I-Corps Teams: IoT Sensor Networks Detecting User Behavior in Architectural Space

I-Corps 团队：物联网传感器网络检测建筑空间中的用户行为

• 批准号: 2011473
• 负责人: Roman Lubynsky
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2011473&HistoricalAwards=false
• 关键词: Corps; Teams; IoT; Sensor; Networks

The broader impact/commercial potential of this I-Corps project is the development of a cost-effective solution to detect user behavior in architectural space without violating the user’s privacy. This technology uses passive infrared (PIR) sensors to collect lightweight data and identify user behavior, which is traditionally done by camera and image recognition at higher processing power and cost. One possible application includes optimizing mall market targeting and revenue growth by generating traffic flow and headcount analytics. This technology also may help commercial properties and hotels to understand their customers’ needs better and create cross-sale opportunities through features including individual behavior path predicting, crowd behavior prediction, population forecasts, and specific behavioral analytics, like queue management and purchase-tracking habits. Another possible application is in automating rental property management for homeowners by remotely coordinating check-in and check-out procedures, monitoring cleaning results, and diminishing the likelihood of unpermitted parties, fire, flood, or theft.This I-Corps project is based on the development of a novel neural network system that can classify low pixel thermal data into the number of users and type of user posture. With non-fragmented thermal data in architectural space, the proposed method is capable of matching user behavior with hassle-free automation. Using 64 thermal data points from a single PIR sensor, the system can real-time classify the dataset into occupancy numbers at a 95% accuracy rate and possible postures in under 800ms. With more sensors in the network, the system can identify specific user behavior and indoor location. These behavior data are the key to achieve frustration-free automation in smart living and working spaces.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目的更广泛的影响/商业潜力是开发一种具有成本效益的解决方案，以检测建筑空间中的用户行为，而不会侵犯用户的隐私。该技术使用被动红外（PIR）传感器来收集轻量级数据并识别用户行为，传统上这是通过摄像头和图像识别以更高的处理能力和成本完成的。一个可能的应用包括通过生成流量和人数分析来优化购物中心市场目标和收入增长。这项技术还可以帮助商业地产和酒店更好地了解客户的需求，并通过包括个人行为路径预测，人群行为预测，人口预测和特定行为分析（如队列管理和购买跟踪习惯）在内的功能创造交叉销售机会。另一个可能的应用是通过远程协调入住和退房程序，监控清洁结果，减少未经许可的聚会，火灾，洪水或盗窃的可能性，为房主自动化租赁物业管理。这个I-Corps项目是基于一个新颖的神经网络系统的开发，可以将低像素的热数据分类为用户数量和用户姿势类型。利用建筑空间中的非碎片化热数据，所提出的方法能够将用户行为与轻松的自动化相匹配。使用来自单个PIR传感器的64个热数据点，该系统可以在800 ms内以95%的准确率将数据集实时分类为占用数量和可能的姿势。网络中有更多的传感器，系统可以识别特定的用户行为和室内位置。这些行为数据是在智能生活和工作空间中实现无障碍自动化的关键。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。