I-Corps: Passive Infrared Sensor Technology Solution for Advanced Occupancy Sensing

I-Corps：用于高级占用感应的被动红外传感器技术解决方案

• 批准号: 2229358
• 负责人: Ya Wang
• 金额: $ 5万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229358&HistoricalAwards=false
• 关键词: Corps; Passive; Infrared; Sensor; Technology

The broader impact/commercial potential of this I-Corps project is the potential development of artificial intelligence (AI)-enabled solid-state lens integrated with passive infrared (PIR) sensors to detect occupancy. Today, buildings are equipped with PIR sensors for occupancy detection, owing to their low cost, low energy consumption, wide field of view, and high reliability. Despite these advantages, PIR sensors only detect motion - not stationary occupancy. By differentially perceiving infrared energy received by standard PIR sensors, the proposed technology may detect moving and stationary occupants. This may provide a low-cost plug-and-play solution to achieve personalized heating and cooling, identify underutilized spaces to more accurately forecast usage, improve workspaces and reduce operating expense costs. In addition, the proposed technology provides a privacy-reserving solution to monitor sleep quality and fall detection. The proposed applications include security systems, smart home appliances, traffic management, consumer psychology, and public health. This I-Corps project is based on the potential development of a solid-state electronic lens composed of liquid crystal treated with nanoparticles to enable stationary occupancy detection with current passive infrared (PIR) sensors. The proposed technology is designed to actively perceive and differentiate infrared energy by modulating the transmission rate. Though the occupants are stationary, their thermal signatures, such as body temperature, body shape, and gestures may be perceived differentially by tuning the apertures of the lens. Using a well-tuned transmission ratio in the long-wave infrared range (8-12µm) where human skin radiates the most energy, the standard PIR sensor may now intelligently perceive infrared thermal signatures of warm subjects in the field of view. In addition, using an online AI algorithm, the lens is capable of differentiating human from non-human warm subjects by classifying human identification and activities. Localized environmental information and training data that is collected can reduce computing complexity thereby elevating detection accuracy to high accuracy.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/商业潜力是具有人工智能(AI)功能的固态透镜的潜在开发，该固态透镜集成了被动红外(PIR)传感器来检测入住率。如今，由于低成本、低能耗、大视场和高可靠性，建筑物都配备了PIR传感器来检测入住率。尽管有这些优势，但PIR传感器只能检测运动，而不能检测静止的乘客。通过差分感知标准PIR传感器接收的红外能量，所提出的技术可以检测运动和静止的乘员。这可能会提供低成本的即插即用解决方案，以实现个性化供暖和制冷，识别未充分利用的空间，以更准确地预测使用情况，改善工作空间并降低运营费用成本。此外，这项拟议的技术还提供了一种隐私保护解决方案，用于监控睡眠质量和摔倒检测。拟议的应用包括安全系统、智能家电、交通管理、消费者心理和公共卫生。该i-Corps项目基于固态电子透镜的潜在开发，该固态电子透镜由经过纳米颗粒处理的液晶组成，能够利用当前的被动红外(PIR)传感器实现静止的占有率检测。提出的技术旨在通过调制传输速率来主动感知和区分红外能量。虽然居住者是静止的，但他们的热特征，如体温、体型和手势，可以通过调节镜头的光圈来不同地感知。标准PIR传感器在人体皮肤辐射能量最大的长波红外范围(8-12微米)使用良好的透射率，现在可以智能地感知视野中温暖对象的红外热特征。此外，使用在线人工智能算法，该镜头能够通过对人类的识别和活动进行分类来区分人类和非人类的温暖对象。收集的本地化环境信息和训练数据可以降低计算复杂性，从而将检测精度提高到高精度。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Bayes Filter-Based Occupancy Detection Using Networked SLEEPIR Sensors

• DOI: 10.1109/jsen.2023.3304372
• 发表时间: 2023-10
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Muhammad Emad-ud-din;Zhangjie Chen;Qijie Shen;Libo Wu;Ya Wang

Promoting Occupancy Detection Accuracy Using On-Device Lifelong Learning

• DOI: 10.1109/jsen.2023.3260062
• 发表时间: 2023-05
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Muhammad Emad-ud-din;Ya Wang

Improving Indoor Occupancy Detection Accuracy of the SLEEPIR Sensor Using LSTM Models

使用 LSTM 模型提高 SLEEPIR 传感器的室内占用检测精度

• DOI: 10.1109/jsen.2023.3287565
• 发表时间: 2023
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Chen, Zhangjie;Wang, Mingyi;Wang, Ya
• 通讯作者: Wang, Ya

Indoor Occupancy Estimation Using Particle Filter and SLEEPIR Sensor System

使用粒子过滤器和 SLEEPIR 传感器系统估计室内占用情况

• DOI: 10.1109/jsen.2022.3192270
• 发表时间: 2022
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Emad-ud-Din, Muhammad;Chen, Zhangjie;Wu, Libo;Shen, Qijie;Wang, Ya
• 通讯作者: Wang, Ya