Deep learning for RFID-based activity recognition
Proceedings of the 14th ACM Conference on Embedded Networked Sensor Systems, SenSys 2016
Activity recognition; Convolutional neural network.; Deep learning; Passive RFID; Process phase detection
© 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.
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