CAREER: Pyramidal Intelligence for Ultra-low-power Wearable Massive-sensor Computers

职业：超低功耗可穿戴大规模传感器计算机的金字塔智能

• 批准号: 2047849
• 负责人: Qingxue Zhang
• 金额: $ 48.59万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047849&HistoricalAwards=false
• 关键词: CAREER; Pyramidal; Intelligence; Ultra; low

The era of big data is prompting a large-scale deployment of on-body monitors towards wearable massive-sensor computers. These massive sensors have promising and broad prospects to greatly advance big data-driven precision health, through comprehensively capturing behavioral, physiological or biological signals from the human body. Nevertheless, because of the big data volume brought by massive sensors, the system is very power-hungry and thus it is very pressing to innovate an ultra-low-power architecture. Targeting this crucial challenge, this project aims to develop new design methodologies, techniques and implementations to achieve a generalizable ultra-low-power architecture for wearable massive-sensor computers. Concretely, this project seeks to leverage novel deep learning approaches to minimize the power consumption of the system. Firstly, the data characteristics of sensor streams will be learned by deep learning to analyze, evaluate, and measure the redundancy in the data, which will then be used to activate just-enough sensors. The deep learning will learn the signal dynamics to intelligently determine the sensor activation schemes. Besides, the data on the activated sensors will be further analyzed and compressed to minimize the power consumption. The signal fluctuations and patterns will be learned by efficient deep learning models and then be encoded to sparsified representations. Real-world experiments will also be conducted to evaluate and validate the effectiveness of the proposed ultra-low-power architecture. This project will develop a new ultra-low-power architecture to enable energy-efficient wearable massive-sensor computers, and thus greatly advance their real-world deployment. This new architecture will dramatically boost the battery life, enhance the usability, and improve the long-term data capturing capability of the wearable sensors. This is essential for big data-driven precision health. The achieved human big data will effectively contribute to the study of time-varying, nonlinear, and unknown dynamics of the human body, and broadly benefit many areas like fitness and lifestyle management, medical decision support, disease model establishment, individualized treatment plan, and population-level big data mining. The research findings from this project will be broadly disseminated to the scientific communities, medical areas and other communities. The broad impact of this proposal also stems from the educational program for students from K-12 to undergraduate and graduate levels, through efforts like attracting undergraduate, women and underrepresented students to research, training students in real-world problem solving, and broader research training of high school students and outreach to K-12. This systematic plan of integrating education to research aims to train the next generation of professional STEM researchers and engineers.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.

大数据时代正在推动大规模部署身体监测器，转向可穿戴的巨型传感器计算机。这些海量传感器通过全面捕获人体的行为、生理或生物信号，极大地推进了大数据驱动的精准健康，具有广阔的前景。然而，由于海量传感器带来的大数据量，系统非常耗电，因此创新超低功耗架构非常紧迫。针对这一关键挑战，该项目旨在开发新的设计方法、技术和实现，以实现可通用的超低功耗架构，用于可穿戴的大规模传感器计算机。具体地说，该项目寻求利用新颖的深度学习方法来最大限度地减少系统的功耗。首先，通过深度学习来学习传感器流的数据特征，以分析、评估和测量数据中的冗余，然后使用这些冗余来激活恰好足够的传感器。深度学习将学习信号动力学，以智能地确定传感器激活方案。此外，还将对激活的传感器上的数据进行进一步的分析和压缩，以最大限度地减少功耗。信号的波动和模式将通过有效的深度学习模型学习，然后被编码为稀疏表示。还将进行真实世界的实验，以评估和验证所提出的超低功耗架构的有效性。该项目将开发一种新的超低功耗架构，使能效高的可穿戴大规模传感器计算机能够实现，从而极大地推动其在现实世界中的部署。这种新的架构将极大地延长电池寿命，增强可用性，并提高可穿戴传感器的长期数据捕获能力。这对于大数据驱动的精准健康至关重要。人类大数据的实现将有效地促进人体时变、非线性和未知动力学的研究，并广泛惠及健康和生活方式管理、医疗决策支持、疾病模型建立、个性化治疗计划和人口级大数据挖掘等领域。该项目的研究成果将广泛传播给科学界、医学界和其他社区。这项提议的广泛影响还来自于为从K-12到本科生和研究生的学生提供的教育计划，通过吸引本科生、女性和代表性不足的学生进行研究、培训学生解决现实世界的问题、为高中生提供更广泛的研究培训以及推广到K-12等努力。这项将教育与研究相结合的系统计划旨在培养下一代专业的STEM研究人员和工程师。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Deep Learning of Biomechanical Dynamics With Spatial Variability for Lifestyle Management

具有空间变异性的生物力学动力学深度学习用于生活方式管理

• DOI: 10.1109/lifetech53646.2022.9754916
• 发表时间: 2022
• 期刊: 2022 IEEE 4th Global Conference on Life Sciences and Technologies
• 作者: Gangadharan, Kiirthanaa;Zhang, Qingxue
• 通讯作者: Zhang, Qingxue

Big Data Edge on Consumer Devices for Precision Medicine

精准医疗消费设备上的大数据边缘

• DOI: 10.1109/icce53296.2022.9730484
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Consumer Electronics (ICCE
• 作者: Stauffer, Jake;Zhang, Qingxue
• 通讯作者: Zhang, Qingxue

Deep Transferable Intelligence for Spatial Variability Characterization and Data-Efficient Learning in Biomechanical Measurement

• DOI: 10.1109/tim.2023.3265753
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
• 影响因子: 5.6
• 作者: Gangadharan, Kiirthanaa;Zhang, Qingxue
• 通讯作者: Zhang, Qingxue

Deep Reinforcement Learning with IoT System Characterization and Knowledge Adaptation

• DOI: 10.1109/uemcon54665.2022.9965641
• 发表时间: 2022-10
• 期刊: 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)
• 作者: Jiadao Zou;Qingxue Zhang

Wearable Big Data Pertinence Learning with Deep Spatiotemporal co-Mining

• DOI: 10.1109/i2mtc48687.2022.9806704
• 发表时间: 2022-05
• 期刊: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
• 作者: J. Wong;Qingxue Zhang