Hardware Friendly Machine Learning Integrated Circuits and System for Low Power Wearable Wireless Electrocardiogram Sensors

适用于低功耗可穿戴无线心电图传感器的硬件友好型机器学习集成电路和系统

• 批准号: 2015573
• 负责人: Wei Tang
• 金额: $ 40万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015573&HistoricalAwards=false
• 关键词: Hardware; Friendly; Machine; Learning; Integrated

Future wearable wireless biomedical sensors demand novel technologies to overcome the increasing challenge in implementing intelligent signal sensing and processing, the shortage of battery lifetime, as well as latency and security issues. For instance, one major problem with the current U.S. health care systems is that sensing and processing medical data require significant and costly resources. To alleviate this problem, wearable medical devices are expected to provide automatic monitoring and processing of physiological signals and be capable of identifying abnormal signals and contacting medical systems if necessary. Such devices are the key components in the future "unmanned medical nursing systems". The goal of this project is to ultimately address the challenges of next-generation wireless wearable biomedical sensors by systematical efforts, which include interrelated studies in low power circuit design, hardware-friendly algorithm design, and communication system analysis. The project also covers the integrated circuit design and characterization of the overall wearable sensor with the power budget estimation of each individual building block. Since the power-efficient smart wireless device is a critical component for a wide range of existing and emerging mobile sensing systems, the outcomes of this project can result in a direct technological and societal impact on the quality of our lives. To validate the benefits, the project plans to directly explore the research impact of the proposed systems in elderly care applications, which is a particularly important topic for the state of New Mexico. In addition to training undergraduate and graduate students via the proposed research projects, the educational impact of the activities outlined in this project includes increasing participation of minority students and attracting high school students to STEM college programs. Wearable Electrocardiogram (ECG) sensors are one of the important wearable medical devices for arrhythmia detection, as continuous ECG monitoring is needed by patients and even by normal people with uncomfortable heart feelings. A Wearable ECG sensor with wireless body sensor networks is one of the best candidates. Recently, machine learning has become a promising solution and has been applied to continuous monitoring of physiological signals for on-sensor processing. Due to latency, security, and privacy requirement, on-sensor processing rather than sending the raw data to the cloud is preferred in medical devices. Therefore, a machine learning algorithm that can accommodate real-time processing without too much data storage and movement is preferred in wearable sensor applications. This project addresses the fundamental computing issue of the above-mentioned technical challenges in wearable wireless biomedical sensors, especially ECG sensors. The goal is to find an alternative sensing and processing circuit architecture to enable low-computing overhead machine learning algorithms for power-limited wireless sensors with local processing capability. The research aims to significantly advance the state-of-the-art in low-power wireless wearable biomedical sensor architecture by employing ideas in a cross-disciplinary fashion from integrated circuits, hardware-friendly machine learning algorithms, and power-efficient wireless communication systems.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.

未来的可穿戴无线生物医学传感器需要新的技术来克服实现智能信号传感和处理，电池寿命不足以及延迟和安全问题方面日益严峻的挑战。例如，当前美国医疗保健系统的一个主要问题是感测和处理医疗数据需要大量且昂贵的资源。为了缓解这一问题，可穿戴医疗设备有望提供生理信号的自动监测和处理，并能够识别异常信号，并在必要时联系医疗系统。这些设备是未来“无人医疗护理系统”的关键部件。本计画的目标是透过系统性的研究，以解决下一代无线可穿戴生物医学感测器所面临的挑战，包括低功率电路设计、硬体友善演算法设计、以及通讯系统分析等相关研究。该项目还包括整体可穿戴传感器的集成电路设计和表征，以及每个构建块的功率预算估计。由于高能效智能无线设备是各种现有和新兴移动的传感系统的关键组件，因此该项目的成果可能会对我们的生活质量产生直接的技术和社会影响。为了验证这些好处，该项目计划直接探索拟议系统在老年人护理应用中的研究影响，这对新墨西哥州来说是一个特别重要的话题。除了通过拟议的研究项目培训本科生和研究生外，该项目中概述的活动的教育影响包括增加少数民族学生的参与，吸引高中生参加STEM大学课程。可穿戴心电图（ECG）传感器是用于心律失常检测的重要可穿戴医疗设备之一，因为患者甚至心脏感觉不舒服的正常人都需要连续的ECG监测。一个可穿戴心电传感器与无线身体传感器网络是最好的候选人之一。最近，机器学习已经成为一种很有前途的解决方案，并已被应用于连续监测生理信号，以进行传感器处理。由于延迟、安全性和隐私要求，在医疗设备中，传感器处理而不是将原始数据发送到云是首选。因此，在可穿戴传感器应用中，可以适应实时处理而没有太多数据存储和移动的机器学习算法是优选的。该项目解决了可穿戴无线生物医学传感器，特别是心电传感器中上述技术挑战的基本计算问题。我们的目标是找到一种替代的传感和处理电路架构，以实现低计算开销的机器学习算法，用于具有本地处理能力的功率受限的无线传感器。该研究旨在通过采用集成电路，硬件友好的机器学习算法，和权力-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

Joint Learning and Channel Coding for Error-Tolerant IoT Systems based on Machine Learning

基于机器学习的容错物联网系统的联合学习和信道编码

• DOI: 10.1109/tai.2023.3235778
• 发表时间: 2023
• 期刊: IEEE Transactions on Artificial Intelligence
• 作者: Tang, Xiaochen;Reviriego, Pedro;Tang, Wei;Mitchell, David G.;Lombardi, Fabrizio;Liu, Shanshan
• 通讯作者: Liu, Shanshan

Dynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing

• DOI: 10.1109/tcsii.2023.3238279
• 发表时间: 2022-11
• 期刊: IEEE Transactions on Circuits and Systems II: Express Briefs
• 作者: Xiaochen Tang;Mario Renteria-Pinon;Wei-Chien Tang

An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors

使用三元二阶 Delta 调制器对可穿戴 ECG 传感器进行斜率变化测量的 ECG 描绘和心律失常分类系统

• DOI: 10.1109/tbcas.2021.3113665
• 发表时间: 2021
• 期刊: IEEE Transactions on Biomedical Circuits and Systems
• 影响因子: 5.1
• 作者: Tang, Xiaochen;Tang, Wei
• 通讯作者: Tang, Wei

Real-Time In-Sensor Slope Level-Crossing Sampling for Key Sampling Points Selection for Wearable and IoT Devices

用于可穿戴和物联网设备关键采样点选择的实时传感器内斜率平交采样

• DOI: 10.1109/jsen.2023.3243460
• 发表时间: 2023
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Renteria-Pinon, Mario;Tang, Xiaochen;Tang, Wei
• 通讯作者: Tang, Wei

A Near-sensor ECG Delineation and Arrhythmia Classification System

近传感器心电图描绘和心律失常分类系统

• DOI: 10.1109/jsen.2022.3183136
• 发表时间: 2022
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Tang, Xiaochen;Liu, Shanshan;Reviriego, Pedro;Lombardi, Fabrizio;Tang, Wei
• 通讯作者: Tang, Wei