Article

The freezing of gait (FOG) is a common and highly distressing motor
symptom in patients with Parkinson's Disease (PD). Effective management of FOG is
difficult given its episodic nature, heterogeneous manifestation and limited
responsiveness to drug treatment. METHODS: In order to verify the acceptance of a
smartphone-based architecture and its reliability at detecting FOG in real-time,
we studied 20 patients suffering from PD-related FOG.
They were asked to perform
video-recorded Timed Up and Go (TUG) test with and without dual-tasks while
wearing the smartphone. Video and accelerometer recordings were synchronized in
order to assess the reliability of the FOG detection system as compared to the
judgement of the clinicians assessing the videos. The architecture uses two
different algorithms, one applying the Freezing and Energy Index (Moore-Bachlin
Algorithm), and the other adding information about step cadence, to algorithm 1.
RESULTS: A total 98 FOG events were recognized by clinicians based on video
recordings, while only 7 FOG events were missed by the application. Sensitivity
and specificity were 70.1% and 84.1%, respectively, for the Moore-Bachlin
Algorithm, rising to 87.57% and 94.97%, respectively, for algorithm 2 (McNemar
value=28.42; p=0.0073). CONCLUSION: Results confirm previous data on the
reliability of Moore-Bachlin Algorithm, while indicating that the evolution of
this architecture can identify FOG episodes with higher sensitivity and
specificity. An acceptable, reliable and easy-to-implement FOG detection system
can support a better quantification of the phenomenon and hence provide data
useful to ascertain the efficacy of therapeutic approaches.
CI - Copyright (c) 2016 Elsevier B.V. All rights reserved.

Langue : ANGLAIS