Emadi S, Miranda Montenegro S, Castellini C (2026)
Publication Type: Conference contribution
Publication year: 2026
Publisher: IEEE Computer Society
Pages Range: 1308-1314
Conference Proceedings Title: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Event location: Edmonton, AB, CAN
ISBN: 9798331548131
DOI: 10.1109/BioRob66782.2026.11681034
Reliable and low-latency biosignal-based intent detection is essential for seamless human-robot interaction in rehabilitation and assistive technologies. Fingertip force estimation provides a challenging instance of this problem, requiring methods that can capture subtle forearm muscle activity in real time. While ultrasound imaging has emerged as a promising modality, existing feature extraction methods often produce high-dimensional vectors or rely on computationally expensive operations, limiting their applicability in resourceconstrained systems. This paper introduces a novel feature extraction method based on efficient horizontal summation of B-mode ultrasound frames. The approach requires simple arithmetic operations, producing compact and discriminative features that preserve anatomical information while minimizing redundancy. Benchmarking against three established baselines-Histogram of Oriented Gradients, ROI-Gradient, and Image Moments-on a dataset of eight participants shows that the proposed method predicts fingertip forces with state-of-the-art accuracy (nRMSE =6.1 6%, R2=0.847), while reducing feature dimensionality by a factor of five and achieving more than four times faster extraction. A calibration analysis explores the reduction of required training data acquisition while maintaining high accuracy. These results highlight the method's potential for real-time deployment in wearable ultrasound systems, with a wide range of applications of human-robot interfaces.
APA:
Emadi, S., Miranda Montenegro, S., & Castellini, C. (2026). Fast and Compact Ultrasound Features for Real-Time Hand Intent Detection in Human-Robot Interfaces. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 1308-1314). Edmonton, AB, CAN: IEEE Computer Society.
MLA:
Emadi, Seyedahmad, Silvana Miranda Montenegro, and Claudio Castellini. "Fast and Compact Ultrasound Features for Real-Time Hand Intent Detection in Human-Robot Interfaces." Proceedings of the 11th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2026, Edmonton, AB, CAN IEEE Computer Society, 2026. 1308-1314.
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