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Towards a wearable system for continuous monitoring of sniffing and panting in dogs

机译:迈向可监测狗的嗅探和喘气的可穿戴系统

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Although numerous advances have been made in instrumental odor detection systems, these still cannot match the efficient sampling, odor discrimination, agile mobility and the olfactory acuity of odor detection dogs. A limiting step in using dogs to detect odors is the subjectivity of the translation of odor information processed by the dog to its handler. We present our preliminary efforts towards a wireless wearable system for continuous auscultation of respiratory behavior by recording internal sounds at the neck and chest by means of a commercially available electronic stethoscope to provide objective decision support for handlers. We have identified discrete features of sniffing and panting in the time domain and utilize event duration, event rate, event mean energy, and the number of consecutive events in a row to build a decision tree classifier. Since feature extraction requires segmentation of individual sniffing and panting events, we developed an adaptive method using short-time energy contour and an adaptive threshold. The performance of the system was evaluated on recordings from a Greyhound and a Labrador Retriever and achieved high classification accuracies.
机译:尽管在仪器气味检测系统方面已取得了许多进步,但这些仍无法与气味检测犬的有效采样,气味识别,敏捷移动性和嗅觉敏锐度相提并论。使用狗来检测气味的一个限制步骤是将狗所处理的气味信息翻译给其处理者的主观性。我们通过无线市售的电子听诊器记录颈部和胸部的内部声音,为持续听诊呼吸行为的无线可穿戴系统提供了初步的努力,以为操作者提供客观的决策支持。我们在时域中确定了嗅探和喘气的离散特征,并利用事件持续时间,事件发生率,事件平均能量和连续的连续事件数来构建决策树分类器。由于特征提取需要对单个嗅探和喘气事件进行分段,因此我们开发了一种使用短时能量轮廓和自适应阈值的自适应方法。根据灵狮和拉布拉多猎犬的记录对系统的性能进行了评估,并获得了很高的分类精度。

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