A Multipurpose Human–Machine Interface via 3D-Printed Pressure-Based Force Myography

Artificially intelligent (AI), powerful, and reliable human–machine interfaces (HMIs) are highly desired for wearable technologies, which proved to be the next advancement when it comes to humans interacting with physical, digital, and mixed environments. To demonstrate them, here we report on an innovative noninvasive, lightweight, low-cost, wearable, and soft pressure-based force myography (pFMG) HMI in the form of an armband. The armband acquires stable mechanical biosignals in the form of air pressure information in response to forces induced by muscle activity consisting of contraction and relaxation that deform its pressure-sensitive chambers (PSCs). The PSCs are characterized by a fast response to a mechanical biosignal, negligible hysteresis, repeatability, reproducibility, reliability, stability, minimal calibration requirements, and durability (more than 1 500 000 cycles). The pFMG armband is resistant to sweat, body hair present on the skin, worn cloth, and scars, and resilient to external mechanical deformations. We demonstrate the capability and versatility of the pFMG-based HMI armband to interact with and control collaborative robot manipulators, robotic prosthetic hands, drones, computer games, and any system where humans are in the loop. The control signals are generated through the implementation of a machine learning algorithm to decode and classify the acquired biosignals of different hand gestures to rapidly and accurately recognize the intentions of a user. The easy and direct fabrication and customization of the armband in addition to its ability to decode any desired gesture rapidly and reliably based on stable and reliable biosignals makes it ideal to be integrated into AI-powered HMI applications.