  Freezing-of-gait a mysterious symptom of Parkinsons disease and defined as a
sudden loss of ability to move forward. Common treatments of freezing episodes
are currently of moderate efficacy and can likely be improved through a
reliable freezing evaluation. Basic-science studies about the characterization
of freezing episodes and a 24/7 evidence-support freezing detection system can
contribute to the reliability of the evaluation in daily life. In this study,
we analyzed multi-modal features from brain, eye, heart, motion, and gait
activity from 15 participants with idiopathic Parkinsons disease and 551
freezing episodes induced by turning in place. Statistical analysis was first
applied on 248 of the 551 to determine which multi-modal features were
associated with freezing episodes. Features significantly associated with
freezing episodes were ranked and used for the freezing detection. We found
that eye-stabilization speed during turning and lower-body trembling measure
significantly associated with freezing episodes and used for freezing
detection. Using a leave-one-subject-out cross-validation, we obtained a
sensitivity of 97%+/-3%, a specificity of 96%+/-7%, a precision of 73%+/-21%, a
Matthews correlation coefficient of 0.82+/-0.15, and an area under the
Precision-Recall curve of 0.94+/-0.05. According to the Precision-Recall
curves, the proposed freezing detection method using the multi-modal features
performed better than using single-modal features.
