A wearable echomyography system based on a single transducer
Open accessAbstract
Wearable electromyography devices can detect muscular activity for health monitoring and body motion tracking, but this approach is limited by weak and stochastic signals with a low spatial resolution. Alternatively, echomyography can detect muscle movement using ultrasound waves, but typically relies on complex transducer arrays, which are bulky, have high power consumption and can limit user mobility. Here we report a fully integrated wearable echomyography system that consists of a customized single transducer, a wireless circuit for data processing and an on-board battery for power. The system can be attached to the skin and provides accurate long-term wireless monitoring of muscles. To illustrate its capabilities, we use this system to detect the activity of the diaphragm, which allows the recognition of different breathing modes. We also develop a deep learning algorithm to correlate the single-transducer radio-frequency data from forearm muscles with hand gestures to accurately and continuously track 13 hand joints with a mean error of only 7.9°.
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Ultrasound signals can serve as a key intermediate variable between the muscle and joint levels for decoding movement intent, as validated in joint angle and torque estimation.
“Furthermore, since muscle morphology often precedes and constrains joint movement, ultrasound signals can serve as a key intermediate variable between the muscle and joint levels for decoding movement intent, as validated in joint angle 118-120 and torque estimation [120][121][122] .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [118]
Acoustic sensors analyze ultrasound echoes to track muscle morphological dynamics and infer joint movement, and are used for muscle morphology monitoring, joint angle estimation, and torque estimation.
“Analyze ultrasound echoes to track muscle | morphological dynamics and infer joint | movement 214 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [118]
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- Authors
- Gao, X., Chen, X., Lin, M., Yue, W., Hu, H., Qin, S., Zhang, F., Lou, Z., Yin, L., Huang, H., Zhou, S., Bian, Y., Yang, X., Zhu, Y., Mu, J., Wang, X., Park, G., Lu, C., Wang, R., Wu, R. S., Wang, J., Li, J., & Xu, S.
- Affiliations
- University of California San Diego (US)
- Terasaki Foundation (US)
- Corresponding author
- Xu, Sheng
- ORCID
- Venue
- Nature Electronics
- Published
- 2024-10-31
- Volume
- 7
- Issue
- 11
- Pages
- 1035-1046
- ISSN
- 2520-1131 (ISSN-L)
- PMC
- PMC12269893
- OpenAlex
- W4403948534
- Cited by
- 95
- References
- 57
- Citations by year
- 2026: 53 · 2025: 41 · 2024: 1
- FWCI
- 10.43
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Electronics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Foundation for the National Institutes of Health (1R21EB027303-01A1, 3R21EB027303-02S1, 1R01EB033464-01)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- single transducer, diaphragm activity, forearm muscles, hand gesture tracking, wireless health monitoring, body motion tracking, deep learning
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