I-Corps Teams: IoT Sensor Networks Detecting User Behavior in Architectural Space
I-Corps Teams: IoT Sensor Networks Detecting User Behavior in Architectural Space
批准号:
2011473
负责人:
Roman Lubynsky
金额:
$5.0万
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
项目状态:
已结题
起止时间:
2020-02-01 至 2021-07-31
中文摘要
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英文摘要
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.
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