Long Xi, Yin Bin, Aarts Ronald M
Eindhoven University of Technology, Eindhoven, the Netherlands.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6107-10. doi: 10.1109/IEMBS.2009.5334925.
In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of approximately 80%, which was comparable with that obtained by a Decision Tree classifier.
在本研究中，使用一个置于腰部的单轴加速度计来记录用于人类身体活动分类的加速度数据。数据收集涉及24名受试者在自然环境中进行日常现实生活活动，且无研究人员干预。为评估客户的每日能量消耗，选择步行、跑步、骑自行车、驾驶和运动作为分类的目标活动。本研究将贝叶斯分类与基于决策树的方法进行了比较。贝叶斯分类器具有更易于扩展的优势，在目标活动进一步扩展或修改时，几乎无需努力进行分类器再训练和软件更新。应用主成分分析来消除特征之间的相关性并降低特征向量维度。使用留一法和10折交叉验证协议进行的实验显示分类准确率约为80%，这与决策树分类器所获得的准确率相当。
