A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

Abstract

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.

AI‐Enabled Soft Sensing Array for Simultaneous Detection of Muscle Deformation and Mechanomyography for Metaverse Somatosensory Interaction

“…To further investigate the MMG signals, a 5‐Hz high‐pass filter was applied to the time‐domain signals to remove the lower‐frequency signals because this frequency threshold (5 Hz) was commonly used in previous research to isolate pure MMG signals. [ 65 , 66 ] Figure 3a(iv) shows the filtered time‐domain signals (MMG signals) of the calf muscles for motion A8, where the signal amplitude (that is, the value of the change rate) substantially decreased compared to the original signals. This reduction implies that the large‐amplitude signals were primarily caused by muscle shape changes, which have a human‐subject‐controlled motion frequency (<1 Hz).…”

“…The FFT spectra of the signals obtained over the entire motion time featured peaks below 1 Hz, which mainly reflected the human-controlled frequency for lower-limb was commonly used in previous research to isolate pure MMG signals. [65,66] The responses of all 16 channels to the ten motions are provided in Figure 3b, and the corresponding FFT plot is presented in Figure S5 (Supporting Information). Each channel responds differently to these motions.…”

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

“…There are three main types of strain−sensing devices based on the method of signal extraction: piezoelectric [31,32], capacitive, and resistive. Piezoelectric sensors are self−generating electromechanical transducers.…”

Lower Limb Exoskeleton Sensors: State-of-the-Art

“…When myographic signals are used in the exoskeletons field, one of the essential areas of optimization is the number and placement of the sensors used [ 72 ]. To enforce endeavors to improve sensing quality, multimodal approaches as well as new sensor configurations are looked for.…”