Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may\ud
be the most incapacitating. FOG episodes may result in falls and reduce patients’\ud
quality of life. Accurate assessment of FOG would provide objective information\ud
to neurologists about the patient’s condition and the symptom’s characteristics,\ud
while it could enable non-pharmacologic support based on rhythmic\ud
cues.\ud
This paper is, to the best of our knowledge, the first study to propose a\ud
deep learning method for detecting FOG episodes in PD patients. This model\ud
is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach\ud
was evaluated using data collected by a waist-placed inertial measurement unit\ud
from 21 PD patients who manifested FOG episodes. These data were also employed\ud
to reproduce the state-of-the-art methodologies, which served to perform\ud
a comparative study to our FOG monitoring system.\ud
The results of this study demonstrate that our approach successfully outperforms\ud
the state-of-the-art methods for automatic FOG detection. Precisely, the\ud
deep learning model achieved 90% for the geometric mean between sensitivity\ud
and specificity, whereas the state-of-the-art methods were unable to surpass the\ud
83% for the same metric.Peer ReviewedPostprint (published version
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