کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5707791 | 1603838 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms
ترجمه فارسی عنوان
یک بررسی سیستماتیک از روش های تجزیه و تحلیل راه رفتن بر اساس سنسورهای اینرسی و الگوریتم های سازگاری
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کلمات کلیدی
سینماتیک گیتار، هوش مصنوعی، الگوریتم های یادگیری ماشین، واحد اندازه گیری درونی، شتاب سنج،
موضوعات مرتبط
علوم پزشکی و سلامت
پزشکی و دندانپزشکی
ارتوپدی، پزشکی ورزشی و توانبخشی
چکیده انگلیسی
The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation. Articles were identified through searches of the main databases, which were encompassed from 1968 to October 2016. We have identified 22 studies that met the inclusion criteria. The included papers were analyzed due to their data acquisition and processing methods with specific questionnaires. Concerning the data acquisition, the mean score is 6.1 ± 1.62, what implies that 13 of 22 papers failed to report relevant outcomes. The quality assessment of AI algorithms presents an above-average rating (8.2 ± 1.84). Therefore, AI algorithms seem to be able to support gait analysis based on inertial sensor data. Further research, however, is necessary to enhance and standardize the application in patients, since most of the studies used distinct methods to evaluate healthy subjects.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Gait & Posture - Volume 57, September 2017, Pages 204-210
Journal: Gait & Posture - Volume 57, September 2017, Pages 204-210
نویسندگان
Rafael Caldas, Marion Mundt, Wolfgang Potthast, Fernando Buarque de Lima Neto, Bernd Markert,