A Novel Dynamic Latent Variables-Based Framework for Enhancing Freezing of Gait Detection in Parkinson's Disease Patients

Freezing of Gait (FOG) is one of the most severe symptoms of Parkinson's disease (PD), which often lead to life-threatening falls. Wearable sensor-based technologies coupled with data driven methods have advanced the detection of FOG in a timely fashion. However, most existing monitoring methods overlook the dynamics of processes when extracting effective information from high-dimensional sensor data. To tackle these problems, we develop a novel framework for FOG detection by integrating Dynamic Latent Variable (DLV)-based dimensionality reduction strategies and personalized monitoring. First, a multi-channel sliding window mechanism is adopted to extract the multiple potentially effective feature sequences. Second, an interpretable DLV-based method incorporating time-lagged terms is designed for the subspace representation of complex high-dimensional sequences. Third, the extracted DLVs are integrated with threshold-based methods or the Statistical Process Control (SPC) method for anomaly detection. We identified distinct variations in gait patterns among individuals, underscoring the importance of personalized approaches. The proposed framework demonstrates its effectiveness in FOG detection via validating on real world dataset, achieving a sensitivity of $\mathbf {0.845} \pm \mathbf {0.254}$ and a specificity of $\mathbf {0.842} \pm \mathbf {0.211}$.