CDIO project on MaViCo heat compress for tackwondo players

Taekwondo is a traditional Korean martial arts sport which is currently practiced by many countries around the world. As claimed by the International Olympics Committee (2015), this sport made its debut in 1988 as a demonstration of Olympic sport in Korea and became an official medal sport in Sydney, Australia in the year 2000. The art of taekwondo develops posture, graceful movement, excellent coordination, and attention to detail. As a martial arts sport, taekwondo is characterized by its emphasis on dynamic techniques for taking mobile stances, speed, flexibility, and endurance in order to perform the whole process perfectly. Thus, athletes are required to have control over their shoulders, back, hips, knees, ankles, and feet when practicing this sport

Assessment of Heart Rate Variability Response in Children with Autism Spectrum Disorder using Machine Learning

Autism spectrum disorder (ASD) is a developmental disability that involves persistent challenges in social interaction, communication and behaviour. The purpose of this study is to apply a machine learning approach to differentiate between autistic and normal children and to evaluate the performance of different classifiers in the detection of autism disorder. Heart Rate Variability (HRV) analysis is one of the strategies used for ASD detection by assessing the autonomic nervous system (ANS), which serves as a biomarker for the autism phenotype. HRV can be derived from the photoplethysmogram (PPG). Logistic Regression, Linear Discriminant Analysis and a Cubic Support Vector Machine (SVM) were chosen to evaluate the performance of HRV features in differentiating between normal and autistic children. Three different combinations of features were selected out of 19 features in total. From the results, Logistic Regression was the best classifier to differentiate between autistic and normal children in a colour stimulus test, while Linear Discriminant Analysis was best suited in the baseline test. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future

Assessment of Heart Rate Variability Response in Children with Autism Spectrum Disorder using Machine Learning

Autism spectrum disorder (ASD) is a developmental disability that involves persistent challenges in social interaction, communication and behaviour. The purpose of this study is to apply a machine learning approach to differentiate between autistic and normal children and to evaluate the performance of different classifiers in the detection of autism disorder. Heart Rate Variability (HRV) analysis is one of the strategies used for ASD detection by assessing the autonomic nervous system (ANS), which serves as a biomarker for the autism phenotype. HRV can be derived from the photoplethysmogram (PPG). Logistic Regression, Linear Discriminant Analysis and a Cubic Support Vector Machine (SVM) were chosen to evaluate the performance of HRV features in differentiating between normal and autistic children. Three different combinations of features were selected out of 19 features in total. From the results, Logistic Regression was the best classifier to differentiate between autistic and normal children in a colour stimulus test, while Linear Discriminant Analysis was best suited in the baseline test. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future