Classifying wheelchair propulsion patterns with a wrist mounted accelerometer
Classifying wheelchair propulsion patterns with a wrist mounted accelerometer
复制标题
使用腕部安装的加速度计对轮椅推进模式进行分类
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Divya Tyamagundlu
中科院分区：
文献类型：
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作者：
Brian French;A. Smailagic;D. Siewiorek;Vishnu Ambur;Divya Tyamagundlu
在本文中，我们描述了一个手动轮椅推进分类系统，它使用手腕安装的加速度计识别不同的模式。在一项有限的用户研究中，已经确定了四种不同的推进模式。这项研究是第一次尝试使用低保真、穿戴在身上的传感器对轮椅推进模式进行分类。数据是使用各种表面类型上的所有四种推进模式收集的。对两种机器学习算法的结果进行了比较。即使使用简单的分类器，如k近邻(KNN)，也可以达到90%以上的准确率。能够识别当前的推进模式，并向轮椅新手和专家用户提供实时反馈，可能有助于防止未来的重复使用伤害。
In this paper, we describe a manual wheelchair propulsion classification system which recognizes different patterns using a wrist mounted accelerometer. Four distinct propulsion patterns have been identified in a limited user study. This study is the first attempt at classifying wheelchair propulsion patterns using low-fidelity, body-worn sensors. Data was collected using all four propulsion patterns on a variety of surface types. The results of two machine learning algorithms are compared. Accuracies of over 90% were achievable even with a simple classifier such as k-Nearest Neighbor (kNN). Being able to identify current propulsion patterns and provide real-time feedback to novice and expert wheelchair users is potentially useful in preventing future repetitive use injuries.
影响因子：
4.3
作者：
Boninger, ML;Souza, AL;Fay, BT
通讯作者：
Fay, BT