SCH: INT: Wearable knee Joint Health Sensing Using Acoustical Emissions

每年都有数百万美国人因膝盖受伤而住院，如半月板撕裂或前十字韧带（AGL）扭伤。此外，膝盖受伤是错过工作日的最常见原因之一。目前治疗膝关节损伤的范例最初涉及频繁的物理治疗访问、专家的主观评估以及可能的手术;在这些初始步骤之后，患者继续参与物理治疗，定期地（通常不频繁地）返回诊所进行后续主观评估，并且主要基于症状和疼痛来确定他/她的关节健康康复状态。目前还没有技术可以为膝关节损伤患者提供有关其关节康复状况的频繁、客观和深入的信息。该项目的假设是，使用嵌入可穿戴包裹中的传感器测量的关节声音可以为关节健康康复评估提供临床相关的生物标志物，并最终允许患者根据客观反馈动态调整他们的康复练习。这可能会加速康复，降低再次受伤的风险，并使患者能够控制自己的康复。本研究拟对这些声音及其测量进行研究，并提出一个综合方案，具体目标包括：（1）设计并实现一种超低噪声、高带宽、晶圆级封装的微加速度计芯片，用于皮肤表面关节声的接触式高保真测量;（2）利用尸体模型阐明这些声音的来源及其随损伤的变化;（3）开发用于从关节声音信号中提取可用于评估关节健康的显著特征的算法;（4）在手术前后以及在手术后几个月的康复期间两次评估20名半月板撕裂受试者的群体中的传感器和分析。

Every year, millions of Americans present at the hospital with knee injuries, such as meniscus tears or anterior cruciate ligament (AGL) sprains. Moreover, knee injuries are one of the most common causes of missed workdays. The current paradigm of treating knee injuries initially involves frequent physical therapy visits, subjective evaluations by experts, and possibly surgery; following these initial steps the patient continues to participate in physical therapy, periodically - and typically infrequently - returns to the clinic for follow-up subjective evaluations, and bases his I her joint health rehabilitation status mainly on symptoms and pain. There is no technology available currently to provide patients with knee injuries frequent, objective, and in-depth information regarding the status of their joint rehabilitation. The hypothesis for this project is that the sounds of the joints measured using sensors embedded in a wearable wrap can provide a clinically-relevant biomarker for joint health rehabilitation assessment, and can ultimately allow patients to tune their rehabilitation exercises dynamically based on objective feedback. This could potentially accelerate rehabilitation, reduce the risk of re-injury, and empower patients to be in control of their rehabilitation. This project proposes to study these sounds, and their measurement, with an integrative program including the following specific aims: (1) Design and implement an ultra-low noise, high-bandwidth, wafer-level-packaged micro-accelerometer chip for contact measurement of joint sounds from the skin surface with high fidelity; (2) Elucidate the origin of the sounds and how they change with injury using a cadaver model; (3) Develop algorithms for extracting salient features from the joint sound signals that can be used to assess joint health; (4) Evaluate the sensors and analytics in a population of 20 subjects with meniscus tears, before and after surgery, and twice during rehabilitation several months following surgery.