SCH: MS-ADAPT: Multi-Sensor Adaptive Data Analytics for Physical Therapy

目前的项目解决了美国人腰痛（LBP）的需求。慢性复发性腰痛是一个重大的公共卫生问题，它会导致功能限制和残疾，以及个人和社会的经济负担。多达80%的人会在一生中的某个时候经历过LBP，在美国，LBP的总成本每年超过1000亿美元，其中包括因无法工作而导致的工资损失。物理治疗是有效的管理慢性腰痛和改善患者的结果。然而，患者对物理治疗师（PT）建议的依从性很低，研究表明，全天对姿势和运动的实时个性化反馈可以改善结果。然而，现有的传感器系统有很大的局限性。此外，还需要将这些传感器数据与现有的传感器技术和临床测量相结合。为了满足这一需求，该项目将设计和开发一种传感器系统，该系统支持远程监测LBP患者的姿势和运动、患者对PT建议的依从性以及依从性对结果的影响。该系统将通过开发非侵入性、低姿态的传感器来测量现实生活中腰背部的姿势和运动，并将这种新型传感器信息与现有设备和用于监测腰痛患者活动和疼痛影响的临床措施联系起来。此外，从这些新型传感器收集的新的诊断和治疗信息可用于推进治疗和改善LBP患者的预后。这项研究也可以扩展到其他严重健康状况的管理，如截肢、脊髓损伤和中风。这项工作将导致可穿戴技术、深度学习、系统集成和人机界面的创新。值得注意的是，我们将使用电阻抗断层扫描验证织物传感器的分布式运动监测，并开发算法来捕捉穿戴者正在经历的压力，以及这些压力的方向。对于预测建模，我们将使用数学和统计方法，将接收到的数据映射到正在经历的应变类别。除了推进科学和临床实践之外，该项目还将有助于培养新一代工程和健康科学交叉领域的跨学科研究人员，包括博士、硕士、本科生和健康专业学员。MS-ADAPT系统被提议作为一个人在环的网络物理系统，它集成了来自新型织物传感器的数据、来自手腕加速度计的数据和基于应用程序的患者报告结果，并使用机器学习分析来支持个性化物理治疗的预测。这项工作分为五个研究目标：1)用于腰椎功能运动评估的织物传感器；分布式传感是通过形成智能“K-Tape传感器”网络来实现的，该传感器是应变敏感的纳米复合材料，与商业运动机能学磁带集成，用于表征皮肤张力、运动和肌肉活动；2)数据集成和可视化；整合多模型数据（来自智能K-Tape传感器的皮肤阻力变化和应变图，来自Fitbit的加速度测量，以及来自应用程序的患者报告结果）和PT可视化的平台，以支持决策；3)实验室评估，解释智能K-Tape数据，以客观评估腰椎运动和肌肉活动；4)反映腰椎生物力学的新型机器学习模型：创建基于物理的模型和深度学习模型，以预测姿势和运动类型、肌肉激活、运动强度和质量；5)临床评估；评估在自由生活环境中的姿势和运动，对PT建议的依从性，以及与腰痛症状和功能改善的关系。在表征新的传感器和使用新的数据流进行精准医学洞察方面存在许多技术挑战。将创建基于物理的模型和CNN-LSTM模型，并对其进行比较，以预测全天的运动类型和质量，并评估对PT建议的依从性。该奖项反映了美国国家科学基金会的法定使命，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。

The current project addresses the needs of the US population that suffers from low back pain (LBP). Chronic recurrent LBP is a significant public health problem that results in functional limitations and disability, as well as financial burden for the individual and society. Up to 80% of people will experience LBP at some point in their lifetime, and the total costs of LBP in the U.S. exceed 100 billion dollars per year, including lost wages resulting from an inability to work. Physical therapy is effective for managing chronic LBP and improving patient outcomes. However, patient adherence to physical therapist (PT) recommendations is low, and research suggests that real-time personalized feedback on posture and movement throughout the day can improve outcomes. However, there are major limitations of existing sensor systems. Additionally, there is a need to integrate these sensor data with existing sensor technologies and clinical measures. To address this need, this project will design and develop a sensor system that supports remote monitoring of posture and movement in patients with LBP, patient adherence to PT recommendations, and the impact of adherence on outcomes. This system will both advance the science through development of non-invasive, low-profile sensors to measure low back posture and movement in a real-life setting, and connect this novel sensor information with existing devices and clinical measures that are used to monitor activity and the impact of pain in patients with LBP. Further, new diagnostic and treatment information gathered from these novel sensors could be used to advance treatments and improve outcomes for people with LBP. This research could also be extended to management of other serious health conditions, such as amputations, spinal cord injury, and stroke. This work will result in innovations in wearable technologies, deep learning, system integration, and human-computer interfaces. Notably, we will validate the fabric sensors for distributed motion monitoring using Electrical Impedance Tomography and develop algorithms to capture not how much strain the wearer is experiencing but also the direction of these strains. For predictive modeling, we will use mathematics and a statistical approach that maps the received data to the category of strain being experienced. In addition to advancing the science and clinical practice, this project will contribute to the training of a new generation of interdisciplinary researchers at the intersection of engineering and health sciences across PhD, Masters, and undergraduate students, and health professional trainees.The MS-ADAPT system is proposed as a human-in-the-loop cyber-physical system that integrates data from novel fabric sensors with data from wrist accelerometers and app-based patient-reported outcomes, and uses machine-learning analytics to enable predictions in support of personalized physical therapy. The work is structured into five research aims: 1) fabric sensors for functional movement assessment of the lumbar spine: distributed sensing is achieved by forming a network of smart “K-Tape sensors”, which are strain-sensitive nanocomposites integrated with commercial kinesiology tape for characterizing skin strains, movements, and muscle activity, 2) data integration and visualization: a platform for integrating multi-model data (changes in skin resistance and strain maps from smart K-Tape sensors; accelerometry from Fitbit; and patient-reported outcomes from apps) and PT visualizations to support decision-making; 3) laboratory assessment to interpret smart K-Tape data for objective assessment of lumbar spine movement and muscle activity, 4) novel machine-learning models that reflect lumbar spine biomechanics: create both physics-based models and deep learning models to predict posture and movement type, muscle activation, movement magnitude, and quality, and 5) clinical evaluation: assess posture and movement in a free-living environment, adherence to PT recommendations, and the association with improvement in terms of LBP symptoms and function. Many technical challenges exist with respect to characterizing new sensors and using novel data streams for precision medicine insights. Both physics-based models and CNN-LSTM models will be created and compared for predicting the movement type and quality and assessing adherence to PT recommendations throughout the day.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.