Understanding the actual spinal kinematics in completing critical daily activities is utmost important for human being as it can lead for better quality of life. Two of the most common functions which are necessary for human being are standing up and bend forward. Researchers tried to explore the kinematics of human spine during Sit-to-Stand (STS) and Stand-to-Flexion (STF) but most of them only focussed on thoracic and lumbar spine. Literatures of similar study within thoracic spine only divide the region up to three segments thus reducing the accuracy of actual thoracic multi segments behaviours in completing daily task. This paper aims to study the differences of spinal kinematics contribution between cervical and multi-segmental thoracic spine during STS & STF among healthy Asian adults using non-invasive approach. Interclass correlation coefficient (ICC) for both tasks specified during the study showed excellent reliability with all ICC value were above 0.90 (0.932-0.976). During STS, cervical region displayed quicker flexion-extension transition response. Roughly equivalent behaviour was observed within all thoracic segments. Lower thoracic segments (T10-12) exhibited passive increment behaviour upon reaching upright standing compared with other segments. All segments displayed increase of angular displacement during upright standing. Peak of flexion during STF was achieved at 50% phase with latter response within lower thoracic segments (T8-12). Throughout the completion of STF, most of the segments shared approximately identical behaviour with the adjacent segment. The results provide a clear explanation of the healthy spinal condition of asymptomatic adults and may serve for spinal treatment and rehabilitation purposes.

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 with 100% accuracy, while Linear Discriminant Analysis was best suited in the baseline test with 90% accuracy. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future.

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