Learning Gestures Using A Passive Data-Glove With RFID Tags
Name
Gesture_recognition_with_tags (1).pdf
Description
Accepted version
Size
5.24 MB
Format
Adobe PDF
Checksum (MD5)
7a31965d5a2356134ec8ae0ac0501ca7
Author(s)
Kantareddy, Sai Nithin R.
Sun, Yongbin
Bhattacharyya, Rahul
Sarma, Sanjay E
Date Issued
September 2019
Journal
2019 IEEE International Conference on RFID Technology and Applications, RFID-TA 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kantareddy , Sai Nithin R. et al. “Learning Gestures Using A Passive Data-Glove With RFID Tags.” Paper presented at the 2019 IEEE International Conference on RFID Technology and Applications (RFID-TA), Pisa, Italy, September 25-27, 2019, IEEE © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Hand gesture recognition enables non-tactile interfaces for human-machine interactions. Cameras are currently powerful tools to recognize these gestures, however, use of cameras is constrained by privacy concerns and need for welllit, line of sight implementation. In this study, we propose an alternate method to recognize gestures using a passive data-glove augmented with passive RFID tags. We envision passive tags-based gesture recognition will have applications in improving operator safety around machines, activity monitoring in factories and sign to speech recognition, etc. Low-level reader information (RSSI, Phase and Doppler frequency) can be used to capture changes to the tags in the environment, therefore generating enough information to infer gestures. In this paper, we present a technique to enable fast feature recognition using low-level reader data by correcting for inconsistencies in phase data due to frequency hopping. We experimented with four different classifiers on the low-level reader data and our Fully-Connected Neural Network (FCCN) classifier is able to learn gestures from tag-data with 98% accuracy.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/RFID-TA.2019.8892224
