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 10 μm and peeling strength of larger than 1 N 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. Tattoo-like wearable electronic patches could help patients who have lost the capacity for speech to communicate once again. The aftermath of surgeries for mouth and throat cancers can result in permanent voice loss, but the information needed to decode speech can still be recovered from the movements of facial muscles. Researchers led by Hongmiao Zhang and Lining Sun at China’s Soochow University and Chengkuo Lee at the National University of Singapore have developed flexible sensors that can be applied to the skin to capture this information. In a series of tests with human volunteers, they demonstrated that vocalization-associated muscle activity collected from these patches could be computationally decoded with up to 92% accuracy. Similar devices could eventually be coupled to voice synthesizers or other devices to help restore normal speech after cancer surgery.