UNASSIGNED: Freezing of gait ( @entity6556 ) is common in @entity191 and strongly relates to falls. Current clinical @entity6556 assessments are @entity1 ' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this work, we aim to develop an automated @entity6556 detector for subject-independent. After extracting highly relevant features, we apply @entity154 detection techniques to detect @entity6556 events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 candidates, 36 candidates were selected using saliency and robustness criteria. We develop an @entity154 score detector (ASD) with adaptive thresholding to identify @entity6556 events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multi-channel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of 96% (79%). On the other hand, freezing index from the vertical @entity2270 was the best choice for a single input, achieving sensitivity (specificity) of 94% (84%) for ankle and 89% (94%) for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., 3s vs. 7.5s) and/or lower tolerance (e.g., 0.4s vs. 2s).

Freezing of Gait Detection in XXXX : A Subject-Independent Detector Using Anomaly Scores.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity154 :: ('MESH:D000013', 'Disease') :: ['anomaly'] @entity191 :: ('MESH:D010300', 'Disease') :: ['Parkinsonian gait'] @entity2270 :: ('MESH:C566610', 'Disease') :: ['axis'] @entity6556 :: ('161882', 'Gene') :: ['FoG']
@entity191 :: (MESH:D010300,Disease) :: ['Parkinson's Disease']
