Real-Time Continuous ASL Fingerspelling Recognition
with a Single Ring

We introduce a single smart ring worn on the thumb that recognizes words continuously fingerspelled in ASL at natural speed. The ring uses active acoustic sensing (via a microphone and speaker) and an inertial measurement unit (IMU) to track handshape and movement, which are processed through a deep learning algorithm using Connectionist Temporal Classification (CTC) loss. We evaluated the system with 20 ASL signers (13 fluent and 7 learners), using the MacKenzie-Soukoref Phrase Set of 1,164 words and 100 phrases. Offline evaluation yielded top-1 and top-5 word recognition accuracies of 82.45% (±9.67%) and 92.42% (±5.70%), respectively. In real-time, the system achieved a word error rate (WER) of 0.099 (±0.039). Based on these results, we discuss key lessons and design implications for future minimally obtrusive ASL recognition wearables.