کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6205289 1603845 2016 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A smartphone-based architecture to detect and quantify freezing of gait in Parkinson's disease
ترجمه فارسی عنوان
یک معماری مبتنی بر گوشی های هوشمند برای شناسایی و اندازه گیری راه رفتن در بیماری پارکینسون
کلمات کلیدی
یخ زدن راه رفتن، گوشی های هوشمند، شتاب سنج، سیستم پوشیدنی بیماری پارکینسون،
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی ارتوپدی، پزشکی ورزشی و توانبخشی
چکیده انگلیسی


- A smartphone application for real-time detection of freezing of gait is presented.
- The application is tested on 20 patients with Parkinson's disease and FOG.
- An innovative algorithm is compared to a traditional one.
- The architecture is highly reliable in FOG detection, even if it occurs at turning.

IntroductionThe 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.MethodsIn 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.ResultsA 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).ConclusionResults 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Gait & Posture - Volume 50, October 2016, Pages 28-33
نویسندگان
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