SBIR Phase I: Prototyping a Wearable Device that Continuously Monitors Biometrics using Machine Learning to Predict Meltdowns in Children with Autism

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力在于它能够使用机器学习和可穿戴技术来减少自闭症儿童不受控制的破坏性事件，即所谓的崩溃。崩溃对这些儿童及其家庭来说是非常痛苦的事件，可能需要代表应急人员和医疗保健提供者进行干预。通过主动发现情绪崩溃来治疗自闭症患者，将使护理人员有时间进行干预，减轻和预防破坏性行为发作的发生。预测崩溃，然后实施战略干预以防止崩溃的能力，可以通过减少社会耻辱感，使学校和家庭活动更加主流化，以及减轻重大的财务保健负担，对儿童、其家庭及其照顾者产生积极的改变生活的影响。这项技术也可用于减轻创伤后应激障碍患者的惊恐发作。这个小企业创新研究（SBIR）第一阶段项目旨在开发一种可穿戴设备，用于检测、预测并帮助预防自闭症儿童的崩溃。市场上有测量生理参数的可穿戴设备，但没有一种是针对自闭症的，也没有一种是主动预测行为发作的。这种可穿戴设备的一个独特之处在于，它使用机器学习来预测熔毁。结合机器学习，每个设备都可以学习佩戴者的独特生物特征，这样它就可以高精度地预测熔毁。当孩子处于崩溃的高风险时，该设备将检测到相关的生理机能，并及时提醒护理人员和治疗师进行干预。该项目的目标是创建一个原型，其中包括可穿戴产品和通过机器学习实现的个性化，以将儿童的生物特征测量与行为状态联系起来。目标是减少熔毁的频率和/或严重程度。通过使自闭症儿童的看护人能够控制和防止情绪崩溃，这个项目有可能消除这些孩子在公共场合情绪崩溃时所面临的耻辱，并使孩子们随着年龄的增长变得更加独立。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in its ability to use machine learning and wearable technology to reduce uncontrolled destructive episodees, known as meltdowns, in children with autism. Meltdowns are highly distressing events for these children and their families and may require intervention on behalf of emergency response personnel and healthcare providers. Treating individuals with autism by proactively detecting meltdowns will allow caregivers time to intervene, mitigate, and prevent the onset of destructive behavioral episodes. The ability to predict a meltdown, and then implement strategic intervention to prevent the meltdown, may have positive life-changing effects for the children, their families, and their caretakes by reducing social stigma, enabling more mainstreaming of school and family activities, and reducing significant financial healthcare burdens. This technology may also be used to mitigate panic attacks in individuals with post-traumatic stress disorders.This Small Business Innovation Research (SBIR) Phase I project seeks to develop a wearable device that detects, predicts, and helps prevent meltdowns in children with autism. Wearable devices that measure physiological parameters are available in the market, but none of them are specific to autism, and none of them proactively predict behavior episodes. A unique feature of this wearable device is that it uses machine learning to predict meltdowns. Incorporating machine learning allows each device to learn the unique biometric signature of the wearer so it can predict meltdowns with high accuracy. When a child is at high risk for a meltdown, the device will detect the relevant physiology and alert caregivers and therapists in time to intervene. The objectives of this project are to create a prototype which includes the wearable product and the individualization enabled by machine learning to correlate a child’s biometric measures with behavioral states. The goal is to achieve decreased frequency and/or severity of meltdowns. By enabling caregivers of children with autism to take control of and prevent meltdowns, this project has the potential to eliminate the stigma these children face during a meltdown in public, and empowers the children to become more independent as they age.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.