Automating At-Home Balance Training Using Wearable Sensors

衰老和感官障碍导致的平衡能力下降对生活质量和长期健康产生负面影响。平衡不良会增加福尔斯跌倒的风险、对跌倒的恐惧和久坐不动的生活方式，从而导致随后的发病率、死亡率和医疗费用增加。平衡训练旨在加强或恢复导致成功平衡的复杂感觉运动通路，可以改善有福尔斯跌倒风险的个体的功能;然而，由物理治疗师管理的基于诊所的会话受到患者负荷和保险约束的限制。目前的家庭训练并不像它可能的那样有效，因为患者无法准确评估他们的表现或在没有专家指导的情况下有效地自我进步他们的训练。该项目将通过开发和验证可穿戴技术和数据驱动模型来推动科学发展，促进国民健康、繁荣和福利，这些技术和模型能够：1）远程评估用户家中的平衡; 2）根据观察到的临床决策模型推荐平衡练习，这些临床决策模型根据用户不断发展的平衡能力充分、安全地挑战用户。这项研究是实现创建自动平衡训练技术以补充，补充和增加临床质量护理的长期目标的第一步和必要步骤。这项研究的结果有可能适用于各种平衡和步态障碍的美国人，包括感觉，神经和运动障碍。此外，学生还将参与多个课程，包括实践设计课程项目，并与更广泛的社区互动。这项研究迈出了开发可穿戴技术和机器智能的第一步，这将使平衡训练计划能够在没有实时物理治疗师（PT）指导的情况下进行。该项目将（1）识别物理治疗师使用的重要运动学和视觉信息，以估计潜在的平衡运动能力，并为平衡运动进展的临床决策提供信息，以及（2）评估自适应机器学习模型的能力，以模拟专家知情的平衡运动进展策略，以响应不同个人和群体不断变化的需求。为了实现这些目标，将以实时和异步视频记录格式收集眼动跟踪和患者运动学测量，以确定与物理治疗师评估成人平衡能力相关的信息收集的关键方面。此外，强化学习框架下的马尔可夫决策过程建模将捕获物理治疗师-患者共同适应有效运动进展策略的动态。待开发的模型将捕获在训练计划过程中演变的专家-患者交互的迭代、共同适应过程，其中患者由于所选训练而适应其感觉运动行为，物理治疗师根据患者的进展来适应训练。这项工作将导致模型的开发，整合异构的临床和生物力学数据，并产生新的方法来模拟专家-患者的互动，是强大的患者之间的差异和共同适应的专家和患者随着时间的推移。在康复环境中，物理治疗师与患者互动的动态过程模型的开发有望为未来的努力提供信息，为老年人和前庭功能障碍患者开发有效的，可扩展的家庭平衡训练解决方案。这样的解决方案将补充和/或补充在家中使用自适应可穿戴技术的临床环境内的平衡训练的当前提供。此外，将通过这项研究开发的框架和技术可以适用于其他基于临床的培训环境（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Reductions in balance ability caused by aging and sensory disabilities have a negative impact on quality of life and long-term health. Poor balance increases the risk of falls, fear of falling, and sedentary lifestyles, which contribute to subsequent morbidity, mortality, and increased healthcare costs. Balance training designed to strengthen or restore the complex sensorimotor pathways that lead to successful balance can improve function in individuals at risk for falls; however, clinic-based sessions administered by physical therapists are limited by patient load and insurance constraints. Current home-based training is not as effective as it could be because patients cannot accurately assess their performance or effectively self-progress their training without expert guidance. This project will advance science and promote national health, prosperity, and welfare by developing and verifying wearable technology and data-driven models capable of 1) remotely assessing balance in users’ homes; and 2) recommending balance exercises informed by models of observed clinical decision-making that adequately and safely challenge users based on their evolving balance abilities. This research is a first and necessary step in achieving the long-term goal of creating automated balance training technologies to complement, supplement, and increase access to clinic-quality care. The outcomes of this research have the potential to be adapted to a diverse population of Americans with a wide range of balance and gait impairments including sensory, neurological, and motor disorders. Additionally, students will be engaged through multiple curricular offerings including hands-on design course projects with broader community interactions.This research takes the first step toward developing wearable technology and machine intelligence that will enable balance training programs that can be performed in the absence of real-time physical therapist (PT) guidance. The project will (1) identify important kinematic and visual information used by physical therapists to estimate underlying balance exercise ability and to inform clinical-decision making regarding balance exercise progression, and (2) assess the capabilities of adaptive machine learning models to simulate expert-informed balance exercise progression strategies that are responsive to the evolving needs of different individuals and groups. To achieve these objectives, eye movement tracking and patient kinematic measures will be collected in both live and asynchronous video-recorded formats to identify key aspects of information-gathering relevant to physical therapists’ evaluations of adult balance capabilities. Additionally, Markov decision process modeling under a reinforcement learning framework will capture the dynamics of the physical therapist-patient co-adaptation of effective exercise progression policies. The models to be developed will capture the iterative, co-adaptive process of expert-patient interaction that evolves over the course of the training program, where the patient adapts their sensorimotor behavior due to the selected training and the physical therapist adapts the training based on patient progress. This work will result in the development of models that integrate heterogeneous clinical and biomechanical data and generate new approaches for modeling expert-patient interaction that are robust to patient differences and co-adaptive between the expert and patient over time. The development of models that characterize the dynamic process of physical therapist-patient interaction in a rehabilitative setting promises to inform future efforts to develop effective, scalable at-home balance training solutions for older adults and people with vestibular dysfunction. Such solutions would complement and/or supplement the current provision of balance training within clinical settings using adaptive wearable technology at home. Furthermore, the framework and technology that will be developed through this research may be adapted for use in other clinic-based training contexts (e.g., stroke recovery and post-surgical rehabilitation).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.