Long-term activity recognition from wristwatch accelerometer data.
Long-term activity recognition from wristwatch accelerometer data.
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DOI：
10.3390/s141222500
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发表时间：
2014-11-27
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影响因子：
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通讯作者：
Garrido L
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文献类型：
--
作者：
Garcia-Ceja E;Brena RF;Carrasco-Jimenez JC;Garrido L
随着具有多个嵌入式传感器的可穿戴设备的发展，可以收集可以分析的数据，以了解用户的需求并提供个性化服务。这些类型的设备的示例是智能手机、健身手环、智能手表，仅举几例。在过去的几年里，一些作品已经使用这些设备来识别简单的活动，如跑步，走路，睡觉和其他身体活动。也有关于识别复杂活动的研究，如烹饪，运动和服用药物，但这些通常需要安装外部传感器，这些传感器可能会对用户造成干扰。在这项工作中，我们使用手表的加速度数据来识别长期活动。我们比较使用隐马尔可夫模型和条件随机场的分割任务。我们还通过将其编码为约束条件，将有关活动持续时间的先验知识添加到模型中，并以特征函数的形式添加序列模式。我们还进行了子类化，以处理类内碎片化的问题，当相同的标签应用于概念上相同但从加速的角度来看非常不同的活动时，就会出现这种问题。
With the development of wearable devices that have several embedded sensors, it is possible to collect data that can be analyzed in order to understand the user's needs and provide personalized services. Examples of these types of devices are smartphones, fitness-bracelets, smartwatches, just to mention a few. In the last years, several works have used these devices to recognize simple activities like running, walking, sleeping, and other physical activities. There has also been research on recognizing complex activities like cooking, sporting, and taking medication, but these generally require the installation of external sensors that may become obtrusive to the user. In this work we used acceleration data from a wristwatch in order to identify long-term activities. We compare the use of Hidden Markov Models and Conditional Random Fields for the segmentation task. We also added prior knowledge into the models regarding the duration of the activities by coding them as constraints and sequence patterns were added in the form of feature functions. We also performed subclassing in order to deal with the problem of intra-class fragmentation, which arises when the same label is applied to activities that are conceptually the same but very different from the acceleration point of view.
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
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期刊：
Sensors (Basel, Switzerland)
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