Design of Motion-Artifact Robust Electronic Tattoos and Software Reconfiguration Methodologies for Bio-impedance Sensing

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Electronic-tattoos (e-tattoos) are ultra-thin, ultra-soft sensors and electronics that can noninvasively adhere to human skin like a temporary transfer tattoo. Compared to the state-of-the-art wearable electronics, e-tattoos offer several exceptional characteristics. First, they conform to the skin and create a tight contact with the human body enabling robust signal measurements. Second, they may allow the skin to breathe eliminating the adverse effect of traditional adhesive patches. Lastly, they do not constrain natural skin motion hence present high degrees of comfort for the user. In other words, the user may "put it on and forget about it". E-tattoos are poised to enable new opportunities for the next generation of ubiquitous, unobtrusive and cost-effective health and wellness monitoring impacting the national health, bringing personalized care the individuals need to their homes. A strategic education and outreach effort focuses on broadening the participation of underrepresented groups in science and engineering via a year-long undergraduate research experience with enhanced graduate school preparation in partnership with Texas A & M University EnMed program and University of Texas-Austin NASCENT NSF Engineering Research Center. Emerging bio-impedance sensing offers new paradigms to capture a number of important physiological signals including heart rate, respiration rate and blood pressure, all around the human wrist. As the most dynamic body part, the wrist is under constant movement. The major challenge in bio-impedance sensing is the negative effects of motion artifacts that corrupt the data, degrade the signal fidelity, and prevent decision making with sufficient confidence. Our project leverages ultra-thin and ultra-soft e-tattoos for bio-impedance sensing on the wrist because e-tattoos enable the most intimate but noninvasive coupling for the electrodes and the human skin, even under severe skin deformation. Our project also explores software reconfiguration methodologies and machine learning techniques to further address the challenges. In particular, we investigate: 1) design and development of an array of submicron-thick, skin-conformable graphene electrode tattoos for the first time, and 2) novel reconfiguration techniques that would eliminate or reduce the noise associated with motion artifacts and enhance the signal fidelity.