SCH: A Sensing Platform Monitoring Interactions with Daily Objects to Assess Real-World Motor Performance in Stroke Survivors

SCH：监测与日常物体相互作用的传感平台，以评估中风幸存者的真实运动表现

• 批准号: 10816915
• 负责人: Sunghoon Lee
• 金额: $ 29.85万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10816915
• 关键词: Accelerometer; Adult; American; Augmented Reality; Behavior; Brachial Paresis; Clinic; Clinical; Communication; Communities; Data; Elements; Engineering; Environment; Goals; Home; Human; Human body; Impairment; Informatics; Kinetics; Laboratories; Measurement; Measures; Monitor; Motor; Movement; Multiple Sclerosis; Outcome; Patient Monitoring; Patients; Pattern; Performance; Process; Reader; Recovery; Rehabilitation therapy; Research; Research Project Grants; Series; Signal Transduction; Stroke; System; Tactile; Taxonomy; Technology; Time; Translating; Traumatic Brain Injury; Upper Extremity; Upper limb movement; Work; Wrist; clinical investigation; community setting; disability; experimental study; hemiparesis; human-robot interaction; improved; instrument; kinematics; machine learning algorithm; machine learning model; multimodal data; multimodality; novel; post stroke; pressure sensor; programs; radio frequency; rehabilitation science; sensor technology; smart home; stroke rehabilitation; stroke survivor; tool; wrist worn device

Stroke is the leading cause of disability in adults worldwide. Upper-limb paresis is the most common impairment post-stroke. The ultimate goal of stroke rehabilitation is to improve patients’ motor performance in their home and community settings (i.e., what patients actually do). However, current clinical standards to monitor patients’ recovery process are limited to assessing patients’ motor capacity observed in the clinic (i.e., what patients are capable of doing). Wrist-worn accelerometers have been considered as a potential solution but criticized for providing a limited view of upper-limb performance. Therefor, the research and clinical communities have emphasized the need for a technological solution to support a more comprehensive understanding of stroke survivors’ motor performance. In this work, we propose to develop a novel multi-modal sensing platform to monitor important elements of upper-limb motor performance: the amount, type, and quality of movements. To that end, we introduce a new kind of sensing technology, namely Body Channel Identification (BCID), that can accurately and reliably track human interactions with the environment and, thus, human behaviors. In our setting, everyday objects are instrumented with small, inexpensive, batteryless BCID tags that can be powered by and communicate with wrist-worn devices (so-called readers) by exploiting the human body as the signal transfer channel during tactile interactions. The system provides multi-modal data, including the object ID, binary time-series of interaction patterns (contact vs. no contact), kinetic data from an optional pressure sensor embedded in the tag, and kinematic data from the inertial measurement unit on the wrist-worn readers. Leveraging data obtained from 50 stroke survivors and ten healthy subjects, we propose to develop a unique set of machine learning algorithms to process these data to taxonomically identify important types of upper-limb movements relevant to stroke rehabilitation, which are further processed to assess the quality and amount of movements performed. Finally, we investigate the relationship between the motor capacity observed in the clinic vs. motor performance outside the clinic, a topic that has been deemed critical in stroke rehabilitation but infeasible due to technical limitations. We believe the proposed research will lay the technological groundwork to open up new research and clinical opportunities, leading to key scientific discoveries to transform current practices of stroke rehabilitation.

中风是全世界成年人残疾的主要原因，上肢麻痹是最常见的。 中风后损伤的最终目标是改善患者的运动能力。 然而，他们在家庭和社区环境中的表现（即患者实际所做的事情）。 监测患者康复过程的临床标准仅限于评估患者的运动能力 在诊所观察到（即患者能够做什么）。 被认为是一种潜在的解决方案，但因提供上肢性能的有限视图而受到批评。 因此，研究和临床界强调需要一种技术解决方案 支持更全面地了解中风幸存者的运动表现。 在这项工作中，我们建议开发一种新颖的多模态传感平台来监测重要元素 上肢运动表现：运动的数量、类型和质量为此，我们引入了一个。 新型传感技术，即身体通道识别（BCID），可以准确、准确地 可靠地跟踪人类与环境的互动，从而跟踪人类在我们的环境中的行为。 日常物品都配备了小型、廉价、无电池的 BCID 标签，这些标签可以由 并利用人体作为信号与腕戴式设备（所谓的阅读器）进行通信 触觉交互过程中的传输通道系统提供多模态数据，包括对象ID、 交互模式的二进制时间序列（接触与非接触），来自可选压力的动力学数据 标签中嵌入的传感器以及来自腕戴式惯性测量单元的运动学数据 读者利用从 50 名中风幸存者和 10 名健康受试者获得的数据，我们建议： 开发一套独特的机器学习算法来处理这些数据以进行分类识别 与中风康复相关的重要上肢运动类型，经过进一步处理 最后，我们研究了动作的质量和数量之间的关系。 诊所内观察到的运动能力与诊所外的运动表现，这个话题一直被关注 对于中风康复至关重要，但由于技术上的限制，我们认为所提出的限制是不可行的。 研究将为开辟新的研究和临床机会奠定技术基础，引领 改变当前中风康复实践的关键科学发现。