Collaborative Research: Micro-Electro-Mechanical Neural Integrated Sensing and Computing Units for Wearable Device Applications

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As wearable devices gain traction in the consumer market, unobtrusive and continuous monitoring of health and behavior directly translate to improving wellness and quality of life. These platforms provide new opportunities to detect the early onset of a disease, assess human performance, or enhance productivity, among many other potential applications. The principal challenge with these devices, however, is their battery life. Due to stringent space requirements, the batteries within such devices are small and can be quickly drained by performing sophisticated algorithms (e.g. machine learning) and heavy wireless communications. This, in turn, forces users to charge them more frequently and discourages widespread adoption of these devices. To overcome this challenge, the goal of the proposed project is to highlight the computational potential of micro-electro-mechanical-systems (MEMS) devices as hybrid sensing and computing elements to enable wearable devices to efficiently perform sophisticated algorithms while preserving their battery power. This project has tremendous potential to impact US industry by bringing forward a new, highly-intelligent computing unit technology that can be powered by a permanent battery and can be incorporated into many medical applications. Bringing together three institutions including the University Nebraska-Lincoln, the University of Texas at Dallas, and Texas A&M University The results of this project will also be adopted into various courses being taught at all three institutions. It will also be used in a NanoBridge summer camp beginning in 2020 to promote engineering interest among high school students from underrepresented groups through educational activities in MEMS and nanoengineering.This project aims to develop an ultra-power computing unit for wearable devices to locally perform machine-learning algorithms. The algorithms will be coded in the mechanical responses of MEMS that also simultaneously capture the measurement of interest, such as acceleration. Wearable devices equipped with machine learning algorithms hold great potential for saving lives, for example, by automatically detecting falls. However, due to stringent space requirements, the batteries within such devices are small and are quickly drained, for the most part, by multiple MEMS sensors read-out circuity, wireless communication, and microprocessors. This contributes directly to nonadherence as users must charge their devices frequently and may have trouble with false alarms caused by the less accurate algorithms that must be used due to limited local computing power. To overcome these challenges, a novel approach is proposed that moves some of the computing to the sensing physical layer. This approach builds on the fact that the sensing element of a MEMS device requires very little power, and its mechanical response coupled with other sensing elements can be tuned to naturally perform machine learning algorithms from their own measurements. Thus, rather than producing row measurement signals that need to be amplified, conditioned, and converted from analog to digital to be read and processed by a microprocessor, the response of the multiple sensing elements will collectively encode high-level information. This approach will enable wearable devices to locally perform advanced algorithms while consuming two orders of magnitude less power than present state-of-the-art technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.