SCH: Enhanced detection of impending problem behavior in people with intellectual and developmental disabilities through multimodal sensing and machine learning

SCH：通过多模态传感和机器学习增强对智力和发育障碍人士即将出现的问题行为的检测

• 批准号: 2124002
• 负责人: Nilanjan Sarkar
• 金额: $ 110.4万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124002&HistoricalAwards=false
• 关键词: SCH; Enhanced; detection; impending; problem

Children with Intellectual and Developmental Disabilities (IDD) are at increased risk of showing “problem behavior” that place them at risk of getting hurt, removed from the classroom, or hospitalized. Approximately 1 in 6 children and adolescents in the United States are diagnosed with IDD and half of them experience some form of problem behavior. Therapists trained in Applied Behavior Analysis, or ABA, can help determine why problem behavior happens and how to prevent it. These therapists watch children, try to evoke problem behaviors by changing a child’s environment, then try things that might change behavior, and see if the behavioral data changes. Because problem behavior can be triggered during this process, this strategy sometimes put them or their patient at risk. It also takes a lot of time. Wearable technology and advanced computational strategies could help increase the safety and helpfulness of strategies to prevent problem behavior. Specifically, small sensors worn in clothing or on the wrist could provide data about a child’s body, or “physiological responses,” like heart rate or sweat. Machine learning can then be used to determine what combination of body signals imply a problem behavior is about to happen. This project has two stages. In the first stage, the team will design new sensors that detect biological signals such as sweating, motion, and heart rate. The team will then measure how well these sensors work. This includes asking people with IDD what they think about the sensors. Based on that input, the team will change the sensors and then use them in a larger study. The goal is to test whether the system can predict problem behavior, how well it works when used in the real-world with real therapists, and what users think about the system. Results of this study will help researchers and practitioners understand if this kind of wearable technology is helpful and acceptable as part of supporting people with problem behavior and IDD. This project proposes to integrate transdisciplinary expertise in cutting-edge wearable sensing, affective computing, machine learning, and behavioral and clinical science to enhance and transform existing models of behavioral intervention for problem behaviors in children and adolescents with IDD. Problem behaviors, including self-injury, aggression, property destruction, and wandering not only can cause serious injury or death, but also interfere with the ability to participate in school, home, and other community settings. In the context of problem behavior and IDD, this project will fundamentally advance the scientific and the technological methodologies of multimodal wearable sensing-based design of predictive machine learning models. The two research thrusts are: (1) Design of multimodal sensor framework; and 2) Real-time precursor prediction. Across these thrusts, the project will make fundamental scientific and technological advancements in: (i) A low-power, open-access, user-centric wearable sensor framework that can sense physiological responses and gestures to be used for affective computing; and (ii) A set of novel, clinically grounded, semi-supervised machine learning models to predict problem behavior that can be used by behavioral interventionists in real-time. An important novelty of this research that separates it from existing work in the field is that the team proposes to address the detection of problem behavior through its precursors, rather than the behaviors themselves, with the goal of increasing the safety and efficiency of sessions. These scientific and technological advancements will be created within a state-of-the-art clinical and behavioral science framework. The proposed work will foster interdisciplinary research in engineering and health sciences. The team proposes a number of outreach and educational activities that will have broader impact in STEM education: i) involve individuals with ASD directly in the research through the Frist Center for Autism & Innovation’s Neurodiversity Corps; ii) provide interdisciplinary training opportunities for early stage clinical scientists; iii) provide research opportunity to high school, undergraduate, and graduate students; iv) provide research opportunity to high school teachers; v) bring research into the classroom; and vi) disseminate the research through seminars, presentation, and publication. Emphasis will be placed on recruiting candidates from minority and underrepresented groups to improve diversity in STEM.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.

患有智力和发育障碍（IDD）的儿童表现出“问题行为”的风险增加，这使他们面临受伤、被赶出教室或住院的风险。在美国，大约六分之一的儿童和青少年被诊断患有IDD，其中一半的人经历了某种形式的问题行为。接受过应用行为分析（ABA）培训的治疗师可以帮助确定问题行为发生的原因以及如何预防。这些治疗师观察孩子，试图通过改变孩子的环境来唤起问题行为，然后尝试一些可能改变行为的事情，看看行为数据是否发生了变化。因为在这个过程中可能会引发问题行为，这种策略有时会使他们或他们的病人处于危险之中。这也需要很多时间。可穿戴技术和先进的计算策略可以帮助提高策略的安全性和有用性，以防止问题行为。具体来说，戴在衣服上或手腕上的小传感器可以提供孩子身体的数据，或者心率或出汗等“生理反应”的数据。然后，机器学习可以用来确定身体信号的哪种组合意味着问题行为即将发生。这个项目分为两个阶段。在第一阶段，研究小组将设计新的传感器，检测出汗、运动和心率等生物信号。然后，研究小组将测量这些传感器的工作效果。这包括询问患有IDD的人对传感器的看法。基于这些输入，研究小组将改变传感器，然后在更大的研究中使用它们。目标是测试系统是否可以预测问题行为，在现实世界中与真正的治疗师一起使用时效果如何，以及用户对系统的看法。这项研究的结果将帮助研究人员和实践者了解这种可穿戴技术作为支持有问题行为和缺乏症的人的一部分是否有用和可接受。本项目拟整合前沿可穿戴传感、情感计算、机器学习、行为与临床科学等跨学科专业知识，增强和改造现有的儿童和青少年IDD问题行为干预模式。问题行为，包括自残、攻击、破坏财产和流浪，不仅会导致严重的伤害或死亡，而且还会干扰参与学校、家庭和其他社区环境的能力。在问题行为和IDD的背景下，本项目将从根本上推进基于多模态可穿戴传感的预测机器学习模型设计的科学和技术方法。主要研究方向为：(1)多模态传感器框架设计；2)实时前兆预测。通过这些重点，该项目将在以下方面取得根本性的科学和技术进步：(i)低功耗、开放访问、以用户为中心的可穿戴传感器框架，可以感知用于情感计算的生理反应和手势；（ii）一套新颖的、基于临床的、半监督的机器学习模型，用于预测行为干预学家可以实时使用的问题行为。这项研究与该领域现有工作的一个重要的新颖之处是，该团队建议通过其前体而不是行为本身来解决问题行为的检测，其目标是提高会话的安全性和效率。这些科学和技术进步将在最先进的临床和行为科学框架内创造。拟议的工作将促进工程和健康科学的跨学科研究。该团队提出了一系列将对STEM教育产生更广泛影响的推广和教育活动：i)通过自闭症和创新第一中心的神经多样性小组，让自闭症患者直接参与研究；Ii)为早期临床科学家提供跨学科的培训机会；Iii)为高中生、本科生和研究生提供研究机会；4)为高中教师提供研究机会；V)将研究带入课堂；通过研讨会、演讲和出版物传播研究成果。重点将放在从少数族裔和代表性不足的群体中招募候选人，以提高STEM的多样性。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。