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SCH:INT: Collaborative Research: Semi-Automated Rehabilitation in the Home

SCH:INT：合作研究：家庭半自动康复

基本信息

批准号：
2230762
负责人：
Thanassis Rikakis
金额：
$ 110万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230762&HistoricalAwards=false
关键词：
SCH INT Collaborative Research Semi

项目摘要

With the aging of the US population, there is an increasing need for effective and accessible rehabilitation services for debilitating illnesses and injuries such as stroke and arthritis. Intensive long-term rehabilitation is challenging to administer in an accessible and affordable way as it requires frequent trips to the clinic (usually supported by a caregiver), and significant one-on-one time with rehabilitation experts. Telemedicine and telehealth are gaining prominence as cost effective ways to deliver home-based health and wellness to wider populations. However, automated tele-rehabilitation is not currently feasible as the expert functions of the therapist cannot yet be fully automated and replicated in the home. In addition, there are significant technical, behavioral, and clinical challenges to scaling technology assisted home-based rehabilitation. This project aims to address these challenges through the development of a system for Semi-Automated Rehabilitation At Home (SARAH). The system is defined as semi-automated because it relies on the remote participation of the therapist for developing and adapting the therapy program. The SARAH system uses the remote therapists’ instructions to guide the patient through daily intensive therapy sessions at the home. Using inexpensive sensing technologies that are non-intrusive and mindful of the patient’s privacy, the system records and analyzes the daily therapy sessions as well as the general activities of the patient in the home. The SARAH system then provides feedback to the patient based on their therapy activities and general movements around the home. The system also provides summaries of patient progress to the remote therapist so that they can adapt the program for subsequent therapy sessions. The first version of the SARAH system focuses on upper extremity stroke rehabilitation at the home as the team of researchers has significant experience in this space. Additional outputs from this project, including the development of a generalized system and relevant methodology, are designed to support a wide variety of home-based rehabilitation contexts. The technical goals of the project are the development of movement assessment algorithms fusing knowledge based and data driven approaches. This fused approach produces automated patient assessment feedback during home-based therapy, and summaries of patient therapy and daily activities to assist the therapist with remote decision making. The project utilizes a Hierarchical Bayesian Model (HBM) approximating the therapist decision process as a common framework for the development of integrative cyber-human movement assessment algorithms. Therapy sessions are captured using two video cameras and four wearable Inertial Measurement Units (IMUs), while daily activity is only be tracked through the IMUs to estimate the wearer's 3D kinematics. The project fuses clinician’s expert knowledge of therapy tasks and segments with video and IMU data to implement automated segmentation and rating of therapy at the home. The fused cyber-human assessment of therapy data is used to inform the translation of low-level IMU feature tracking during daily life activities into daily movement summaries assisting remote therapy assessment and customization. The automated summaries include: therapy adherence, quality of therapy performance, quantity of patient daily activity and movement in the house, use of impaired limb, tasks detected during daily activity, and confidence of identification. The fusion of knowledge based and data driven approaches for computational movement analysis, as well as the cyber-human design process itself, will yield higher-level generalizable insights extending to many more applications of machine learning and deep learning in data-constrained scenarios. The low-cost sensor networks and wearable sensor solutions produced by the project will provide practical ways to monitor kinematics in real-world environments such as improved control systems for prosthetics and exoskeletons, prevention of workplace injuries through biofeedback, and enhancements in human-robot collaboration.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.

随着美国人口的老龄化，对中风和关节炎等衰弱性疾病和损伤的有效和可获得的康复服务的需求日益增加。以可获得和负担得起的方式进行密集的长期康复具有挑战性，因为它需要频繁前往诊所（通常由护理人员提供支持），并与康复专家进行大量的一对一时间。远程医疗和远程保健作为向更广泛的人群提供家庭保健和健康的具有成本效益的方式，正日益受到重视。然而，自动远程康复目前还不可行，因为治疗师的专家功能还不能完全自动化并在家中复制。此外，有重大的技术，行为和临床挑战，以扩大技术辅助家庭为基础的康复。该项目旨在通过开发家庭半自动康复系统（SARAH）来应对这些挑战。该系统被定义为半自动化，因为它依赖于治疗师的远程参与来开发和调整治疗程序。SARAH系统使用远程治疗师的指示来指导患者在家中进行日常强化治疗。该系统使用非侵入性和关注患者隐私的廉价传感技术，记录和分析日常治疗过程以及患者在家中的一般活动。然后，SARAH系统根据患者的治疗活动和在家周围的一般运动向患者提供反馈。该系统还向远程治疗师提供患者进展的摘要，使得他们可以针对后续治疗会话调整程序。SARAH系统的第一个版本专注于上肢中风康复，因为研究人员团队在这个领域拥有丰富的经验。该项目的其他产出，包括开发一个通用系统和相关方法，旨在支持各种各样的家庭康复环境。该项目的技术目标是融合基于知识和数据驱动方法的运动评估算法的开发。这种融合的方法在基于家庭的治疗期间产生自动化的患者评估反馈，以及患者治疗和日常活动的摘要，以帮助治疗师进行远程决策。该项目利用了一个层次贝叶斯模型（HBM）近似治疗师的决策过程作为一个共同的框架，综合网络人类运动评估算法的发展。使用两个摄像机和四个可穿戴惯性测量单元（伊穆斯）捕获治疗会话，而日常活动仅通过伊穆斯进行跟踪，以估计佩戴者的3D运动学。该项目融合了临床医生的治疗任务和视频和IMU数据段的专业知识，以实现自动分割和评级的治疗在家里。治疗数据的融合网络-人类评估用于将日常生活活动期间的低级IMU特征跟踪转化为日常运动摘要，以辅助远程治疗评估和定制。自动摘要包括：治疗依从性、治疗性能的质量、患者日常活动的数量和在家中的移动、受损肢体的使用、日常活动期间检测到的任务以及识别的置信度。基于知识和数据驱动的计算运动分析方法的融合，以及网络人类设计过程本身，将产生更高层次的可概括的见解，扩展到数据受限场景中机器学习和深度学习的更多应用。该项目生产的低成本传感器网络和可穿戴传感器解决方案将提供实用的方法来监测现实环境中的运动学，例如改进假肢和外骨骼的控制系统，通过生物反馈预防工伤，和人类的增强-该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。