Bilateral Leg Stepping Coherence as a Predictor of Freezing of Gait in Patients With Parkinson's Disease Walking With Wearable Sensors

Freezing of Gait (FOG) is among the most debilitating symptoms of Parkinson's Disease (PD), characterized by a sudden inability to generate effective stepping. In preparation for the development of a real-time FOG prediction and intervention device, this work presents a novel FOG prediction algorithm based on detection of altered interlimb coordination of the legs, as measured using two inertial movement sensors and analyzed using a wavelet coherence algorithm. Methods: Fourteen participants with PD (in OFF state) were asked to walk in challenging conditions (e.g. with turning, dual-task walking, etc.) while wearing inertial motion sensors (waist, 2 shanks) and being videotaped. Occasionally, participants were asked to voluntarily stop (VOL). FOG and VOL events were identified by trained researchers based on videos. Wavelet analysis was performed on shank sagittal velocity signals and a synchronization loss threshold (SLT) was defined and compared between FOG and VOL. A proof-of-concept analysis was performed for a subset of the data to obtain preliminary classification characteristics of the novel measure. Results: 128 FOG and 42 VOL episodes were analyzed. SLT occurred earlier for FOG (MED = 1.81 sec prior to stop, IQR = 1.57) than for VOL events (MED = 0.22 sec, IQR = 0.76) (Z =-4.3, p < 0.001, ES = 1.15). These time differences were not related with measures of disease severity. Preliminary results demonstrate sensitivity of 98%, specificity of 42% (mostly due to 'turns' detection) and balanced accuracy of 70% for SLT-based prediction, with good differentiation between FOG and VOL. Conclusions: Wavelet analysis provides a relatively simple, promising approach for prediction of FOG in people with PD.