Devices that facilitate nonverbal communication typically require high computational loads or have rigid and bulky form factors that are unsuitable for use on the face or on other curvilinear body surfaces. Here, we report the design and pilot testing of an integrated system for decoding facial strains and for predicting facial kinematics. The system consists of mass-manufacturable, conformable piezoelectric thin films for strain mapping; multiphysics modelling for analysing the nonlinear mechanical interactions between the conformable device and the epidermis; and three-dimensional digital image correlation for reconstructing soft-tissue surfaces under dynamic deformations as well as for informing device design and placement. In healthy individuals and in patients with amyotrophic lateral sclerosis, we show that the piezoelectric thin films, coupled with algorithms for the real-time detection and classification of distinct skin-deformation signatures, enable the reliable decoding of facial movements. The integrated system could be adapted for use in clinical settings as a nonverbal communication technology or for use in the monitoring of neuromuscular conditions.
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The data supporting the results in this study are available within the paper and its Supplementary Information. The raw patient data are available from the corresponding author, subject to approval from the Institutional Review Board of the Massachusetts Institute of Technology.
Code used for addressing and capturing images from the cameras for 3D-DIC is available at GitHub (https://github.com/ConformableDecoders/PT-Grey-Image-Acquisition). Code used for 3D-DIC analysis is available at GitHub (https://github.com/MultiDIC/MultiDIC). Code used for RTD of facial deformations is available at GitHub (https://github.com/ConformableDecoders/cFaCES_RTD).
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C.D. thanks the late S. Hawking for the discussion on 25 April 2016 at the Harvard Society of Fellows, and for inspiring this research since then; Y. Büyükerşen for his suggestions on face painting material selections and wax sculpting; and M. Mercan for his support and discussions during the manuscript preparation. F.T. and C.D. thank D. Roy and D. Beeferman for initial discussions concerning kNN–DTW. F.T. thanks K. Warren for discussions on sensor characterization and R. Wiken for discussions on the design and fabrication of 3D-DIC set-ups. C.D., F.T. and T.S. thank R. Brown of the University of Massachusetts Medical School for helping to recruit the patients with ALS and for discussion on cFaCES application on the patients with ALS. We thank the families of P. Gerber and D. Ceruti for their help and dedication in trials of patients with ALS; and members of the microfabrication facility/cleanroom of the Conformable Decoders research group at the MIT Media Lab, the YellowBox and the Instron Laboratory of the Koch Institute For Integrative Cancer Research at MIT. C.D. acknowledges that this research was supported by MIT Media Lab Consortium funding and the National Science Foundation under NSF award no. 2026344. This work was performed in part at the Center for Nanoscale Systems (CNS), which is a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which was supported by the National Science Foundation under NSF award no. 1541959. CNS is part of Harvard University. M.A.K. acknowledges the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award no. R21EB023613, which partially supported this work. M.A.K. also acknowledges the National Science Foundation under grant no. 1905252, which partially supported this work. T.S. and Y.G. acknowledge the Institute of Microelectronics (IME), A*STAR, Singapore for funds for initial sensor fabrication and materials.
The authors declare no competing interests.
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Supplementary methods, figures and tables, and captions for Supplementary Videos 1–8.
Method to make cFaCES visually invisible with the facial skin.
3D-DIC set-up and an example of results of the trial for the motion OM without the sensor.
3D-DIC set-up and an example of the results of the trial for the motion PL without the sensor.
3D-DIC set-up and an example of the results of the trial for the motion SM without the sensor.
3D-DIC set-up and an example of the results of the trial for the motion OM with the sensor.
3D-DIC set-up and an example of the results of the trial for the motion PL with the sensor.
3D-DIC set-up and an example of the results of the trial for the motion SM with the sensor.
RTD set-up, trials and an example of usage.
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Sun, T., Tasnim, F., McIntosh, R.T. et al. Decoding of facial strains via conformable piezoelectric interfaces. Nat Biomed Eng 4, 954–972 (2020). https://doi.org/10.1038/s41551-020-00612-w
npj Flexible Electronics (2022)
Morphological Engineering of Sensing Materials for Flexible Pressure Sensors and Artificial Intelligence Applications
Nano-Micro Letters (2022)
Electronic Materials Letters (2022)
Light: Science & Applications (2021)
Nature Biomedical Engineering (2020)