HPD: Human Proximity Device
Format
SOECS Senior Project
Faculty Mentor Name
Rahim Khoie
Faculty Mentor Department
Electrical and Computer Engineering
Abstract/Artist Statement
With the progressive nature and growing capabilities of sensor technologies, and their increasing commercial availability, developing more affordable, modular, and increasingly smaller devices for real-world applications has become more common now than ever. Considering the recent Coronavirus pandemic, the importance of discerning the number of human individuals within a given enclosed space has become more pronounced for personal safety. Our research into developing a device that counts the number of unique human interactions within an adjustable proximity aims to combine concise and comprehensible statistics, efficient sensor power management, and portability to allow user interactions to be tracked in a set location or on the move. Our current implementation uses a combination of Passive Infrared, LiDAR, and Image Processing to collect and process data locally and provide a running total of all unique interactions. Then, at the user’s discretion, the data can be uploaded through Bluetooth to a web application for further data processing. The device is handheld and portable, and after starting its process, it does not need any additional input until the user wants to stop collecting data. Upon the completion of our device and the setup of our application environment, we will solidify our design with the aggregation and interpretation of our image segmentation and lidar proximity data to report on our phone app as a future feature update.
Location
School of Engineering & Computer Science
Start Date
7-5-2022 2:30 PM
End Date
7-5-2022 4:00 PM
HPD: Human Proximity Device
School of Engineering & Computer Science
With the progressive nature and growing capabilities of sensor technologies, and their increasing commercial availability, developing more affordable, modular, and increasingly smaller devices for real-world applications has become more common now than ever. Considering the recent Coronavirus pandemic, the importance of discerning the number of human individuals within a given enclosed space has become more pronounced for personal safety. Our research into developing a device that counts the number of unique human interactions within an adjustable proximity aims to combine concise and comprehensible statistics, efficient sensor power management, and portability to allow user interactions to be tracked in a set location or on the move. Our current implementation uses a combination of Passive Infrared, LiDAR, and Image Processing to collect and process data locally and provide a running total of all unique interactions. Then, at the user’s discretion, the data can be uploaded through Bluetooth to a web application for further data processing. The device is handheld and portable, and after starting its process, it does not need any additional input until the user wants to stop collecting data. Upon the completion of our device and the setup of our application environment, we will solidify our design with the aggregation and interpretation of our image segmentation and lidar proximity data to report on our phone app as a future feature update.