In Partial Fulfillment of the Requirements for the Degree of Master of Science
will defend his thesis
Device-Free Activity Recognition Using Ultra-Wideband Radio Communication
Human Activity Recognition (HAR) is a fundamental building block in many Internet of Things (IoT) applications. While there has been a lot of interest in HAR, research in non-intrusive activity recognition is still in nascent stages. This research investigates the capability of Ultra-Wideband (UWB) communication technology to be used for HAR. In this work, UWB radio devices are placed in the periphery of a monitored area. This setup infers user activities without the need of any additional sensors or physical device. Packets are exchanged between these UWB devices, and received packets are used to obtain information of the environment. The key idea is that these received packets are affected by environmental modification due to the human activities. We collect Channel Impulse Response (CIR) data from the received packets of the UWB signals. We then use machine learning algorithms to classify the activity (standing, sitting, laying) being performed. The experiments show that by using CIR data as features we can classify simple activities such as standing, sitting, laying and when the room is empty with an accuracy of 95%. To compare this performance, we trained classification models using Wi-Fi Channel State Information (CSI). We found that, for all the models UWB CIR significantly outperformed Wi-Fi CSI when we tried to infer activities using the two techniques. This study also includes an application for this system. We used the HAR system for caloric expenditure estimation during a time period. We use HAR to infer the pose and time spent at each pose and use models from the literature to estimate the caloric expenditure for each pose. We compare our caloric estimate with the estimate produced by commercial devices available in the market. Our approach is 42% more accurate in counting calories spent by stationary subjects. Thus, UWB is an effective and accurate technology for device free activity recognition.
Date: Friday, April 20, 2018
Time: 11:00 AM
Place: PGH 550
Advisor: Dr. Omprakash Gnawali
Faculty, students, and the general public are invited.