Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects


Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the fully integrated USoP.
Fig. 2: Monitoring and analysis of tissue interface motions using the USoP.
Fig. 3: Autonomous and continuous blood pressure recording in a moving subject.
Fig. 4: Continuous monitoring during exercise.

Data availability

The data and material resources supporting the findings of this study are available within the article and supplementary materials. The raw data can be found in a publicly accessible repository77 (

Code availability

The codes used in M-mode image classification and domain adaptation can be found in a publicly available repository (


  1. Savoia, A. S., Caliano, G. & Pappalardo, M. A CMUT probe for medical ultrasonography: from microfabrication to system integration. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59, 1127–1138 (2012).

    Article  PubMed  Google Scholar 

  2. Shung, K. K., Cannata, J. & Zhou, Q. Piezoelectric materials for high frequency medical imaging applications: a review. J. Electroceram. 19, 141–147 (2007).

    Article  Google Scholar 

  3. Rothberg, J. M. et al. Ultrasound-on-chip platform for medical imaging, analysis, and collective intelligence. Proc. Natl Acad. Sci. USA 118, e2019339118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R. & Samir, A. E. Machine learning for medical ultrasound: status, methods, and future opportunities. Abdom. Radiol. 43, 786–799 (2018).

    Article  Google Scholar 

  5. Liu, S. et al. Deep learning in medical ultrasound analysis: a review. Engineering 5, 261–275 (2019).

    Article  Google Scholar 

  6. Powers, J. & Kremkau, F. Medical ultrasound systems. Interface Focus 1, 477–489 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Moran, C. M. & Thomson, A. J. Preclinical ultrasound imaging—a review of techniques and imaging applications. Front. Phys. 8, 124 (2020).

    Article  Google Scholar 

  8. Jensen, J. A. Medical ultrasound imaging. Prog. Biophys. Mol. Biol. 93, 153–165 (2007).

    Article  PubMed  Google Scholar 

  9. Price, D., Wallbridge, D. & Stewart, M. Tissue Doppler imaging: current and potential clinical applications. Heart 84 (Suppl. 2), ii11–ii18 (2000).

  10. Sigrist, R. M., Liau, J., El Kaffas, A., Chammas, M. C. & Willmann, J. K. Ultrasound elastography: review of techniques and clinical applications. Theranostics 7, 1303 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Poelma, C. Ultrasound imaging velocimetry: a review. Exp. Fluids 58, 3 (2017).

    Article  Google Scholar 

  12. Kasban, H., El-Bendary, M. & Salama, D. A comparative study of medical imaging techniques. Int. J. Inf. Sci. Intell. Syst. 4, 37–58 (2015).

    Google Scholar 

  13. Kalender, W. A. X-ray computed tomography. Phys. Med. Biol. 51, R29 (2006).

    Article  PubMed  Google Scholar 

  14. Katti, G., Ara, S. A. & Shireen, A. Magnetic resonance imaging (MRI)—a review. Int. J. Dent. Clin. 3, 65–70 (2011).

    Google Scholar 

  15. Díaz-Gómez, J. L., Mayo, P. H. & Koenig, S. J. Point-of-care ultrasonography. N. Engl. J. Med. 385, 1593–1602 (2021).

    Article  PubMed  Google Scholar 

  16. Kenny, J.-É. S. et al. A novel, hands-free ultrasound patch for continuous monitoring of quantitative Doppler in the carotid artery. Sci. Rep. 11, 7780 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hu, H. et al. Stretchable ultrasonic transducer arrays for three-dimensional imaging on complex surfaces. Sci. Adv. 4, eaar3979 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang, C. et al. Monitoring of the central blood pressure waveform via a conformal ultrasonic device. Nat. Biomed. Eng. 2, 687–695 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wang, C. et al. Continuous monitoring of deep-tissue haemodynamics with stretchable ultrasonic phased arrays. Nat. Biomed. Eng. 5, 749–758 (2021).

    Article  CAS  PubMed  Google Scholar 

  20. Wang, C. et al. Bioadhesive ultrasound for long-term continuous imaging of diverse organs. Science 377, 517–523 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. The Ultrasound Monitoring Patch (Pulsify Medical, 2016);

  22. Project Ulimpia (Penta, 2021);

  23. Baribeau, Y. et al. Handheld point-of-care ultrasound probes: the new generation of POCUS. J. Cardiothorac. Vasc. Anesth. 34, 3139–3145 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hu, H. et al. A wearable cardiac ultrasound imager. Nature 613, 667–675 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lin, M., Hu, H., Zhou, S. & Xu, S. Soft wearable devices for deep-tissue sensing. Nat. Rev. Mater. 7, 850–869 (2022).

    Article  Google Scholar 

  26. IEEE Standard for Information Technology–Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks-Specific Requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications (IEEE, 2016);

  27. Carovac, A., Smajlovic, F. & Junuzovic, D. Application of ultrasound in medicine. Acta Inform. Med. 19, 168 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Feigenbaum, H. Role of M-mode technique in today’s echocardiography. J. Am. Soc. Echocardiogr. 23, 240–257 (2010).

    Article  PubMed  Google Scholar 

  29. Gamble, G., Zorn, J., Sanders, G., MacMahon, S. & Sharpe, N. Estimation of arterial stiffness, compliance, and distensibility from M-mode ultrasound measurements of the common carotid artery. Stroke 25, 11–16 (1994).

    Article  CAS  PubMed  Google Scholar 

  30. Testa, A. et al. Ultrasound M-mode assessment of diaphragmatic kinetics by anterior transverse scanning in healthy subjects. Ultrasound Med. Biol. 37, 44–52 (2011).

    Article  PubMed  Google Scholar 

  31. Prada, G. et al. Echocardiographic applications of M-mode ultrasonography in anesthesiology and critical care. J. Cardiothorac. Vasc. Anesth. 33, 1559–1583 (2019).

    Article  PubMed  Google Scholar 

  32. Stabouli, S. et al. Comparison of the SphygmoCor XCEL device with applanation tonometry for pulse wave velocity and central blood pressure assessment in youth. J. Hypertens. 37, 30–36 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Elliot, C. A., Hamlin, M. J. & Lizamore, C. A. Inter-operator reliability for measuring pulse wave velocity and augmentation index. Front. Cardiovasc. Med. 7, 72 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Hoesein, F. A. M., Zanen, P. & Lammers, J.-W. J. Lower limit of normal or FEV1/FVC <0.70 in diagnosing COPD: an evidence-based review. Respir. Med. 105, 907–915 (2011).

    Article  Google Scholar 

  35. Johnson, J. D. & Theurer, W. M. A stepwise approach to the interpretation of pulmonary function tests. Am. Fam. Physician 89, 359–366 (2014).

    PubMed  Google Scholar 

  36. Limbu, Y. R., Gurung, G., Malla, R., Rajbhandari, R. & Regmi, S. R. Assessment of carotid artery dimensions by ultrasound in non-smoker healthy adults of both sexes. Nepal Med. Coll. J. 8, 200–203 (2006).

    PubMed  Google Scholar 

  37. Morerio, P., Cavazza, J. & Murino, V. Minimal-entropy correlation alignment for unsupervised deep domain adaptation. In International Conference on Learning Representations (2018).

  38. Magder, S. Volume and its relationship to cardiac output and venous return. Crit. Care 20, 271 (2016).

    Article  Google Scholar 

  39. White, D. W. & Raven, P. B. Autonomic neural control of heart rate during dynamic exercise: revisited. J. Physiol. 592, 2491–2500 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Laughlin, M. H., Korthuis, R. J., Duncker, D. J. & Bache, R. J. in Comprehensive Physiology (ed. Terjung, R.) 705–769 (Wiley, 2011).

  41. O’Rourke, M. F. & Mancia, G. Arterial stiffness. J. Hypertens. 17, 1–4 (1999).

    Article  PubMed  Google Scholar 

  42. Plowman, S. A. & Smith, D. L. Exercise Physiology for Health Fitness and Performance (Lippincott Williams & Wilkins, 2013).

  43. Salvi, P. Pulse Waves: How Vascular Hemodynamics Affects Blood Pressure 19–68 (Springer, 2017).

  44. Munir, S. et al. Exercise reduces arterial pressure augmentation through vasodilation of muscular arteries in humans. Am. J. Physiol. Heart. Circ. Physiol. 294, H1645–H1650 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Antonini-Canterin, F. et al. Arterial stiffness and ventricular stiffness: a couple of diseases or a coupling disease? A review from the cardiologist’s point of view. Eur. J. Echocardiogr. 10, 36–43 (2008).

    Article  PubMed  Google Scholar 

  46. Brett, S. E., Ritter, J. M. & Chowienczyk, P. J. Diastolic blood pressure changes during exercise positively correlate with serum cholesterol and insulin resistance. Circulation 101, 611–615 (2000).

    Article  CAS  PubMed  Google Scholar 

  47. Scolletta, S., Biagioli, B. & Giomarelli, P. in Anaesthesia, Pain, Intensive Care and Emergency A.P.I.C.E. (ed Gullo, A.) Ch 21 (Springer, 2007).

  48. Stöhr, E. J., González-Alonso, J. & Shave, R. Left ventricular mechanical limitations to stroke volume in healthy humans during incremental exercise. Am. J. Physiol. Heart. Circ. Physiol. 301, H478–H487 (2011).

    Article  PubMed  Google Scholar 

  49. Vieira, S. S. et al. Does stroke volume increase during an incremental exercise? A systematic review. Open Cardiovasc. Med. J. 10, 57 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Glynn, A. J. et al. The Physiotherapist’s Pocket Guide to Exercise E-Book: Assessment, Prescription and Training (Elsevier, 2009).

  51. Guiraud, T. et al. High-intensity interval training in cardiac rehabilitation. Sports Med. 42, 587–605 (2012).

    Article  PubMed  Google Scholar 

  52. Hannan, A. L. et al. High-intensity interval training versus moderate-intensity continuous training within cardiac rehabilitation: a systematic review and meta-analysis. Open Access J. Sports Med. 9, 1–17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Gao, W. et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 529, 509 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kim, J. et al. Battery-free, stretchable optoelectronic systems for wireless optical characterization of the skin. Sci. Adv. 2, e1600418 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Huang, X. et al. Materials and designs for wireless epidermal sensors of hydration and strain. Adv. Funct. Mater. 24, 3846–3854 (2014).

    Article  CAS  Google Scholar 

  56. Kim, J. et al. Miniaturized battery-free wireless systems for wearable pulse oximetry. Adv. Funct. Mater. 27, 1604373 (2017).

    Article  PubMed  Google Scholar 

  57. Naqvi, J., Yap, K. H., Ahmad, G. & Ghosh, J. Transcranial Doppler ultrasound: a review of the physical principles and major applications in critical care. Int. J. Vasc. Med. 2013, 629378 (2013).

    PubMed  PubMed Central  Google Scholar 

  58. Koski, J. M. et al. Assessing the intra- and inter-reader reliability of dynamic ultrasound images in power Doppler ultrasonography. Ann. Rheum. Dis. 65, 1658–1660 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Banegas, J. R. et al. Effectiveness of blood pressure control outside the medical setting. Hypertension 49, 62–68 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Cosson, E. et al. Detecting silent coronary stenoses and stratifying cardiac risk in patients with diabetes: ECG stress test or exercise myocardial scintigraphy? Diabet. Med. 21, 342–348 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Miyai, N. et al. Blood pressure response to heart rate during exercise test and risk of future hypertension. Hypertension 39, 761–766 (2002).

    Article  CAS  PubMed  Google Scholar 

  62. Lane, C. J. The inspection of curved components using flexible ultrasonic arrays and shape sensing fibres. Case Stud. Nondestr. Test. Eval. 1, 13–18 (2014).

    Article  Google Scholar 

  63. Hunter, A. J., Drinkwater, B. W. & Wilcox, P. D. Autofocusing ultrasonic imagery for non-destructive testing and evaluation of specimens with complicated geometries. NDT & E Int. 43, 78–85 (2010).

    Article  Google Scholar 

  64. Huang, Z. et al. Three-dimensional integrated stretchable electronics. Nat. Electron. 1, 473–480 (2018).

    Article  Google Scholar 

  65. Yin, L. et al. A self-sustainable wearable multi-modular E-textile bioenergy microgrid system. Nat. Commun. 12, 1542 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Baik, S. et al. Bioinspired adhesive architectures: from skin patch to integrated bioelectronics. Adv. Mater. 31, 1803309 (2019).

    Article  Google Scholar 

  67. Isozaki, A. et al. AI on a chip. Lab Chip 20, 3074–3090 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Gao, X. et al. A photoacoustic patch for three-dimensional imaging of hemoglobin and core temperature. Nat. Commun. 13, 7757 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Wang, F. et al. Flexible Doppler ultrasound device for the monitoring of blood flow velocity. Sci. Adv. 7, eabi9283 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, S. et al. Stretchable electronic facial masks for sonophoresis. ACS Nano 16, 5961–5974 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Jang, K.-I. et al. Self-assembled three dimensional network designs for soft electronics. Nat. Commun. 8, 15894 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lall, P., Goyal, K., Leever, B. & Marsh, J. Thermo-mechanical deformation in flexible-board assemblies during reflow and post-assembly usage. In Proc. 2018 7th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm) 26–31 (IEEE, 2018).

  73. Li, C. H., Guan, G. Y., Reif, R., Huang, Z. H. & Wang, R. K. K. Determining elastic properties of skin by measuring surface waves from an impulse mechanical stimulus using phase-sensitive optical coherence tomography. J. R. Soc. Interface 9, 831–841 (2012).

    Article  PubMed  Google Scholar 

  74. Park, Y. et al. Wireless, skin-interfaced sensors for compression therapy. Sci. Adv. 6, eabe1655 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  76. Hayashi, K., Handa, H., Nagasawa, S., Okumura, A. & Moritake, K. Stiffness and elastic behavior of human intracranial and extracranial arteries. J. Biomech. 13, 175–184 (1980).

    Article  CAS  PubMed  Google Scholar 

  77. Lin, M., Zhang, Z. & Gao, X. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. figshare (2023).

Download references


We thank Z. Wu, W. Qiu and X. Tian for guidance on the ultrasonic sensor and circuit design; A. Abdal for helping with mechanical simulation; and J. Li for sensor fabrication. The material was partially based on research sponsored by the Air Force Research Laboratory (AFRL) under agreement number FA8650-18-2-5402. The US Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the AFRL or the US Government. This research was partially supported by the National Institutes of Health (NIH) (grant no. 1 R01 EB033464-01). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. All biological experiments were conducted in accordance with the ethical guidelines with the approval of the Institutional Review Board of the University of California, San Diego.

Author information

Authors and Affiliations



M.Y.L., Z.Y.Z., X.X.G. and S. Xu designed the research. M.Y.L., Z.Y.Z., Y.Z.B., R.S.W., G.P. and Z.Y.L. performed the experiments. Z.Y.Z., Y.Z.B. and Z.R.Z. designed the signal processing algorithms. M.Y.L., Z.Y.Z. and S. Xu analyzed the data. M.Y.L., Z.Y.Z. and S. Xu wrote the paper. All authors provided constructive and valuable feedback on the paper.

Corresponding author

Correspondence to Sheng Xu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biotechnology thanks Pranav Rajpurkar, Michel Maharbiz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Characterizing bandwidth, axial resolution, and penetration of the stretchable ultrasonic probes.

a, Pulse-echo response and bandwidth of the probes with three frequencies. The full width at half maximum (FWHM) is labeled to show the axial resolution of each probe. The 2 MHz, 4 MHz and 6 MHz can achieve 604 μm, 333 μm and 229 μm resolution, respectively. Three probes could achieve a relative bandwidth of ~50% to their center frequencies at -3 dB. b, The pulse-echo response of a commercial ultrasound probe with a center frequency of 3 MHz, which could achieve a relative bandwidth of 42.3%. c, Tissue targets to be sensed by the stretchable ultrasonic probe in this work. The 2 MHz probe is used for deep organ (for example, heart and diaphragm) sensing. The 4 MHz probe is used for deep major artery (for example, carotid, femoral, and abdominal aorta) sensing. The 6 MHz probe is used for shallow peripheral artery (for example, radial and brachial) sensing. d, Transmission beam intensities as a function of penetration depth in tissues of the probes with different frequencies. The intensity decay was measured in water, and then converted into tissue decay with an attenuation factor of -0.3 dB/cm/MHz. Based on the penetration threshold of a -3 dB drop in intensity, the 2 MHz, 4 MHz and 6 MHz can penetrate 164.0 mm, 77.7 mm and 9.2 mm, respectively.

Extended Data Fig. 2 Schematics and control sequence of ultrasonic sensing.

a, Block diagram and signal transmission lines between the functional modules. The control circuit includes two parts: the AFE and the wireless DAQ module. The AFE consists of a multiplexer (Mux), a transmit/receive switch (T/R SW), a receiver, a sequencer, and a pulse generator. The DAQ module consists of a microcontroller (MCU) with an on-chip analog-to-digital convertor (ADC), and a Wi-Fi transmitter. The dashed lines are for digital signal transmission and the solid lines are for analog signal transmission. b, Simulated control sequence for multiplexing and pulse-echo sensing, which shows the time sequence of the receive (Rx) enable, trigger, high-voltage (HV) pulse, clock (CLK), reset (RES), digital input (Din), and latch enable (\(\overline{{\rm{LE}}}\)) signals. c, Signals acquired by an oscilloscope showing the control sequence of the pulse-echo sensing and transducer multiplexing. d, Signals acquired by an oscilloscope showing the input sequence to the shift register for multiplexing and driving the transducer elements. All figure panels share the same color encoding scheme.

Extended Data Fig. 3 Deformation of the packaged USoP.

a, 90° bending, b, 90° twisting, and c, 20% uniaxial stretching of the packaged USoP. d, A zoom-in view of the stretched interconnects.

Extended Data Fig. 4 Skin integration of the conformal USoP device.

The soft patch could conform to multiple curved body parts, including a, forearm, b, brachium, c, neck, d, lower chest, and e, abdomen. f-g, Skin integration of the device before and after exercise. The USoP could maintain robust adhesion to the skin after the subject performs intensive exercise and sweats.

Extended Data Fig. 5 Pulse wave velocity (PWV) measurements.

a, Schematic illustration of the pulse wave propagation paths in this study. Five paths were investigated, including the heart to the abdominal aorta (H-Ao), the heart to the carotid artery (H-CA), the heart to the femoral artery (H-FA), the heart to the brachial artery (H-BA), and the brachial artery to the radial artery (BA-RA). b, Pulse waveforms collected by synchronized USoP pairs. The pulse transit time (PTT) was defined as the delay between the diastolic feet of the ventricular contraction and arterial pulses. c, The average PTT values by the USoP and the tonometer, showing consistency for both H-BA and BA-RA. Ten consecutive pulses were recorded to calculate average PTT values. The error bars represent the measurement standard deviations. d, PWV calculated across five arterial segments using the USoP. e, PWV mapping under normal conditions and cold pressor test. The average PWV along each path was calculated from five independent measurements. The error bars indicate the standard deviations of the measured values. The PWV increases from heart-proximal to heart-distal branches. There is a regional increase of PWV in H-BA and BA-RA segments owing to cold-induced vasoconstriction.

Extended Data Fig. 6 The validation metrics of four models on ideal and compromised image datasets.

a, The images used for validation including ideal carotid artery images and compromised images (for example, noise coupled images, artery shifting images and artery missing images). b, The receiver operating characteristic curves validated on 460 ideal images, suggesting the best model VGG13 has an area under the curve value of 100%. c, The precision, recall and accuracy validated on ideal images. d, The receiver operating characteristic curves validated on 460 images with a mix of ideal and compromised images, suggesting the best model VGG13 has an area under the curve value of 99.4%. e, The precision, recall and accuracy validated on mixed ideal and compromised images.

Extended Data Fig. 7 Continuous monitoring during high-intensity interval training (HIIT).

a, Photographs showing the participant performing HIIT. Six training sessions, including (i) touch shoulder push-ups, (ii) cycling Russian twist, (iii) push-up rotations, (iv) burpees, (v) side kick through and (vi) hand-release push-ups. b, The head motions are recorded by the inertia measurement units, which show the rolling, yawing and pitching rates during the 12 min training and rest. The carotid blood pressure waveforms and heart rate are recorded simultaneously and continuously using the USoP. The systolic pressure increased ~25 mmHg between training sessions and rest sessions, while the diastolic pressure experienced less fluctuation. c, Zoomed-in view of the head motions, continuous blood pressure waveforms and heart rate recorded during the training sessions.

Supplementary information

Supplementary Information

Supplementary Discussions 1–20, Figs. 1–39, Tables 1–5 and Video captions 1–3.

Reporting Summary

Supplementary Video 1

B-mode imaging of the carotid artery and jugular vein. The cross-sectional structure of the carotid artery and a dilated jugular vein could be identified. The subject performed the Valsalva maneuver to dilate the jugular vein during the recording period.

Supplementary Video 2

Autonomous carotid artery tracking under head yawing. The USoP tracked the carotid artery motion while a commercial probe was manually placed adjacent to the USoP to image the carotid artery (left). The participant rotated the head to induce displacement of the carotid artery. The prediction profile was able to follow the moving artery during head rotation (right).

Supplementary Video 3

Continuous blood pressure waveforms recorded during cycling. A point-of-view camera was mounted on the participant’s head to record the motions during cycling (left). The carotid pulse waveforms were continuously recorded by the USoP. A smartphone application could host the data and display tissue motion images, pulse waveforms, heart rate and blood pressure values (right). The tissue motion images illustrated arterial wall positions. The continuous pulse waveforms showed the real-time pulsation of the carotid artery. The corresponding heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were displayed simultaneously.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, M., Zhang, Z., Gao, X. et al. A fully integrated wearable ultrasound system to monitor deep tissues in moving subjects. Nat Biotechnol (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing