EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Privacy Enhancing Framework to Advance Behavior Models

这个项目旨在推进对问题饮食行为的研究。该项目研究了可穿戴传感器来测量饮食行为，并开发了包含多种可观察行为的行为模型，比如单独或与朋友一起吃饭，或者咀嚼速度。这些数据可以帮助科学家改进目前的传统方法，比如自我报告的饮食日记，这种方法往往不一致、稀疏，而且很少及时。我们使用定制的可穿戴增强摄像头捕捉人类行为。可穿戴摄像头提供了丰富的数据，但也引发了隐私问题。该项目将通过构建一个使用机器学习和信息理论的框架来解决这些问题，同时包括人类报告的隐私问题。该框架将解决佩戴者可能限制在现实环境中记录真实行为的担忧，并将优化算法，以增强对人类行为的检测和分类。该项目探讨了各种活动及其必要任务的混淆技术的可接受性。拟议的研究将设计一套计算效率高的任务特定算法，该算法使用计算受限（原位）的原始图像和非受限环境（离线）的模糊图像，为个性化地面真相可穿戴相机的可扩展开发构建信息性能曲线。该项目还将开发一种模块化、即插即用、低复杂性和高效的混淆计算硬件设备，以促进和加速所提出方法和算法的使用。这项工作将使用设计框架和设备在现实环境中验证暴饮暴食行为模型，提供饮食行为的视觉确认，并展示如何使用它来测试现有模型。这个项目可能对社会科学的其他领域有用，从根本上改变研究人员在现实世界中建立和验证行为模型的方式。在健康（特别是预防医学）、社会和经济科学方面有潜在的应用：能量平衡、婴儿发育、药物依从性、消费者行为和人与环境的相互作用。该奖项反映了美国国家科学基金会的法定使命，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。

This project is designed to advance research on problematic eating behavior. The project investigates wearable sensors to measure eating behavior and developing models of behavior that comprise multiple observable behaviors such as eating alone or with friends, or chewing speed. These data can help scientists improve upon current traditional methods such as self-reported eating diaries, which tend to be inconsistent, sparse, and rarely timely. We capture human behavior using a custom wearable augmented camera. Wearable cameras provide rich data, but raise privacy concerns. The project will address these concerns by building a framework using machine learning and information theory while including human-reported privacy concerns. The framework will address wearers' concerns that may limit recording authentic behavior in real-world settings and will optimize algorithms to enhance the detection and classification of human behavior. The project explores the acceptability of obfuscation techniques on varied activities and their requisite tasks. The proposed research will design a suite of computationally efficient task-specific algorithms that use raw images in computationally restrictive (in situ) and obfuscated images in unrestrictive environments (offline) to build information-performance curves for the scalable development of personalized ground truth wearable cameras. The project also will develop a modular, plug-and-play, low-complexity and efficient obfuscation computing hardware device to facilitate and accelerate the use of the proposed methods and algorithms. This work will validate an overeating behavior model in a real-world setting using the design framework and device, providing visual confirmation of eating behaviors, showing how it can be used to test existing models. This project is likely to be useful to other domains in the social sciences, fundamentally changing the way researchers build and validate behavioral models in real-world settings. There are potential applications in health (especially preventive medicine), social, and economic sciences: energy balance, infant development, medication adherence, consumer behavior, and human-environment interaction.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.