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

这个小型企业创新研究(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的不受控制的破坏性发作，即所谓的崩溃。熔毁对这些儿童及其家人来说是非常令人痛苦的事件，可能需要代表应急人员和医疗保健提供者进行干预。通过主动检测情绪崩溃来治疗自闭症患者，将使照顾者有时间进行干预，缓解和防止破坏性行为事件的发生。预测危机，然后实施战略干预以防止危机的能力，可能会通过减少社会耻辱，使学校和家庭活动更多地纳入主流，并减少重大的经济医疗负担，对儿童、他们的家庭和他们的照顾产生积极的改变生活的影响。这项技术还可以用来缓解创伤后应激障碍患者的恐慌症发作。这个小企业创新研究(SBIR)第一阶段项目旨在开发一种可检测、预测和帮助防止自闭症儿童崩溃的可穿戴设备。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，也没有一种主动预测行为发作。这款可穿戴设备的一个独特功能是，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，因此它可以高精度地预测熔毁。当孩子处于崩溃的高风险时，该设备会检测到相关的生理状况，并及时提醒照顾者和治疗师进行干预。该项目的目标是创建一个原型，其中包括可穿戴产品和机器学习实现的个性化，以将儿童的生物测量指标与行为状态相关联。其目标是降低熔毁的频率和/或严重程度。通过使自闭症儿童的照顾者能够控制和防止自闭症儿童的崩溃，该项目有可能消除这些儿童在公共场合崩溃时面临的耻辱，并赋予儿童随着年龄的增长变得更加独立的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优点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.