FingerSpeller: Camera-Free Text Entry Using Smart Rings for American Sign Language Fingerspelling Recognition
David Martin, Zikang Leng, Tan Gemicioglu, Jon Womack, Jocelyn Heath, William C Neubauer, Hyeokhyen Kwon, Thomas Plötz, Thad E. Starner · 2023
Camera-based text entry using American Sign Language (ASL) fingerspelling has become more feasible due to recent advancements in recognition technology. However, there are numerous situations where camera-based text entry may not be ideal or acceptable. To address this, we present FingerSpeller, a solution that enables camera-free text entry using smart rings. FingerSpeller utilizes accelerometers embedded in five smart rings from TapStrap, a commercially available wearable keyboard, to track finger motion and recognize fingerspelling. A Hidden Markov Model (HMM) based backend with continuous Gaussian modeling facilitates accurate recognition as evaluated in a real-world deployment. In offline isolated word recognition experiments conducted on a 1,164-word dictionary, FingerSpeller achieves an average character accuracy of 91% and word accuracy of 87% across three participants. Furthermore, we demonstrate that the system can be downsized to only two rings while maintaining an accuracy level of approximately 90% compared to the original configuration. This reduction in form factor enhances user comfort and significantly improves the overall usability of the system.