I-Corps: Intelligent wireless sensor network platform for extended human health monitoring

这个i-Corps项目的更广泛的影响/商业潜力是开发一个通用的健康监测平台，使生理信号能够进行智能监测。该项目旨在提高可穿戴设备的电池寿命和隐私，实现对个人健康和医疗条件的长期监测。该系统适用于各种生物参数信号，可用于一般消费者健康监测，告知用户自己的健康状况，激励生活方式的改变，并检测紧急情况。该系统还应用于重症监护，通过在保持长寿命的情况下移除电线来改善长期监测患者的舒适度。对压力、情绪、声音和运动等特定信号的监测可以为军事应用提供有用的工具，包括战场上的士兵监测、士兵训练和创伤后疾病。该系统还可以为专门的体育运动监测甚至专门的医疗条件进行定制。例如，中风复发等慢性病患者可以从心脏监测中受益，体温监测可以预测对病毒的免疫反应，这样患者就可以得到治疗，呼吸跟踪可以预测哮喘发作的风险。总体而言，该系统具有改善普通公众健康监测的潜力，并有能力让慢性病患者在危急事件之前得到治疗。这个i-Corps项目基于开发一个智能无线传感器网络平台，通过传感器内机器学习来扩展人类健康监测。该技术使用新兴的大脑启发的超维计算，其特点是计算复杂性非常低，以最大限度地减少网络功耗，提高网络寿命、安全性和隐私。该技术能够集成大量低功率分布式传感器，这些传感器可以无线通信智能检测到的事件/类。当前的系统或者将数据传输到另一设备进行处理，这在功率方面非常昂贵，或者尝试使用比减少传输所节省的量更昂贵的算法来本地处理。为了解决这些问题，本项目利用新兴的大脑启发的超维计算范例来最大限度地减少传感器内计算的净功耗。这种范例用完全二进制向量表示信息，因此数据模式的编码只涉及简单的二元运算符，如右移，这使得它在计算上非常简单。通过将训练数据编码成每个类的原型向量，然后在推理期间，将类似编码的输入数据与训练的原型进行比较以找到最接近的类，该表示可用于分类任务。使用该技术，该方案不仅可以显著提高传感器的使用寿命，而且由于计算的局部性，还可以提供安全性和保密性。该项目旨在开发一个利用这些不同因素进行长期监测的平台。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。

The broader impact/commercial potential of this I-Corps project is the development of a generic health monitoring platform that enables intelligent monitoring of physiological signals. This project aims to improve the battery life and privacy of wearable devices, enabling long-term monitoring for both personal health and medical conditions. The system applies to a variety of bio-parametric signals which could be used for general consumer health monitoring that informs users of their own health, motivating lifestyle changes, and detecting emergency conditions. The system also has applications in intensive care to improve the comfort of patients that are being monitored in the long-term by removing wires while maintaining a long lifetime. Monitoring of specific signals such as stress, emotion, sound, and motion can provide utility for military applications including soldier monitoring in the battlefield, soldier training, and post-traumatic diseases. The system could also be customized for specialized athletic sport monitoring or even for specialized medical conditions. For example, patients with chronic conditions like stroke recurrence could benefit from heart monitoring, temperature monitoring can predict immune response to viruses so patients can get treatment, and respiration tracking can predict the risk of an asthma attack. Overall, the system has the potential to improve health monitoring for the general public, and the ability for patients with chronic conditions to get treatment in advance of critical events.This I-Corps project is based on the development of an intelligent wireless sensor network platform for extended human health monitoring through in-sensor machine learning. The technology uses emerging brain-inspired Hyperdimensional Computing, which is characterized by very low computational complexity, to minimize net power consumption improving network lifetime, security, and privacy. The technology enables integration of a large number of low-power distributed sensors that wirelessly communicate intelligently detected events/classes. Current systems either transmit data to another device for processing, which is very costly in terms of power, or attempt to process locally with algorithms that are more expensive than the amount saved by reducing transmission. To solve these problems, this project utilizes the emerging brain-inspired Hyperdimensional Computing paradigm to minimize net power consumption of in-sensor computation. This paradigm represents information with fully binary vectors and thus the encoding of data patterns involves only simple binary operators such as right shifts, making it extremely computationally simple. The representation can be used for classification tasks through encoding training data into a prototype vector per class and then, during inference, comparing similarly encoded input data against the trained prototypes to find the closest class. Using this technology, this project can significantly improve sensor lifetime and also provide security and privacy due to the local computation. This project aims to develop a platform that takes advantage of these various elements for long-term monitoring.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.