| Paper ID | SS-6.6 |
| Paper Title |
Word-Level ASL Recognition and Trigger Sign Detection with RF Sensors |
| Authors |
Mohammed Rahman, Emre Kurtoglu, University of Alabama, United States; Robiulhossain Mdrafi, Ali Gurbuz, Mississippi State University, United States; Evie Malaia, Chris Crawford, Darrin Griffin, Sevgi Gurbuz, University of Alabama, United States |
| Session | SS-6: Intelligent Sensing and Communications for Emerging Applications |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Special Sessions: Intelligent Sensing and Communications for Emerging Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hard-of-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, and 0.93 trigger recognition rate. |
