IRES Track 1: Sensor Information Processing and Machine Learning for Wearable Devices

该IRES项目将传感器设备的进步与机器学习和数字信号处理（DSP）算法结合在一起，致力于提高移动的和可穿戴技术的精度。 亚利桑那州立大学（ASU）的教师和学生将与都柏林城市大学（DCU）数据分析洞察中心合作，该中心与亚利桑那州立大学在传感器、分析、机器学习（ML）、可穿戴设备和物联网（IoT）等多个领域建立了协同关系。 ASU带来了灵活的传感器，化学和生物传感器，统计信号处理，生物信息学和机器学习的专业知识。DCU带来了数据分析，人类活动监测，环境监测，人工智能，传感器分析和大数据分析方面的专业知识。 参与IRES的学生将在DCU Insight Center参加为期六周的沉浸式暑期课程，以积极提高他们的ML和传感器研究技能;参与者将制作和评估传感器分析，并为物联网，可穿戴设备和移动的健康监测创建算法和软件。将设立方案和讲习班，培训IRES参与者在国际环境中熟练有效地介绍他们的研究。每周在国际地点的演讲和国际导师的指导将丰富该群体的专业经验。在由欧盟（EU）和爱尔兰科学基金会（ISF）资助的DCU Insight中心嵌入学生将提供有关欧盟和国际研究实践，道德，标准和政策的知识。该项目的目标是：a）推进传感器和机器学习算法的集成设计科学，B）培训并使不同的学生群体能够在物联网系统的集成传感和ML方面做出研究贡献，c）获得部署AI，大数据系统和传感器的国际政策/标准的知识，以及d）提供经验，拓宽对全球实践和职业选择的理解。 该项目的动机是物联网，移动的健康和可穿戴系统需要廉价的传感器;为了达到必要的精度，传感器设计必须伴随着纠正ML和SP算法。 因此，IRES的研究重点是新传感器器件设计和新型ML算法开发的重叠。在ML方面，关键目标之一是开发适合与传感器和移动的设备集成的紧凑、低功耗算法。IRES项目领域包括灵活的传感器、传感器信息管理、高效的深度学习和大数据分析。 示例研究项目应用包括生物标志物检测、大数据处理、步态检测以及用于传感器和物联网系统的深度神经网络。工作将通过合作出版物和在国际会议和经评审的期刊上的介绍进行传播。ASU SenSIP和DCU Insight中心的行业参与将提供持续的有价值的反馈，年度外部评估将评估所有IRES活动的进展和成果。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This IRES project brings together advances in sensor devices with machine learning and digital signal processing (DSP) algorithms in an international research endeavor that promises to elevate precision in mobile and wearable technologies. Arizona State University (ASU) faculty and students will collaborate with the Dublin City University (DCU) Insight Center for Data Analytics, which has a synergistic relationship with ASU in several areas including sensors, analytics, machine learning (ML), wearables and Internet of Things (IoT). ASU brings expertise in flexible sensors, chemical and biosensors, statistical signal processing, bio-informatics, and machine learning. DCU brings expertise in data analytics, human activity monitoring, environmental monitoring, artificial intelligence, sensor analytics and big data analysis. IRES participating students will spend an immersive six-week summer program at the DCU Insight Center to actively improve their ML and sensor research skills; participants will produce and evaluate sensor analytics, and create algorithms and software for IoT, wearables and mobile health monitoring. Programs and workshops will be established to train IRES participants to skillfully and effectively present their research in international settings. Weekly presentations at the international site and guidance by international mentors will enrich the cohort’s professional experience. Embedding students in the DCU Insight center funded by European Union (EU) and Irish Science Foundation (ISF) grants will provide knowledge on EU and international research practices, ethics, standards and policies.The goals of this project are to: a) advance the science of integrated design of sensors and machine learning algorithms, b) train and enable a diverse cohort of students to make research contributions in integrated sensing and ML for IoT systems, c) gain knowledge on international policies/standards of deploying AI, big data systems, and sensors, and d) provide experiences that broaden understanding of global practices and career options. This project is motivated by the fact that inexpensive sensors are required for IoT, mobile health and wearable systems; to achieve the requisite precision, sensor design must be accompanied by corrective ML and SP algorithms. IRES research therefore focuses on the overlap of new sensor device design and novel ML algorithm development. In terms of ML, one of the key objectives is to develop compact, low power algorithms adequate for integration with sensors and mobile devices. IRES project areas include flexible sensors, sensor information management, efficient deep learning, and big data analysis. Example research project applications include biomarker detection, big data processing, gait detection, and deep neural nets for sensor and IoT systems. Work will be disseminated via collaborative publications and presentations in international conferences and refereed journals. Industry engagement at the ASU SenSIP and DCU Insight centers will provide ongoing valuable feedback, and annual external evaluation will assess progress and outcomes across all IRES activities.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.