We developed a real-time measurement system including a large array of sensors and an on-chip analog front-end to realize signal amplification, filtering, noise and drift cancellation. We also designed a dynamic geomagnetic field cancellation technique to reduce noise sources such as the acoustic noise and disturbances of magnetic and electric fields from the earth and surrounding equipment.

We optimised the performance and size of the muscle sensors. According to finite-element analysis and experiment outcomes, the best overall noise performance is obtained with large arrays of large-area sensors. In addition, we introduce a low-profile magnetoelectric sensor with analogue frontend circuitry that has the sensitivity to measure pico-Tesla MMG signals at room temperature.

We developed a finite-element method model of muscle sensors and evaluated its performance of the sensitivity and linearization range. It provided a reliable benchmark for modelling future hybrid magnetic-CMOS developments. We believe that this structure can offer a platform to develop ultra-sensitive, smart and scalable sensors for muscle sensing.

Magnetomyography (MMG) is the study of muscle function through the inquiry of the magnetic signal that a muscle generates when contracted, first formally proposed in 1972. Within the last few decades, extensive effort has been invested to identify, characterize and quantify the MMG signals. However, it is still far from miniaturized, sensitive, inexpensive and low-power muscle sensors.