented windows were labeled based on the various percentage of FoG points (PFG) thresholds. Then, time-, frequency-, and time-frequency-based features were extracted and fed to the Support Vector Machine (SVM) model. These features included statistical, wavelet transform-based Shannon entropy, and Fast Fourier transform-based features. The results demonstrated that the multi-modal combination EMG + ACC, with a PFG of 0.75, outperformed all other sensor combinations, achieving the f1 score (binary) of 98.82%, highlighting the significant impact of ACC on FoG detection. It surpasses the state-of-art study, which reported an f1 score of 93.49%. Additionally, the combination of the right shank, waist, and arm within the acceleration sensors yielded the best f1 score of 99.19%. Furthermore, this work shows the power of synergistic concatenation of uni-modal data to enhance the model performance for FoG detection tasks.