Adaptive & Individualized AAC

超过1.3%患有严重身体残疾(SPI)的美国人的异质性 排除使用常见的附加或替代通信(AAC)解决方案，例如手动标志， 手势或与触摸屏的巧妙交互以进行交流。在努力开发替代方案的同时 通过眼睛或头部跟踪的访问方法为这些人提供了一些通信方面的改进 对于个人来说，所有当前的技术都受到相同的基本限制：现有的AAC设备需要 患者必须遵守通用的通信接入方法和接口，而不是符合设备 给用户。因此，AAC用户被迫接受需要过多培训和 仅提供极低的信息传输率(ITR)和经常性的认知工作负载 通信错误，最终剥夺了他们基本的通信权利。相遇 为了满足这种健康需求，我们提出了第一个使用个性化自适应接入方法设计的SMART-AAC系统 和AAC接口，以适应每个用户特定的运动障碍的独特表现。 我们麦当娜康复医院言语研究团队的初步研究(传播 Center Lab)和波士顿大学(Stepp Lab)，利用我们团队(Altec，Inc.)开发的可穿戴传感器 已经证明了基于表面肌电(SEMG)和加速度计的指标 用于头部调节控制的肌肉活动和运动的测量可以与优化的AAC相结合 接口，与传统的未经优化的AAC设备相比，提高了ITR。利用这一试点 工作中，我们的团队现在提出了一个第一阶段的项目，以证明单个sEMG/IMU的概念验证 额头上佩戴的混合传感器可以在以下情况下提高ITR和通信精度 与通过机器学习算法优化的AAC接口集成。原型系统 将在协作的SPI受试者中进行测试并与传统(非适应性)界面进行比较 临床站点。我们的演讲和专家-AAC合作者的帮助将确保技术的所有阶段 开发是以患者为中心的，并在临床护理的背景下可用。在第二阶段，我们将以此为基础 使用自动优化软件设计智能AAC系统的概念验证，以实现动态 通过疾病进展或训练适应个体内部机能变化的学习 不同类型脊髓损伤、外伤性脊髓损伤患者运动障碍的个体差异 脑损伤、脑瘫、肌萎缩侧索硬化症等。这项创新是第一项也是唯一一项AAC技术 将可穿戴式传感器访问方面的改进与为用户自主优化的界面相结合， 从而减少在SPI中实现有效的以人为中心的通信所需的资源和培训， 通过提高HMI性能和减少工作负载。

The heterogeneity of the more than 1.3% of Americans who suffer from severe physical impairments (SPIs) preclude the use of common augmentative or alternative communication (AAC) solutions such as manual signs, gestures or dexterous interaction with a touchscreen for communication. While efforts to develop alternative access methods through eye or head tracking have provided some communication advancements for these individuals, all current technologies suffer from the same fundamental limitation: existing AAC devices require patients to conform to generic communication access methods and interfaces rather than the device conforming to the user. Consequently, AAC users are forced to settle for interventions that require excessive training and cognitive workload only to deliver extremely slow information transfer rates (ITRs) and recurrent communication errors that ultimately deprive them of the fundamental human right of communication. To meet this health need, we propose the first smart-AAC system designed using individually adaptive access methods and AAC interfaces to accommodate the unique manifestations of motor impairments specific to each user. Preliminary research by our team of speech researchers at Madonna Rehabilitation Hospital (Communication Center Lab) and Boston University (STEPP Lab), utilizing wearable sensors developed by our group (Altec, Inc) have already demonstrated that metrics based on surface electromyographic (sEMG) and accelerometer measures of muscle activity and movement for head-mediated control can be combined with optimizable AAC interfaces to improve ITRs when compared with traditional unoptimized AAC devices. Leveraging this pilot work, our team is now proposing a Phase I project to demonstrate the proof-of-concept that a single sEMG/IMU hybrid sensor worn on the forehead can provide improvements in ITR and communication accuracy when integrated with an AAC interface that is optimized through machine learning algorithms. The prototype system will be tested and compared to a conventional (non-adaptable) interface in subjects with SPI at a collaborative clinical site. Assistance by our speech and expert-AAC collaborators will ensure that all phases of technology development are patient-centric and usable in the context of clinical care. In Phase II we will build upon this proof-of-concept to design a smart-AAC system with automated optimization software that achieves dynamic learning which adapts to intra-individual changes in function through disease progression or training as well as inter-individual differences in motor impairments for a diverse set of users with spinal cord injury, traumatic brain injury, cerebral palsy, ALS, and other SPIs. The innovation is the first and only AAC technology that combines advancements in wearable-sensor access with interfaces that are autonomously optimized to the user, thereby reducing the resources and training needed to achieve effective person-centric communication in SPI, through improved HMI performance and reduced workload.