Forearm Radiomyography for Character and Writer Identification in Air Writing

This paper explores the air writing application of radiomyography (RMG), a near-field radio frequency (NFRF) sensing modality, and studies the key issues in human-computer interaction (HCI), including user acceptability and privacy concerns. Our primary objective is to demonstrate the efficacy of our NFRF sensing system in identifying both handwritten characters and the writer. We employed a forearm-worn multiple-input-multiple-output (MIMO) RMG to capture the detailed actuation of superficial and deep muscles during the air writing motion through enhanced electromagnetic coupling in the near-field. The sensing architecture leverages synchronized software-defined radios (SDRs), custom antenna arrays, and frequency-division multiplexing to enable precision muscle activity detection in a wearable armband. Various signal processing and machine learning (ML) algorithms were benchmarked for both classification accuracy and computational efficiency. The experimental study included 26 letters and 25 writers. The best performing algorithm achieved an average character classification accuracy of 92.35% (95% CI: 90.06-94.64). Moreover, through transfer learning, our model reached 95% testing accuracy in character classification for new subjects using just 10% of the available data for training. The writer identification model achieves an average accuracy of 99.1%, including longitudinal studies. These results highlight the promise of forearm-worn RMG as a novel HCI alternative, offering accurate performance without the need for cumbersome devices on the hand that may obstruct dexterity or the need for wet body-contact electrodes in surface electromyography.