CAREER: Enhancing ambient capacitive sensing through improved resolution and multi-modal sensor fusion

从中风中恢复和管理其他神经系统疾病的一个重要部分是神经康复，帮助人们从身体损伤中恢复到他们的姿势和活动能力。在康复治疗期间，治疗师仔细监测运动并提供反馈，以改善运动功能和控制。能够在康复会议之外继续这种监测和反馈可能是治疗的宝贵补充；然而，现有的持续身体姿势和运动估计技术有许多局限性。这项提案的目标是开发基于电容传感器阵列(CSA)的姿势和运动估计新技术，电容传感器阵列是智能手机等设备中使用的一种常见技术。这些新技术将研究如何使用环境中的传感器和灵活的穿戴在身体上的传感器来更好地估计姿势和运动。这项工作将包括开发新的方法来配置和部署CSA，处理它们发回的信号，并结合多种类型的传感器。该项目将专注于上半身姿势和运动检测，特别是人的手臂，但这些见解和方法可能适用于广泛的医疗应用，并更普遍地提供与计算机交互的新方法。特别是，这项工作的成果可能会帮助治疗师开发新的培训程序，并支持远程物理治疗，使居住在非城市地区的人更容易获得治疗。该项目旨在证明嵌入式可穿戴CSA和电子场传感器的可行性，以提供超出技术水平的准确、连续的姿势估计。为此，该团队将解决非接触式可穿戴CSA的现有开放挑战；即1)提高传感器分辨率，2)减少可变位置造成的误差，以及3)补偿电极移位。具体地说，可穿戴系统中的传感器分辨率将通过创建用于姿势估计的定制电容阵列来提高，通过深度迁移学习来增强传感器的分辨率和改善噪声过滤，并通过使用额外的传感机制来增强数据来补偿误差。这些贡献将通过为患有上肢障碍的人进行基于任务的远程神经康复训练来评估，上肢障碍目前缺乏对长期和野外运动评估的支持。如果成功，这项工作有望增加美国每年近80万中风患者的神经康复持续时间、质量和可获得性。该项目由以人为中心的计算(HCC)和既定的刺激竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

One important part of recovering from strokes and managing other nervous system conditions is neurorehabilitation to help people recover from physical impairments to their posture and mobility. During rehabilitation sessions, therapists carefully monitor motions and give feedback to improve motor function and control. Being able to continue this monitoring and feedback outside of rehabilitation sessions could be a valuable addition to therapy; however, existing technologies for continuous body pose and motion estimation have many limitations. This proposal’s goal is to develop new techniques for pose and motion estimation based on capacitive sensor arrays (CSAs), a common technology used in devices such as smartphones. These new techniques will examine how both sensors in the environment and flexible, body-worn sensors can be used to better estimate pose and motion. The work will include developing new ways to configure and deploy CSAs, process the signals they send back, and combine multiple types of sensors. The project will focus on upper body pose and motion detection, particularly people’s arms, but the insights and methods are likely to apply to a wide range of medical applications and more generally provide new ways to interact with computers. In particular, the outcomes of the work may both help therapists develop new training procedures and support remote physical therapy that would make it more accessible to people who live in non-urban areas.This project seeks to demonstrate the feasibility of embedded and wearable CSAs and e-field sensors to provide accurate, continuous pose estimation beyond the state of the art. To do this, the team will address existing open challenges of non-touch wearable CSAs; namely 1) improving sensor resolution, 2) reducing error due to variable positioning, and 3) compensating for electrode shift. Specifically, sensor resolution in wearable systems will be improved by creating tailored capacitive arrays for pose estimation, augmenting the resolution of the sensors and improving noise filtering through deep transfer learning, and compensating for errors by augmenting the data with additional sensing mechanisms. These contributions will be evaluated through task-based remote neurorehabilitation training for people with upper limb impairments, which is currently lacking support for long-term and in-the-wild motor assessment. If successful, the work promises to increase the duration, quality, and accessibility of neurorehabilitation for the nearly 800,000 individuals experiencing strokes each year in the U.S. alone.This project is jointly funded by Human Centered Computing (HCC) and the Established Program to Stimulate Competitive Research (EPSCoR).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.