Magnetic tracking has emerged as a promising solution for biomedical applications, as it can estimate the position and orientation of magnetic sources inside the human body without a line of sight. In prosthetic control, we introduced the myokinetic control interface, deriving control signals through real-time localization of small permanent magnets implanted in the residual muscles of amputees. The quality of these signals strongly depends on localization accuracy, which previous studies demonstrated to be substantially affected by geometric constraints. While factors such as magnet-to-sensor distance and sensor arrangement have been extensively investigated, the lateral boundaries of the optimal localization workspace remain poorly understood. To fill this gap, this study systematically investigated how the distance between magnets and the edges of the sensor-area affects localization accuracy. The latter was quantified by computing the localization error of a magnet pair as its position varied within and beyond the sensor-area boundaries. Experimental results showed that localization remained accurate while magnets stayed within the sensor-area, but degraded rapidly once they moved beyond its lateral boundaries. The optimal localization workspace progressively narrowed as the sensor-to-magnet distance increased, defining a pyramidal region ensuring reliable localization accuracy. Similar results emerged in simulations only after modelling sensor nonidealities. To generalize these findings, we introduced two geometry-related parameters describing magnet position relative to the sensor-area, explaining approximately 80% of the variability in localization accuracy. These results provide insights into the complexity of modelling sensor behaviour using simulated data, and practical guidelines for defining the optimal workspace in multi-magnet localization systems.
Surface acoustic wave (SAW) devices are widely used in integrated microsystems, biosensing, microscale actuation, and particle manipulation. Interdigitated transducers (IDTs) are essential components in SAW devices, determining the efficiency of acoustic excitation. IDTs' designs are often optimized through iterative fabrication and experimental testing. We present a modeling-guided methodology that combines analytical IDT modeling, finite-element simulations, and experimental characterization to establish predictive relationships between IDT geometry and device performance. SAW devices with IDT wavelengths of 100 and 300 µm, with finger pairs ranging from 10 to 30, were fabricated on lithium niobate and characterized for resonance, insertion loss, impedance matching, and return loss. The results establish a relationship between the number of finger pairs and electrical/RF performance, which is consistent across analytical predictions, simulations, and measurements. Based on RF characterization, 100 µm-wavelength SAW devices with 10 and 20 finger pairs were integrated with microfluidic channels for particle manipulation. Particle-pushing experiments with 25 µm polystyrene particles showed that the minimum actuation power required for particle displacement varies with the number of finger pairs, demonstrating the influence of transducer design on acoustic actuation. The proposed workflow provides a predictive method for optimizing SAW transducers before fabrication and can be extended beyond acoustofluidic platforms to sensing, microscale actuation, and integrated optoacoustic or photoacoustic microsystems. This approach provides a general framework for connecting RF/electrical transducer characteristics with functional microsystem performance.
