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，对于中风和关节炎等使人衰弱的疾病和损伤，对有效且方便的康复服务的需求日益增加，因为需要频繁的旅行，所以以方便且负担得起的方式进行强化长期康复是一项挑战。到诊所（通常由护理人员支持），以及与康复专家的重要一对一时间作为向更广泛人群提供家庭健康和保健的成本有效的方式越来越受到重视。目前还没有康复由于治疗师的专家功能尚无法在家庭中完全自动化和复制，因此扩展技术辅助的家庭康复还存在重大的技术、行为和临床挑战。在家半自动康复 (SARAH) 系统的开发该系统被定义为半自动，因为它依赖于治疗师的远程参与来开发和调整治疗程序。来指导该系统使用非侵入性且注重患者隐私的廉价传感技术，记录和嵌套患者的日常治疗课程以及患者在家中的一般活动。然后，系统根据患者的治疗活动和在家中的一般活动向患者提供反馈。该系统还向远程治疗师提供患者进展的摘要，以便他们可以调整程序以进行后续的治疗。专注于上肢中风康复由于研究人员团队在这一领域拥有丰富的经验，因此该项目的其他成果（包括通用系统和相关方法的开发）旨在支持各种家庭康复环境的技术目标。该项目是开发融合基于知识和数据驱动方法的运动评估算法，这种融合方法在家庭治疗期间产生自动患者评估反馈，并利用患者治疗和日常活动的摘要来协助治疗师做出远程决策。分层贝叶斯模型(HBM) 将治疗师决策过程近似为开发综合网络人类运动评估算法的通用框架。使用两个摄像机和四个可穿戴惯性测量单元 (IMU) 捕获治疗过程，而只能通过以下方式跟踪日常活动。 IMU 来估计佩戴者的 3D 运动学该项目将临床医生对治疗任务和分段的专业知识与视频和 IMU 数据相融合，以实现治疗的自动分段和评级。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 特征跟踪转换为日常运动摘要，以协助远程治疗评估和定制。自动摘要包括：治疗依从性、治疗质量。性能、患者日常活动和在家中运动的数量、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以及网络人类的融合。设计过程本身，将产生更高水平的该项目产生的低成本传感器网络和可穿戴传感器解决方案将可推广到机器学习和深度学习在数据受限场景中的更多应用，将为监控现实环境中的运动学提供实用方法，例如改进的控制系统。假肢和外骨骼、通过生物反馈预防工作场所伤害以及增强人机协作。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进行评估，被认为值得支持标准。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications.

使用用于康复应用的低成本传感器捕获上半身运动学和定位。

DOI：
10.3390/s22062300
发表时间：
2022-03
期刊：
Sensors
影响因子：
3.9
作者：
Sarker, A.;Emenonye, D.R.;Kelliher, A.;Rikakis, T.;Buehrer, R.M.;Asbeck, A.T.
通讯作者：
Asbeck, A.T.

Automated Movement Assessment in Stroke Rehabilitation