Objective: The aim of the present study is to objectively detect and quantify freezing of gait (FOG) and falls in Parkinson’s disease (PD) patients during everyday activities.

Background: FOG and falls are debilitating features in PD. There have been no satisfying methods to identify and quantify FOG and falls outside of the clinic.

Methods: Patients were selected from among 36 patients who participated in our previous prospective study on falls. We developed a motion recorder (body-fixed 3D accelerometer) with a long-lasting battery. Movements of recurrent PD fallers with severe FOG were recorded using the waist mounted device in the outpatient clinic and during their everyday activities. A newly developed freezing index (cross correlation calculation based on pattern matching) was calculated and compared with the previously validated index (ratio of power spectrum, 3-8 Hz/0.5-3Hz) by Moore et al.

Results: Characteristic patterns of acceleration signals were recorded for freezing and falls. Knee trembling was recorded as a rapid oscillation of acceleration, and the freezing index increased during knee trembling. In PD patients, actual falls in everyday life were also detected as abrupt trunk angle changes, and knee trembling was recorded when patients reported FOG-induced falls. The freezing index increased during the start and turning hesitations, similarly to the index calculated using previously validated method. In the previous method, the largest value of freezing index varied according to the directions analyzed.

Conclusions: Motion recording using our wearable sensor is useful for detecting FOG and falls in everyday life in PD freezers, and calculating the freezing index may improve the quantification of FOG.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/identifying-freezing-of-gait-and-falls-in-parkinsons-disease-patients-using-a-body-worn-sensor/