Wearable biosensors represent an opportunity to improve treatment and research into a variety of diseases, including substance use disorder. They provide continuous, real-time data about the wearer’s condition in their natural environment in an unobtrusive, increasingly capable, and cost-effective way. However, generating clinically relevant insights from high-velocity, noisy, multidimensional data streams requires new approaches in real-time anomaly machine learning (ML). We present a survey of the existing algorithms for substance use monitoring in wearable biosensor data streams and how the advent of 5G and 6G wireless communications will drive further changes in this field. Our work highlights trends that have emerged among the different efforts published to-date as well as identifying ongoing challenges not adequately addressed by existing ML algorithms.

中文翻译：

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用于实时药物使用检测的可穿戴生物传感器系统概述


可穿戴生物传感器为改善包括物质使用障碍在内的各种疾病的治疗和研究提供了机会。它们以一种不显眼、功能越来越强大且经济高效的方式提供有关佩戴者在自然环境中状况的连续、实时数据。然而，从高速、嘈杂、多维数据流中生成临床相关见解需要实时异常机器学习 (ML) 的新方法。我们对可穿戴生物传感器数据流中物质使用监测的现有算法进行了调查，以及 5G 和 6G 无线通信的出现将如何推动该领域的进一步变化。我们的工作强调了迄今为止发布的不同工作中出现的趋势，并确定了现有机器学习算法未能充分解决的持续挑战。