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

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，这些行为使他们处于受伤、被赶出教室或住院的风险之中。在美国，大约每6个儿童和青少年中就有1个被诊断患有IDD，其中一半人经历过某种形式的问题行为。受过应用行为分析（阿坝）训练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。这些治疗师观察儿童，试图通过改变儿童的环境来引发问题行为，然后尝试可能改变行为的事情，看看行为数据是否发生变化。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预防问题行为的策略的安全性和有用性。具体来说，戴在衣服上或手腕上的小型传感器可以提供有关儿童身体的数据，或“生理反应”，如心率或汗液。然后，机器学习可以用来确定身体信号的组合意味着问题行为即将发生。 该项目分为两个阶段。在第一阶段，该团队将设计新的传感器，检测出汗，运动和心率等生物信号。然后，该团队将测量这些传感器的工作情况。这包括询问IDD患者对传感器的看法。根据这些输入，该团队将更换传感器，然后在更大的研究中使用它们。其目标是测试该系统是否可以预测问题行为，在现实世界中与真实的治疗师一起使用时的效果如何，以及用户对该系统的看法。这项研究的结果将有助于研究人员和从业人员了解这种可穿戴技术是否有助于和可接受的支持问题行为和IDD的人的一部分。该项目旨在整合尖端可穿戴传感、情感计算、机器学习以及行为和临床科学方面的跨学科专业知识，以增强和改造现有的行为干预模式，以解决IDD儿童和青少年的问题行为。问题行为，包括自伤、攻击、破坏财产和游荡，不仅会导致严重的伤害或死亡，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，该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。两个研究重点是：（1）多模态传感器框架的设计;和2）实时前兆预测。在这些方面，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;以及（ii）一组新颖的，临床基础的半监督机器学习模型，用于预测行为干预者可以实时使用的问题行为。这项研究的一个重要新奇在于，该研究小组提出通过其前兆而不是行为本身来解决问题行为的检测问题，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建。拟议的工作将促进工程和健康科学的跨学科研究。该团队提出了一些将在STEM教育中产生更广泛影响的推广和教育活动：i）通过第一自闭症创新中心的神经多样性团队直接参与ASD患者的研究&; ii）为早期临床科学家提供跨学科培训机会; iii）为高中，本科生和研究生提供研究机会; iv）为高中教师提供研究机会; v）将研究带入课堂;以及vi）通过研讨会，演讲和出版物传播研究。重点将放在从少数民族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NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.