An epidermal sEMG tattoo-like patch as a new human–machine interface for patients with loss of voice

Throat cancer treatment involves surgical removal of the tumor,leaving patients with facial disfigurement as well as temporary or permanent loss of voice.Surface electromyography(sEMG)generated from the jaw contains lots of voice information.However,it is difficult to record because of not only the weakness of the signals but also the steep skin curvature.This paper demonstrates the design of an imperceptible,flexible epidermal sEMG tattoo-like patch with the thickness of less than 10μm and peeling strength of larger than 1N cm−1 that exhibits large adhesiveness to complex biological surfaces and is thus capable of sEMG recording for silent speech recognition.When a tester speaks silently,the patch shows excellent performance in recording the sEMG signals from three muscle channels and recognizing those frequently used instructions with high accuracy by using the wavelet decomposition and pattern recognization.The average accuracy of action instructions can reach up to 89.04%,and the average accuracy of emotion instructions is as high as 92.33%.To demonstrate the functionality of tattoo-like patches as a new human–machine interface(HMI)for patients with loss of voice,the intelligent silent speech recognition,voice synthesis,and virtual interaction have been implemented,which are of great importance in helping these patients communicate with people and make life more enjoyable.

All-weather,natural silent speech recognition via machine-learning-assisted tattoo-like electronics

The internal availability of silent speech serves as a translator for people with aphasia and keeps human–machine/human interactions working under various disturbances.This paper develops a silent speech strategy to achieve all-weather,natural interactions.The strategy requires few usage specialized skills like sign language but accurately transfers high-capacity information in complicated and changeable daily environments.In the strategy,the tattoo-like electronics imperceptibly attached on facial skin record high-quality bio-data of various silent speech,and the machine-learning algorithm deployed on the cloud recognizes accurately the silent speech and reduces the weight of the wireless acquisition module.A series of experiments show that the silent speech recognition system(SSRS)can enduringly comply with large deformation(~45%)of faces by virtue of the electricitypreferred tattoo-like electrodes and recognize up to 110 words covering daily vocabularies with a high average accuracy of 92.64%simply by use of small-sample machine learning.We successfully apply the SSRS to 1-day routine life,including daily greeting,running,dining,manipulating industrial robots in deafening noise,and expressing in darkness,which shows great promotion in real-world applications.