An epidermal sEMG tattoo-like patch as a new human–machine interface for patients with loss of voice
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Abstract 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.
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Liu et al. fabricated an epidermal sEMG tattoo-like patch that could be laminated on the jaw and face and monitored the sEMG from a patient who had lost their voice.
“Liu et al. [225] fabricated an epidermal sEMG tattoo-like patch that could be laminated on the jaw and face and monitored the sEMG from a patient who had lost their voice.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [225]
Action instructions from silent speech recognition to control an intelligent car, emotion instructions to control a Bluetooth speaker, and virtual interaction in real time could be recognized with high accuracy using wavelet decomposition and machine-learning algorithms.
“The action instructions from silent speech recognition to control an intelligent car, emotion instructions to control a Bluetooth speaker, and virtual interaction in real time could be recognized with high accuracy using wavelet decomposition and machine-learning algorithms.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [225]
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The emergence of AI-based wearable sensors for digital health technology: a review
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- Authors
- Liu, H., Dong, W., Li, Y., Li, F., Geng, J., Zhu, M., Chen, T., Zhang, H., Sun, L., & Lee, C.
- Affiliations
- Soochow University (CN)
- National University of Singapore (SG)
- Corresponding author
- Zhang, Hongmiao
- Sun, Lining
- Lee, Chengkuo
- ORCID
- Venue
- Microsystems & Nanoengineering
- Published
- 2020-03-09
- Volume
- 6
- Issue
- 1
- Pages
- 16-16
- ISSN
- 2055-7434 (ISSN-L)
- PMC
- PMC8433406
- OpenAlex
- W3010458192
- Cited by
- 144
- References
- 53
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 26 · 2023: 33 · 2022: 23 · 2021: 18 · 2020: 9
- FWCI
- 6.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Microsystems & Nanoengineering (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1713218, No. U1713218, 2018YFB1307700)
- Soochow University
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- surface electromyography, human-machine interaction, voice loss, silent speech recognition, throat cancer, flexible electronics, wavelet decomposition, speech synthesis, virtual interaction
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