HCC: Elder-Centered Recognition Technology for the Assessment of Physical Function

美国人的寿命更长，生活更充实。他们希望尽可能独立地生活。但独立的生活方式也伴随着风险，例如由于护理不足而导致的虚弱跌倒和健康恶化。为了解决这些问题，研究人员正在开发“智能家居”技术，以增强居民的安全，并使用传感器和其他设备监测他们的健康状况。特别是，对身体功能的持续评估是健康和功能能力最初下降的关键指标。在问题还很小的时候识别和评估问题，可以为干预措施提供机会之窗，以便在问题领域变得灾难性之前缓解它们。在这个项目中，PI将领导一个由计算机科学和工程、护理和医学信息学研究人员组成的多学科团队，致力于开发和评估技术，以保持老年人在更高水平上的功能和独立生活。他们将利用当地一家独特的老年人护理设施(TigerPlace)正在进行的研究，研究用于多人环境的基于视觉的识别方法，旨在捕获对老年人身体功能的连续和自动评估。项目目标包括：利用老年人参与者在现实的多人环境中收集分阶段场景的视频数据，从而产生大量标签数据；利用收集的标签数据，开发和评估以保护隐私的方式分析视频的算法，提取多个人的姿势序列，跟踪无生命物体的运动，并对观察到的活动和身体功能生成评估和摘要；通过向老年学专家展示视频和提取的信息并获得反馈，评估总结和评估的有效性；以及通过向老年人展示经过处理的(“匿名”)视频和提取的信息，评估他们对视频监控的感知和态度。为了实现这些目标，PI和她的团队将在人体姿势和步态的无标记运动捕获、基于颜色视觉的对象识别、家庭环境中的活动识别、通过被动传感自动评估身体功能以及了解老年人的基本隐私问题方面取得进展。广泛的影响：这项研究将影响老年人的技术、医疗保健、政策、生活质量，以及他们家庭的安心。技术的进步对其他领域也有影响，包括健身和身体康复。这些进步将帮助医疗保健提供者识别潜在的健康问题，并让老年人保持更长时间的独立。当然，这意味着老年人及其家人的生活更幸福。提供老年护理技术的模式也可能为政策制定者提供信息，以指导有关为老年人提供服务的决策。

Americans are living longer and more fulfilling lives. They desire to live as independently as possible. But independent lifestyles come with risks, such as debilitating falls and deteriorating health resulting from inadequate care. To address these issues, researchers are developing "smart home" technologies to enhance the safety of residents and monitor their health conditions using sensors and other devices. In particular, the continuous assessment of physical function is a key indicator of initial decline in health and functional ability. Identifying and assessing problems while they are still small can provide a window of opportunity for interventions that will alleviate the problem areas before they become catastrophic. In this project, the PI will lead a multidisciplinary team comprised of researchers in computer science and engineering, nursing, and medical informatics dedicated to developing and evaluating technology to keep older adults functioning at higher levels and living independently. They will leverage ongoing research at a unique local eldercare facility (TigerPlace) to study vision-based recognition methods for multi-person environments designed to capture continuous and automated assessments of older adults' physical function. Project objectives include: to collect video data of staged scenarios in realistic multi-person settings using older adult participants, thereby producing a body of labeled data; to utilize the collected labeled data, develop and evaluate algorithms for analyzing video in a way that preserves privacy, extracts the pose sequences of multiple persons, tracks the movement of inanimate objects, and generates assessments and summarizations of the observed activities and physical function; to evaluate the effectiveness of the summarization and assessments by showing the video and extracted information to gerontology experts and obtaining feedback; and to assess the perceptions and attitudes of older adults towards video monitoring by showing them the processed ("anonymized") video and extracted information. To achieve these goals, the PI and her team will advance the state of the art in markerless motion capture of human pose and gait, color vision-based object recognition, activity recognition in a home setting, automatic assessment of physical function through passive sensing, and understanding fundamental privacy issues for older adults.Broader Impacts: This research will impact technology, health care, policy, quality of life for older adults, and peace of mind for their families. Advances in technology have implications for other areas, including fitness and physical rehabilitation. These strides will assist health care providers to identify potential health problems and keep older adults independent longer. This, of course, means happier lives for the older adults and their families. Offering a model for eldercare technology may also provide policy makers with information to guide decisions about services for older people.