Activity recognition from user-annotated acceleration data
Activity recognition from user-annotated acceleration data
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DOI：
10.1007/978-3-540-24646-6_1
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发表时间：
2004-01-01
期刊：
影响因子：
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通讯作者：
Intille, SS
中科院分区：
文献类型：
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作者：
Bao, L;Intille, SS
在这项工作中，开发并评估了一些算法，用于从同时佩戴在身体不同部位的五个小型双轴加速度计所获取的数据中检测身体活动。在没有研究人员监督或观察的情况下，从20名受试者收集了加速度数据。要求受试者执行一系列日常任务，但没有特别告知他们在何处或如何执行这些任务。计算了加速度数据的均值、能量、频域熵和相关性，并测试了使用这些特征的几种分类器。决策树分类器在识别日常活动方面表现最佳，总体准确率为84%。结果表明，尽管一些活动通过与受试者无关的训练数据能得到很好的识别，但其他活动似乎需要特定于受试者的训练数据。结果表明，多个加速度计有助于识别，因为加速度特征值的组合可以有效地区分许多活动。仅使用两个双轴加速度计——大腿和手腕处的——识别性能仅略有下降。这是第一项使用由受试者自己标注的数据集，研究多个无线加速度计对20种活动的识别算法性能的工作。
In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Decision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.