کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6947901 1450715 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system
ترجمه فارسی عنوان
طبقه بندی کار از اندازه گیری ضربان قلب با استفاده از سیستم استنتاج فازی سازگار
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر تعامل انسان و کامپیوتر
چکیده انگلیسی
In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (V˙O2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Ergonomics - Volume 54, May 2016, Pages 158-168
نویسندگان
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