一个物联网设备曲目咳嗽和实时人群规模可能成为确定存在一个有用的工具，流感样大的人群中症状，根据研究小组在麻省大学阿姆赫斯特的。
FluSense，作为研究人员称呼它，是关于一个字典的大小。它包含一个便宜的麦克风阵列，热传感器，树莓Pi和一个Intel Movidius 2神经计算引擎。我们的想法是使用AI在边缘音频样本进行分类，并在任何给定的时间确定在一个房间的人数。
由于该系统可以区分其他类型的非语音音频的咳嗽，关联与给定人群的规模咳嗽可以给多少人可能有用的索引在经历流感样症状。
A test run between December 2018 and July 2019 saw FluSense installed in four waiting rooms at UMass’ University Health Services clinic, and the researchers said that they were able to “strongly” correlate the system’s results with clinical testing for influenza and other illnesses with similar symptoms.
而对于FluSense更大的计划正在酝酿，根据该论文的主要作者，博士生Forsad铝侯赛因和他的合着者，顾问，助理教授Tauhidur拉赫曼。
“[C]urrently we are planning to deploy the FluSense system in several large public spaces (e.g., large cafeteria, classroom, dormitories, gymnasium, auditorium) to capture syndromic signals from a broad range of people who live in a certain town or city,” they said. “We are also looking for funding to run a large-scale multi-city trial. In the meantime, we are also diversifying our sensing capability by extending FluSense’s capability to capture more syndromic signals (e.g., recently we added sneeze sensing capability to FluSense). We definitely see a significant level of commercialization potential in this line of research.”
因为所有的有意义的处理工作都在本地完成，通过英特尔神经计算引擎和树莓派FluSense特别是从技术的角度来看有趣。症状信息无线发送到实验室进行整理，当然，但繁重的工作在边缘完成。Al Hossain and Rahman were quick to emphasize that the device doesn’t collect personally identifiable information – the emphasis is on aggregating data in a given setting, rather than identifying sickness in any single patient – and everything it does collect is heavily encrypted, making it a minimal privacy concern.
FluSense的关键点，根据研究人员，是把它作为一种健康监控工具，而不是一条诊断设备。铝侯赛因和拉赫曼说，它比其他的健康监测技术，尤其是基于互联网的跟踪，像谷歌流感趋势和Twitter的几个重要的优点。
“FluSense不容易被公众健康活动或广告的影响。此外，该传感器的接触自然是理想的拍摄被动的从不同的地理位置和不同社会经济群体综合征信号（包括弱势群体谁可能无法获得医疗保健，不得去医生/诊所，”他们说。