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

随着可穿戴设备在消费者市场的吸引力越来越大，对健康和行为的不显眼和持续的监测直接转化为改善健康和生活质量。这些平台为检测疾病的早期发作、评估人类表现或提高生产力以及许多其他潜在应用提供了新的机会。然而，这些设备的主要挑战是它们的电池寿命。由于严格的空间要求，这些设备内的电池很小，并且可以通过执行复杂的算法（例如机器学习）和繁重的无线通信而快速耗尽。这反过来又迫使用户更频繁地充电，并阻碍了这些设备的广泛采用。为了克服这一挑战，拟议项目的目标是突出微机电系统（MEMS）设备作为混合传感和计算元件的计算潜力，使可穿戴设备能够有效地执行复杂的算法，同时保持电池电量。该项目具有巨大的潜力，通过提出一种新的、高度智能的计算单元技术来影响美国工业，该技术可以由永久电池供电，并可以融入许多医疗应用。汇集了三个机构，包括内布拉斯加大学林肯分校，得克萨斯大学达拉斯分校和得克萨斯A M大学。该项目的结果也将被纳入所有三个机构正在教授的各种课程中。该项目还将用于2020年开始的NanoBridge夏令营，通过MEMS和纳米工程的教育活动，提高高中生对工程的兴趣。该项目旨在开发可穿戴设备的超功率计算单元，以在本地执行机器学习算法。这些算法将在MEMS的机械响应中编码，同时捕获感兴趣的测量值，例如加速度。配备机器学习算法的可穿戴设备具有巨大的拯救生命的潜力，例如，通过自动检测福尔斯。然而，由于严格的空间要求，这种设备内的电池很小，并且在大多数情况下，通过多个MEMS传感器读出电路、无线通信和微处理器快速耗尽。这直接导致了不遵守，因为用户必须频繁地为他们的设备充电，并且由于有限的本地计算能力而必须使用的不太准确的算法可能会导致错误警报。为了克服这些挑战，提出了一种新的方法，将一些计算移动到感测物理层。这种方法建立在MEMS器件的感测元件需要非常少的功率的事实上，并且其与其他感测元件耦合的机械响应可以被调谐以根据其自身的测量自然地执行机器学习算法。因此，多个感测元件的响应将共同编码高级信息，而不是产生需要被放大、调节和从模拟转换为数字以由微处理器读取和处理的行测量信号。这种方法将使可穿戴设备能够在本地执行先进算法，同时消耗的功率比目前最先进的技术少两个数量级。该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估被认为值得支持优点和更广泛的影响审查标准。

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.