Human gesture classification by brute-force machine learning for exergaming in physiotherapy

Francis Deboeverie, Sanne Roegiers, Gianni Allebosch, P. Veelaert, W. Philips
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引用次数: 17

Abstract

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.
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基于暴力机器学习的人体手势分类在物理治疗中的应用
本文提出了一种基于骨骼数据的人体手势分类新方法,以供运动在物理治疗中的应用。与现有的方法不同,我们建议使用随机森林这样的通用分类器来识别动态手势。时间维度随后通过滑动窗口对分类器的连续预测进行多数投票来处理。手势可以有部分相似的姿势,这样分类器将决定不相似的姿势。这种暴力分类策略是允许的,因为动态的人类手势显示了足够多的不同姿势。在线连续的人类手势识别能够在早期对动态手势进行分类,这是通过自动手势识别控制游戏的一个关键优势。此外,由于一个手势中的所有姿势都得到相同的标签,而不需要离散成连续的姿势,因此可以很容易地获得基础真值。这样,就可以很容易地添加新的手势,这在自适应游戏开发中是有利的。我们通过对自捕获的潜行游戏手势数据集和公开可用的微软研究院剑桥-12 Kinect (MSRC-12)数据集进行留一个主体交叉验证来评估我们的策略。在第一个数据集上,我们的准确率达到了96.72%。此外,我们表明随机森林比支持向量机表现得更好。在第二个数据集上,我们的准确率达到了98.37%,比现有方法平均提高了3.57%。
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