Surface Acoustic Wave (SAW) Force Myography Sensor for Comprehensive Muscle Force and Torque Evaluation

Kohler, Michael

Surface Acoustic Wave (SAW) Force Myography Sensor for Comprehensive Muscle Force and Torque Evaluation

2025

Acoustic surface wave devices
Telecommunication—Equipment and supplies
Human engineering
Human mechanics
Sports medicine

English

Accurate, real-time muscle force and joint torque monitoring is essential across fields such as sports medicine, rehabilitation, biomechanics, and industrial safety. Although dynamometry offers precise measurements, high cost, bulkiness, and required supervision limit continuous use. Many wearable systems also suffer from tethering or bulky batteries, reducing real-world utility. This dissertation addresses these gaps by introducing a novel Surface Acoustic Wave (SAW)–based Force Myography (FMG) framework. Although the present implementation is not wireless, it establishes the foundational principles necessary for future battery-free and wireless muscle-sensing solutions. Integrating FMG with SAW technology, the proposed platform captures muscle deformations and correlates them with torque output. Under isometric and isokinetic conditions, SAW-FMG measurements strongly align with those from a gold-standard electromechanical dynamometer, demonstrating high accuracy and repeatability. Additionally, this work addresses the challenge of the inherently high temperature coefficient of delay (TCD) in 128° YX-cut lithium niobate (LiNbO3). Thin-film over-layers and lithium niobate on insulator (LNOI) substrates are utilized to mitigate thermal drift, thereby enhancing sensor stability under varying temperature conditions. Numerical simulations and prototypes confirm near-zero TCD, ensuring temperature-independent strain readings for consistent performance. Overall, this research lays the groundwork for wearable sensors enabling precise torque measurements with minimal thermal drift. Offering real-time, noninvasive muscle force monitoring, SAW-FMG holds potential for exoskeleton control, injury prevention, and personalized rehabilitation strategies, where timely feedback, reliability, and adaptive load management are paramount. The SAW-FMG approach not only addresses critical limitations of existing systems but also offers a scalable solution for advanced musculoskeletal applications. These findings open pathways for future wireless, battery-free systems in fields such as clinical diagnostics, sports performance, and industrial ergonomics.

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Thesis

College of Engineering and Computing Sciences

Department of Electrical & Computer Engineering

Engineering, Ph.D.