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Self-powered wearable sensor platforms for wellness

Authors: Suman Datta; Jason Strohmaier; Mehmet Ozturk; Shekhar Bhansali; Veena Misra; Ben Calhoun; Omer Oralkan; +2 Authors

Self-powered wearable sensor platforms for wellness

Abstract

Health care continues to be one of the biggest challenges facing our society. Factors such as lifestyle choices, genetics, aging, stress and environmental exposures play a critical role in determining health outcomes. Wearable technologies that can enable continuous/long-term personal health monitoring and personal environmental monitoring can empower users to make better lifestyle decisions and improve health outcomes. While wearable devices promise a compelling future of achieving wellness, current wearable products are not addressing the needs of the health space. To achieve this future, key challenges in wearable systems such as battery life, form factor, sensor functionality, configurability and data analysis will have to be carefully addressed to ensure user adoption and effectively manage health.The NSF Engineering Research Center for Advanced Self-Powered Systems of Integrated Sensors and Technology (ASSIST) Center is addressing the above challenges by developing technologies that aim to maximize the power harvested from the body in the form of heat and movement/strain using flexible materials while simultaneously minimizing the power consumed via subthreshold CMOS computation and ultra-low power radios, thus shifting the balance, such that the system never needs to be recharged. In addition, ASSIST is building ultra-low power health sensors (EKG, hydration, pulse-oximetry, biochemical markers such as cortisol and lactates) and environmental exposure sensors (gasses, volatile organic compounds and particulate matter) that go much further than simple activity monitoring and provide a significantly more sophisticated understanding of human health through correlation of heterogeneous data streams. For example, sensors that can simultaneously measure an individual's environment exposure (ozone) and health response (wheezing) can enable a direct correlation of health and environment critical for Asthma and cardiovascular diseases. The entire ASSIST platform is aimed towards self-powered operation to enable continuous monitoring, which in turn can enable longitudinal health studies and assist in creating a paradigm shift towards data driven medicine. The power levels of the entire suite of sensing events are below 500 microwatts and in many cases around 100 microwatts. These levels are low enough to be sustained by the power harvested from the human body. This combination of flexible materials (energy harvesting and sensors), a positive power balance (energy harvesting and ultra-low power sensing, computation, and communication), and heterogeneous sensing modalities enables comfortable forever -- and therefore user-compliant -- operation of wearable sensor systems for longitudinal health monitoring of diverse user groups and assists in creating a paradigm shift towards personalized medicine and wellness management.The talk will discuss the Center's latest results on energy harvesting from the human body using flexible high-performance thermo-electrics and its accomplishments in ultra-low power management units, low power chip interfaces and low power radios. Multi-modal sensors with record low power consumption for health and environmental monitoring will also be presented. The integration of these enabling components into open-architecture platforms that enable measurement and analysis of the correlation between heart rate variability, respiratory rate variability and exposure to the ozone as an example study of the impact of environmental factors on physiological response in the context of asthma management will be discussed. The talk will also discuss recent successes that enable continuous ECG monitoring powered by energy harvesting that autonomously collects data without the need to change or charge a battery.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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