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.
