An Application of Nonparametric Functional Data Analysis in the Sport Biomechanics Big Data Processing

Publish Year: 1401
نوع سند: مقاله کنفرانسی
زبان: English
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تاریخ نمایه سازی: 7 خرداد 1401


Big data has ۵Vs (Volume, Variety, Velocity, Veracity, and Value) that are present in many fields like sports biomechanics with devices such as wearables, markers, etc. Among different statistical methods for big data analysis, functional data analysis (FDA) provides a methodology to analyze the dataset by considering underlying functions and curves. The open dataset includes ۵۷ healthy subjects with ۵۴ markers among the full-body and three-dimensional ground reaction force (GRF) that is a functional predictor. The GRF curves have registered with three methods: Group-wise function alignment and PCA Extractions, Bayesian methods in Ambient and Quotient space. The result with two indices: The synchronization (Sync) coefficient and the inverse of pairwise correlation (IPC) are compared. And four functional prediction methods considering weight, height, and body mass index (BMI) as continuous responses are used including regression, median, mode, and multimethod (nonlinear) operator. Two kernel functions (Triangle and Quadratic), and three semi-metrics (first and second derivative, PCA) with a local selection of the optimal numbers of the neighbors for kernel estimation are compared to each other. The result showed that the Mean Square Error (MSE) in group-wise function alignment for BMI with second derivative semi-metric and with the quadratic kernel is the lowest: regression (۱.۰۳,۴.۳۲ - ۱.۳۶,۵.۶۹), conditional mode (۱.۷۲,۷.۰۲ - ۰.۹۷,۴.۰۷), conditional median (۱.۴۷,۶.۱۵-۱.۸۳,۷.۶۵), multimethod (۴.۴۳,۵.۶۴- ۴.۹۸,۴.۵۳) for train, test and left-right GRF dataset, respectively. We conclude that the nonparametric FDA methods provide comprehensive methods for studying complex relationships in big data and they are popular in the sports biomechanics literature.


Mohammad Fayaz

PhD in Biostatistics Shahid Beheshti University of Medical Sciences Tehran, Iran

Seyed Mehran Hosseini

MD. PhD, Professor of Medical Physiology Department of Physiology, School of Medicine, Neuroscience Research Center Golestan University of Medical Sciences Gorgan, Golestan