权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CPS: Small: Human-in-the-Loop Learning of Complex Events in Uncontrolled Environments

CPS：小型：不受控环境中复杂事件的人机循环学习

基本信息

批准号：
1932346
负责人：
Hassan Zadeh
金额：
$ 49.37万
依托单位：
Washington 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：
2020-01-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932346&HistoricalAwards=false
关键词：
CPS Small Human Loop Learning

项目摘要

This project aims to advance knowledge of machine learning for human-in-the-loop cyber-physical systems. Mobile and wearable devices have emerged as a promising technology for health monitoring and behavioral interventions. Designing such systems involves collecting and labeling sensor data in free-living environments through an active learning process. In active learning, the system iteratively queries a human expert (e.g., patient, clinician) for correct labels. Designing active learning strategies in uncontrolled settings is challenging because (1) active learning places a significant burden on the user and compromises adoption of the technology; and (2) labels expressed by humans carry significant amounts of temporal and spatial disparities that lead to poor performance of the system. The research will address technical challenges in designing high performance systems, and enable accurate monitoring and interventions in many applications beyond behavioral medicine.This project develops mixed-initiative solutions that will enable learning of human behaviors in uncontrolled environments through the following research objectives: (1) investigating combinatorial approaches to maximize the active learning performance taking into account informativeness of sensor data, burden of data labeling, and reliability of prospective labels; (2) constructing a rich vocabulary of complex behaviors based on knowledge graph embedding and semi-supervised learning techniques; (3) developing network-graph-based learning algorithms that infer complex human behaviors; and (4) validating algorithms for off-line active learning, real-time active learning, behavior vocabulary construction, and behavior inference through both in-lab experiments and user studies.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.

该项目旨在推进人类在环网络物理系统的机器学习知识。移动的和可穿戴设备已经成为健康监测和行为干预的有前途的技术。设计这样的系统涉及通过主动学习过程在自由生活环境中收集和标记传感器数据。在主动学习中，系统迭代地询问人类专家（例如，患者、临床医生）以获得正确的标签。在不受控制的环境中设计主动学习策略是具有挑战性的，因为（1）主动学习给用户带来了很大的负担，并影响了技术的采用;（2）人类表达的标签带有大量的时间和空间差异，导致系统性能低下。该研究将解决设计高性能系统的技术挑战，并在行为医学之外的许多应用中实现准确的监测和干预。该项目开发混合倡议解决方案，通过以下研究目标，使人们能够在不受控制的环境中学习人类行为：（1）研究组合方法以最大化主动学习性能，同时考虑传感器数据的信息量，数据标记的负担，和预期标签的可靠性;（2）基于知识图嵌入和半监督学习技术构建复杂行为的丰富词汇表;（3）开发基于网络图的学习算法，以推断复杂的人类行为;（4）离线主动学习、实时主动学习、行为词汇构建该奖项反映了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标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Memory-Aware Active Learning in Mobile Sensing Systems.

DOI：
10.1109/tmc.2020.3003936
发表时间：
2022-01-01
期刊：
IEEE transactions on mobile computing
影响因子：
7.9
作者：
Ashari ZE;Chaytor NS;Cook DJ;Ghasemzadeh H
通讯作者：
Ghasemzadeh H

Proximity-based active learning for eating moment recognition in wearable systems

基于接近度的主动学习，用于可穿戴系统中的进食时刻识别

DOI：
10.1145/3396870.3400011
发表时间：
2020
期刊：
WearSys '20: Proceedings of the 6th ACM Workshop on Wearable Systems and Applications
影响因子：
0
作者：
Nourollahi, Marjan;Rokni, Seyed Ali;Alinia, Parastoo;Ghasemzadeh, Hassan
通讯作者：
Ghasemzadeh, Hassan

TransNet: Minimally Supervised Deep Transfer Learning for Dynamic Adaptation of Wearable Systems

DOI：
10.1145/3414062
发表时间：
2020-09
期刊：
ACM Trans. Design Autom. Electr. Syst.
影响因子：
0
作者：
Seyed Ali Rokni;Marjan Nourollahi;Parastoo Alinia;Seyed Iman Mirzadeh;Mahdi Pedram;H. Ghasemzadeh
通讯作者：
Seyed Ali Rokni;Marjan Nourollahi;Parastoo Alinia;Seyed Iman Mirzadeh;Mahdi Pedram;H. Ghasemzadeh

Mindful Active Learning

正念主动学习

DOI：
10.24963/ijcai.2019/314
发表时间：
2019
期刊：
In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
影响因子：
0
作者：
Esna Ashari, Zhila;Ghasemzadeh, Hassan
通讯作者：
Ghasemzadeh, Hassan

Personalized Activity Recognition Using Partially Available Target Data