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

合作研究：用于可穿戴设备应用的微机电神经集成传感和计算单元

基本信息

批准号：
1935598
负责人：
Siavash Pourkamali Anaraki
金额：
$ 15.55万
依托单位：
University of Texas at Dallas
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935598&HistoricalAwards=false
关键词：
Collaborative Research Micro Electro Mechanical

项目摘要

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.

随着可穿戴设备在消费市场中获得吸引力，对健康和行为的不引人注目和持续监测直接转化为改善健康和生活质量。这些平台提供了新的机会，以检测疾病的早期发作，评估人类绩效或提高生产力，以及许多其他潜在的应用。但是，这些设备的主要挑战是电池寿命。由于空间的严格要求，此类设备内的电池很小，可以通过执行复杂的算法（例如机器学习）和重型无线通信来快速排干。反过来，这迫使用户更频繁地向他们收费，并不鼓励对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械系统（MEMS）设备作为混合感应和计算元素的计算潜力，以使可穿戴设备有效地执行复杂的算法，同时保持电池电量。该项目通过提出一种可以由永久电池提供动力的新的，高智能的计算单元技术来影响美国行业的巨大潜力，并可以将其纳入许多医疗应用中。汇集了包括内布拉斯加林肯大学，得克萨斯大学达拉斯大学和德克萨斯A＆M大学在内的三个机构，该项目的结果也将在所有三个机构都被教授的各种课程中。从2020年开始，它也将在纳米里奇夏令营中使用，以通过MEMS和纳米工程的教育活动来促进来自代表性不足的团体的高中生的工程兴趣。该项目旨在为可穿戴设备开发一个超功率计算单元，以使可穿戴设备以当地性能的机器学习算法。该算法将编码在MEM的机械响应中，该算法也同时捕获了感兴趣的测量值，例如加速度。配备机器学习算法的可穿戴设备具有巨大的潜力，例如自动检测到跌倒。但是，由于空间的严格要求，此类设备中的电池很小，并且在大多数情况下，由多个MEMS传感器读出循环，无线通信和微处理器迅速排干。这直接导致了不遵守，因为用户必须经常向其设备充电，并且可能由于较少准确的算法而导致的错误警报遇到了麻烦，而这些算法较少，因此由于本地计算能力有限，因此必须使用的算法。为了克服这些挑战，提出了一种新的方法，将某些计算移至感应物理层。这种方法基于以下事实：MEMS设备的传感元素几乎不需要功率，并且它的机械响应与其他传感元素可以调节以自然地从自己的测量中执行机器学习算法。因此，与其产生需要放大，调节和转换从模拟转换为数字的行测量信号以由微处理器读取和处理，而是多个感应元素的响应将集体编码高级信息。这种方法将使可穿戴设备能够在本地执行先进的算法，同时比目前的最先进技术少使用两个数量级的功率。该奖项反映了NSF的法定任务，并认为值得通过基金会的智力优点和更广泛的影响审查标准通过评估来进行评估。