Matthew J. Schueler

Papers

Total Citations

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About

Matthew J. Schueler is a leading researcher at the intersection of biomedical engineering and human-computer interaction, specializing in non-invasive sensing for hand motion recognition. His work focuses on using forearm ultrasound imaging to decode complex hand and finger movements, a critical advancement for intuitive control of augmented/virtual reality (AR/VR) systems, prosthetics, and robotics. Schueler’s major contributions include the simultaneous estimation of hand configurations and finger joint angles from ultrasound data, as demonstrated in his highly cited 2023 paper (20 citations). His 2022 study (19 citations) further refined the prediction of metacarpophalangeal joint angles and hand configuration classification, establishing a robust framework for real-time, non-invasive gesture recognition. By leveraging ultrasound’s ability to capture deep muscle and tendon activity, Schueler’s work overcomes limitations of surface-based sensors, offering higher accuracy and robustness. His research has significant implications for next-generation human-machine interfaces, enabling more natural and fluent interactions. With a growing citation impact, Schueler is recognized for pioneering ultrasound-based hand tracking, positioning him as a key innovator in the fields of wearable sensing, rehabilitation engineering, and immersive technology.