检测双手手势无处不在：为什么它很重要，我们需要什么，缺少什么
Detection of bimanual gestures everywhere: why it matters, what we need and what is missing
摘要：
双手手势对于研究人类和日常活动中的运动协调至关重要。无约束环境中双手手势的可靠检测是其临床研究和评估日常生活中常见活动的基础。本文研究了一种可靠的，无约束的双手手势检测和分类技术。它假设来自两只手/手臂的惯性数据的可用性，建立在先前开发的基于高斯混合建模（GMM）和高斯混合回归（GMR）的手势建模技术的基础上，并比较不同的建模和分类技术，这些技术是基于一些关于双手手势如何在大脑中表示和建模的文献启发的假设。实验结果显示，与5日常双手活动，这已被选定的基础上的三个主要参数：（不）约束双手的物理工具，（不）需要一个特定序列的单手手势，递归（或不）。在建模方法和分类技术的最佳组合中，五个活动中的五个被识别为高达97%的准确率，82%的精确度和100%的召回率。
英文摘要：
Bimanual gestures are of the utmost importance for the study of motor coordination in humans and in everyday activities. A reliable detection of bimanual gestures in unconstrained environments is fundamental for their clinical study and to assess common activities of daily living. This paper investigates techniques for a reliable, unconstrained detection and classification of bimanual gestures. It assumes the availability of inertial data originating from the two hands/arms, builds upon a previously developed technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and Gaussian Mixture Regression (GMR), and compares different modelling and classification techniques, which are based on a number of assumptions inspired by literature about how bimanual gestures are represented and modelled in the brain. Experiments show results related to 5 everyday bimanual activities, which have been selected on the basis of three main parameters: (not) constraining the two hands by a physical tool, (not) requiring a specific sequence of single-hand gestures, being recursive (or not). In the best performing combination of modeling approach and classification technique, five out of five activities are recognized up to an accuracy of 97%, a precision of 82% and a level of recall of 100%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
