|
Abstract
摘要 |
Each of the movements we make in our daily lives can achieve an intended goal, like reaching the handle of a cup of coffee. But if we repeat this task many times, looking at the kinematics of each trajectory at millisecond time scales, away from naked eye detection, we would discover that they are not deterministic but in fact random. Most previous movement studies have been mostly based on visual observations of motor tasks performances, leaving out important information at finer time scales, often considered as noise. People have noticed that individuals with neurological impairments show very heterogeneous types of movement kinematics, for example in the cases of Autism Spectrum Disorders (ASD), Parkinson and Schizophrenia. This heterogeneity has particularly impeded developing efficient and quantitative biological diagnoses for these disorders since usually they are only based on human eye observations. There is then a critical need to identify objective and data-driven biomarkers for these disorders as guides for basic biological research studies. Recent advent of high-resolution wearable sensing devices enable continuous motion dynamic recordings at milliseconds time scales. Using this technological development, we have been able to extract information that leads to the unraveling of quantitative biomarkers for these disorders. To describe the general approach in this lecture I will only concentrate in presenting results for ASD individuals. The essence of our work consists first in identifying and then studying the statistical properties in the speed dynamics’ discontinuities in different types of subjects. By studying the movement’s statistics of human natural hand movements, we unraveled a new data-type characterized by the smoothness levels of the body dynamics. We use correlation functions, nearest neighbor speed-spike statistics plus other statistical metrics to quantitatively characterize each individual within the ASD. Our statistical analysis led to a parameter phase space that provides an automatic screening of different types of ASD subjects linking it, a posteriori, with their verbal speaking abilities. We also found unexpected similarities of the ASD’s movement statistics to that of their parent’s. |
