Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
المؤلفون المشاركون
Pezarat-Correia, Pedro C.
Gamboa, Hugo
Ramos, G.
Vaz, J. R.
Mendonça, G. V.
Rodrigues, J.
Alfaras, M.
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-07
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism.
Fatigue can be seen as a subjective or objective phenomenon.
Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon.
Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline.
Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries.
In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements.
Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier.
The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached.
Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ramos, G.& Vaz, J. R.& Mendonça, G. V.& Pezarat-Correia, Pedro C.& Rodrigues, J.& Alfaras, M.…[et al.]. 2020. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ramos, G.…[et al.]. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ramos, G.& Vaz, J. R.& Mendonça, G. V.& Pezarat-Correia, Pedro C.& Rodrigues, J.& Alfaras, M.…[et al.]. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186298
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر