Deep learning for RFID-based activity recognition
Document Type
Conference Proceeding
Publication Date
11-14-2016
Journal
Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
DOI
10.1145/2994551.2994569
Keywords
Activity recognition; Convolutional neural network.; Deep learning; Passive RFID; Process phase detection
Abstract
© 2016 Copyright held by the owner/author(s). We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.
APA Citation
Li, X., Zhang, Y., Marsic, I., Sarcevic, A., & Burd, R. (2016). Deep learning for RFID-based activity recognition. Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016, (). http://dx.doi.org/10.1145/2994551.2994569
