Poster Abstract: Detecting Kratom Intoxication in Wearable Biosensor Data
Poster Abstract: Detecting Kratom Intoxication in Wearable Biosensor Data
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海报摘要：在可穿戴生物传感器数据中检测卡痛中毒
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
E. Boyer
中科院分区：
文献类型：
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作者：
Joshua Rumbut;Darshan Singh;Hua Fang;Honggang Wang;Stephanie P Carreiro;E. Boyer
在持续的阿片类药物成瘾危机中，无法获得治疗的使用者寻求新的方法来缓解戒断症状。其中有一种来自东南亚的精神活性植物，俗称kratom。随着其传播的消费，自动检测kratom的使用将是有价值的。尽管可穿戴生物传感器过去曾被应用于检测物质使用，但kratom的效果并没有得到很好的理解，而且可能是矛盾的。在本文中，我们进行监督学习的一组功能，从流kratom数据收集的手腕上佩戴的生物传感器部署在参与者在一段时间内的几天，我们提取了几个时域功能，定义一段时间的中毒后使用的基础上，现有的文献，并比较四个分类器的准确性，灵敏度和特异性。我们的研究结果表明，在从家庭使用收集的数据中，使用随机森林分类器可以以95%的准确率检测kratom的使用。
In the ongoing opioid addiction crisis, users who lack access to treatment have sought novel methods to relieve withdrawal symptoms. Among these is a psychoactive plant from South-East Asia popularly known as kratom. With its spreading consumption it would be valuable to automatically detect kratom use. Although wearable biosensors have been applied to detect substance use in the past, kratom’s effects are not as well understood and can be paradoxical. In this paper, we perform supervised learning on a set of features extracted from streaming kratom data gathered from wrist-worn biosensors deployed on participants over a period of several days.We extract several time domain features, define a period of intoxication post-use based on the existing literature, and compare four classifiers based on their accuracy, sensitivity, and specificity. Our results show that kratom use can be detected with 95% accuracy using a random forest classifier in data collected from home use.