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.

随着美国人口的衰老，越来越需要有效且可及的康复服务，以使疾病和损伤（例如中风和关节炎）使人失望。挑战密集的长期康复挑战，要以可及可及的方式进行管理，因为它需要经常去诊所旅行（通常由照料者支持），并与康复专家一对一的一对一时间。远程医疗和远程医疗已成为具有成本效益的方式，可以为更广泛的人群提供家庭健康和健康。但是，由于治疗师的专家职能尚未在家庭中完全自动化和复制，因此目前自动化的电信措施是不可行的。此外，针对缩放技术辅助家庭康复的技术，行为和临床挑战都有重大的挑战。该项目旨在通过开发在家中半自动康复系统（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制定和适应治疗计划。 Sarah系统使用远程治疗师的指示来指导患者在家中每天进行强化治疗课程。系统使用廉价的感应技术，这些技术是无侵入性的，并且要注意患者的隐私，该系统记录并分析了日常治疗课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家庭周围的一般运动向患者提供反馈。该系统还提供了患者对远程治疗师的进展的摘要，以便他们可以适应后续治疗课程。 Sarah系统的第一个版本着重于家庭中的上肢中风康复，因为研究人员在这个领域拥有丰富的经验。该项目的其他输出，包括开发通用系统和相关方法，旨在支持各种基于家庭的康复环境。该项目的技术目标是发展运动评估算法的开发，从而融合了基于知识的方法和数据驱动的方法。这种融合的方法在家庭疗法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。该项目利用层次结构的贝叶斯模型（HBM），将治疗师的决策过程近似为开发综合网络人类运动评估算法的共同框架。使用两个摄像机和四个可穿戴惯性测量单元（IMU）捕获治疗课程，而每日活动仅通过IMU进行跟踪，以估算佩戴者的3D运动学。该项目将临床对治疗任务和细分市场的专家知识与视频和IMU数据融合在一起，以实施自动分割和家庭治疗的评级。融合的网络人类治疗数据评估用于告知日常运动中低级IMU特征跟踪到日常运动的翻译，以帮助远程治疗评估和自定义。自动摘要包括：治疗依从性，治疗性能质量，患者日常活动的数量和房屋运动的运动，使用受损的肢体，在日常活动中检测到的任务以及识别信心。基于知识和数据驱动的计算运动分析的方法以及网络人类设计过程本身的融合将产生更高级别的可概括见解，这些见解扩展到了在数据约束的情况下进行更多的机器学习和深入学习的应用。 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 injury through biofeedback, and enhancements in human-robot collaboration.This award reflects NSF's statutory mission and has been deemed precious of support through evaluation using the Foundation's intellectual merit and broader影响审查标准。