Adaptive & Individualized AAC

超过1.3%的美国人患有严重身体损伤（SPI） 排除使用常见的辅助或替代通信（AAC）解决方案，如手动标志， 手势或与触摸屏的灵巧交互来进行通信。虽然努力发展替代能源， 通过眼睛或头部跟踪的访问方法已经为这些应用提供了一些通信进步。 然而，所有现有技术都受到相同的基本限制：现有的AAC设备需要 患者符合通用通信访问方法和接口，而不是器械符合 给用户因此，审计咨询委员会的使用者被迫接受需要过多培训的干预措施， 认知工作量只能提供极慢的信息传输速率（ITR）和经常性的 沟通错误，最终剥夺了他们的基本人权沟通。满足 这种健康的需要，我们提出了第一个智能AAC系统设计使用个别自适应访问方法 和AAC接口，以适应每个用户特有的运动障碍的独特表现。 麦当娜康复医院（Madonna Rehabilitation Hospital）言语研究团队的初步研究（Communication 中心实验室）和波士顿大学（STEPP实验室），利用我们集团（Altec，Inc）开发的可穿戴传感器 我已经证明，基于表面肌电图（sEMG）和加速度计的指标 用于头部介导控制的肌肉活动和运动的测量可以与可优化的AAC相结合 与传统的未经优化的AAC设备相比，利用这个试点 工作，我们的团队现在提出了第一阶段的项目，以证明概念证明，一个单一的sEMG/IMU 佩戴在前额上的混合传感器可以提供ITR和通信准确性的改进， 与通过机器学习算法优化的AAC接口集成。原型系统 将进行测试，并与SPI受试者的传统（非适应性）接口进行比较， 临床现场。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，可用于临床护理的背景下。在第二阶段，我们将以此为基础 概念验证设计智能AAC系统，自动优化软件，实现动态 通过疾病进展或训练适应个体内功能变化的学习，以及 不同脊髓损伤、创伤性和非创伤性使用者的运动障碍个体间差异 脑损伤、脑瘫、ALS和其他SPI。这项创新是第一个也是唯一一个 结合了可穿戴传感器访问的进步与针对用户自主优化的界面， 从而减少了在SPI中实现有效的以人为中心的通信所需的资源和培训， 通过改善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.