An anomaly detection technique in wearable wireless monitoring systems for studies of gait freezing in Parkinson's disease

Wearable monitoring systems have been in need for studies of gaits especially freezing of gait detection in patients with Parkinson's disease. The causality of gait freezing is still not fully understood. The histogram of gait freezing is the key assessment of the disease, thus monitoring them in patients' daily life is much appreciated. A real-Time signal processing platform for wearable sensors can help record freezing time instances. However, current monitor systems are calibrated with offline training (patient-dependent) that is cumbersome and time-consuming. In this work, by using acceleration data and spectral analysis, we propose an online/real-Time detection technique. Periods of low acceleration and low spectral coherence are identified and patient-independent parameters are then extracted. Using this set of new features, we validated our method by comparing it with clinicians' labels. The proposed approach achieved an overall mean (±SD) sensitivity (specificity) of 87 ± 0.3% (94±0.3%). To our best knowledge, this is the best performance for automated subject-independent approaches.