Deep Learning for RFID-Based Activity Recognition

By Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, Randall S. Burd. 2016

157 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Public Health, Environmental and Occupational Health

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Ala Al-Fuqaha
2. Jindong Wang
3. Sameh Sorour
4. Yiqiang Chen

In Computer Vision and Pattern Recognition

1. Jindong Wang
2. Yiqiang Chen
3. Ala Al-Fuqaha
4. Paul Patras
5. Zhiwen Yu
6. Bin Guo
7. Mohsen Guizani
8. Sameh Sorour

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “[78] also report the usage of raw data from passive”
• Deep Learning for Sensor-based Human Activity Recognition (2020, methodology): “onal expenses [161]. In addition to wearable sensors, RFID-based activity recognition requires the fusion of multiple RFID signals as well, and CNNs are also commonly used for the early fusion manner [86]. Sensor-based Fusion (SF). In contrast to EF, SF fist considers each modality individually and ”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[226] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [227] Smart watch-based activity recognition Edge-based RBM”
• Deep Learning for Sensor-based Activity Recognition: A Survey (2017, background): “(Li et al., 2016a) added pooling after the fully-connected layers to extract the important features.”