Smart and Connected Physiological Monitoring Enabled by Stretchable Bioelectronics and Deep-Learning Algorithm

Commercially available, wearable physiological monitors rely on rigid, multiple electronic components, coupled with aggressive adhesives and conductive gels, often causing discomfort and skin breakdown. Here, we introduce an all-in-one, wireless, stretchable bioelectronics platform for portable, real-time physiological monitoring and accurate classification biopotentials, including electrocardiograms (ECG), electroencephalograms (EEG), and electromyograms (EMG). The nanomembrane sensor and multi-layered electronic system is manufactured by integration of microfabrication techniques, aerosol jet printing of nanoparticles, photonic sintering, and hard-soft materials assembly. Strategic integration with hyperelastic elastomers allows the device to adhere and deform naturally with human body while maintaining the functionalities of the on-board electronics. Stretchable electrodes with optimized structures for intimate skin contact acquire high-quality biopotentials. Comparison of those signals with commercial systems captures the improved performance and significant noise reduction of the stretchable bioelectronics. Implementation of convolutional neural networks for real-time classifications of ECG, EMG, EEG and inertial measurement data demonstrates the feasibility for precise control of external systems. In vivo demonstrations with human subjects in various scenarios reveal the versatility of the device as both a health monitor with real-time cardiac monitoring and a viable human-machine interface.

[1]  Lida Xu,et al.  EMG and EPP-Integrated Human–Machine Interface Between the Paralyzed and Rehabilitation Exoskeleton , 2012, IEEE Transactions on Information Technology in Biomedicine.

[2]  Dong Sup Lee,et al.  Soft, conformal bioelectronics for a wireless human-wheelchair interface. , 2017, Biosensors & bioelectronics.

[3]  M. Renn,et al.  Printing conformal electronics on 3D structures with Aerosol Jet technology , 2012, 2012 Future of Instrumentation International Workshop (FIIW) Proceedings.

[4]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[5]  Dinesh Kumar,et al.  Classification of EOG for human computer interface , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[6]  Woon-Hong Yeo,et al.  Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm , 2019, Nature Machine Intelligence.

[7]  James J. S. Norton,et al.  Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface , 2015, Proceedings of the National Academy of Sciences.

[8]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Woon-Hong Yeo,et al.  All‐in‐One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring , 2019, Advanced science.

[10]  Marek Kurzynski,et al.  Human-machine interface in bioprosthesis control using EMG signal classification , 2010, Expert Syst. J. Knowl. Eng..

[11]  Jeong-Su Han,et al.  Human-machine interface for wheelchair control with EMG and its evaluation , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[12]  L. Francis,et al.  Optimization of aerosol jet printing for high-resolution, high-aspect ratio silver lines. , 2013, ACS applied materials & interfaces.

[13]  Woon-Hong Yeo,et al.  Soft Material-Enabled, Flexible Hybrid Electronics for Medicine, Healthcare, and Human-Machine Interfaces , 2018, Materials.

[14]  Mark E. Josephson,et al.  Practical Clinical Electrophysiology , 2017 .

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Dong Sup Lee,et al.  Wireless, intraoral hybrid electronics for real-time quantification of sodium intake toward hypertension management , 2018, Proceedings of the National Academy of Sciences.

[17]  James J. S. Norton,et al.  Materials and Optimized Designs for Human‐Machine Interfaces Via Epidermal Electronics , 2013, Advanced materials.

[18]  Tzyy-Ping Jung,et al.  A brain-machine interface using dry-contact, low-noise EEG sensors , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[19]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[20]  Jose L. Contreras-Vidal,et al.  Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors , 2016, Front. Neurosci..

[21]  Ralf Bousseljot,et al.  Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet , 2009 .

[22]  A. Pelliccia,et al.  Prevalence of abnormal electrocardiograms in a large, unselected population undergoing pre-participation cardiovascular screening. , 2007, European heart journal.