A smartphone-based architecture to detect and quantify freezing of gait in Parkinson’s disease

Introduction
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-Bächlin 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-Bächlin 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-Bächlin 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.