Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model
Automatic Segmentation and Recognition in Body Sensor Networks Using a Hidden Markov Model
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DOI：
10.1145/2331147.2331156
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发表时间：
2012-08-01
影响因子：
2
通讯作者：
Jafari, Roozbeh
中科院分区：
文献类型：
--
作者：
Guenterberg, Eric;Ghasemzadeh, Hassan;Jafari, Roozbeh
身体传感器网络的一个重要应用是动作识别。动作识别通常隐含地需要将传感器数据划分为间隔，然后根据每个代表的动作或非动作来标记分区。时间分割阶段称为分割，标记称为分类。虽然存在许多有效的分类方法，但分割仍然存在问题。我们提出了一种技术的灵感来自连续语音识别，结合使用隐马尔可夫模型的分割和分类。该技术分布在多个传感器节点上。我们展示了这种技术的结果和在全数据传输的带宽节省。
One important application of body sensor networks is action recognition. Action recognition often implicitly requires partitioning sensor data into intervals, then labeling the partitions according to the action that each represents or as a non-action. The temporal partitioning stage is called segmentation, and the labeling is called classification. While many effective methods exist for classification, segmentation remains problematic. We present a technique inspired by continuous speech recognition that combines segmentation and classification using hidden Markov models. This technique is distributed across several sensor nodes. We show the results of this technique and the bandwidth savings over full data transmission.