Frequency-domain analysis of IMU data for knee movement disorders following TKA

TKA 术后膝关节运动障碍的 IMU 数据频域分析

• 批准号: 2596853
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2596853
• 关键词: Frequency; domain; analysis; IMU; data

Enmovi Ltd is affiliated to the established OrthoSensor Inc., a company whose broad aim is to quantify orthopaedics through sensor-assisted technology. Both have recently been taken over by Stryker. Their products aim to enable healthcare providers to deliver evidence-based treatments to improve clinical and economic outcomes for patients and healthcare stakeholders. Enmovi Ltd. have developed a wearable, inertial measurement unit (IMU) technology and associated basic algorithms to monitor knee function of total knee arthroplasty (TKA) patients post-operatively. These data are then presented in a useful form, via an App, to both patient and clinician, informing clinical decision making and motivating the patient to achieve prescribed functional goals. Moreover, ultimately, multi-patient data will be combined to create a big data set in order for machine learning algorithms to identify hitherto unknown connectivities between surgical and post-operative outcomes.Knee arthroplasty is commonly and successfully employed in the UK for the treatment of knee arthritis. With a growing aged population and increasing prevalence of knee arthritis in these age groups the number of procedures is growing and set to grow further. Revision rates of knee arthroplasty are nationally reported at 3% and pose an additional increasing burden on health services. Optimal post-operative management of TKA patients is key to achieve an overall successful outcome of high patient satisfaction and minimise subsequent referrals and revisions. However, patients do not receive the necessary physiotherapy interventions and struggle to maintain the recommended activities whilst discharged from hospital. A lack of supported rehabilitation often leads to a deficit of function with regards to activities of daily living (ADL) and thus reduced patient satisfaction.A recent PhD student in the Department of Biomedical Engineering, supervised by the PI, developed a classification algorithm, based on the continuous wavelet transform (CWT) of accelerometer data, which discriminates between those who have self-reported knee instability following TKA, and those who do not. Knee instability following TKA accounts for 15-20% of revisions. Hitherto, no biomechanical description nor quantification of "instability" existed and this work has enabled instability to be more accurately and quantitatively defined than a dichotomous, self-reported, yes/no variable, with significant ramifications for understanding the mechanical aetiology of instability and thus potential interventions. However, this work focussed on one acceleration dimension: the mediolateral direction. An IMU device, which generally includes a gyroscope, enables the effects of gravity to be more easily removed from acceleration data emanating from a lone 3D accelerometer. Thus, IMU data lends itself to a multi-dimensional frequency domain analysis of knee function with enhanced expectations of diagnostic and aetiological performance, not just associated with instability, but potentially other mechanical issues such as aseptic loosening, component malalignment and arthrofibrosis. If demonstrated to be successful, any resulting algorithm can be included in the firmware of the commercial device, providing, for the first time a simple biomechanical, diagnostic test for abnormal knee function following TKA.AimThe proposed PhD aims to validate clinical IMU data against gold standard measures; and to develop innovative frequency domain analyses of tri-axial data from the IMU. In doing so, we aim to identify and quantify musculoskeletal pathologies of the TKA knee, and, consequentially, provide mechanistic and aetiological descriptions of these pathologies to inform future clinical interventions.

Enmovi Ltd隶属于已成立的OrthoSensor Inc.，该公司的主要目标是通过传感器辅助技术来量化整形外科。这两家公司最近都被斯特赖克接管了。他们的产品旨在使医疗保健提供者能够提供循证治疗，以改善患者和医疗保健利益相关者的临床和经济结果。Enmovi Ltd.开发了一种可穿戴惯性测量单元（IMU）技术和相关的基本算法，用于监测全膝关节置换术（TKA）患者术后的膝关节功能。然后，这些数据通过应用程序以有用的形式呈现给患者和临床医生，为临床决策提供信息，并激励患者实现规定的功能目标。此外，最终，多名患者的数据将被结合起来创建一个大数据集，以便机器学习算法识别手术和术后结果之间迄今为止未知的联系。膝关节置换术在英国广泛成功地用于治疗膝关节炎。随着老年人口的增长和这些年龄组中膝关节炎患病率的增加，手术数量正在增加并将进一步增加。全国报告的膝关节置换术翻修率为3%，对医疗服务造成了额外的负担。TKA患者的最佳术后管理是实现高患者满意度的总体成功结局并最大限度地减少后续转诊和翻修的关键。然而，患者没有接受必要的物理治疗干预，并且在出院时难以维持推荐的活动。缺乏支持性康复通常会导致日常生活活动（ADL）方面的功能缺陷，从而降低患者满意度。生物医学工程系的一名最近的博士生在PI的监督下开发了一种分类算法，该算法基于加速度计数据的连续小波变换（CWT），该算法可以区分TKA后自我报告膝关节不稳定的患者，和那些没有的人TKA后膝关节不稳定占翻修的15-20%。然而，没有生物力学描述也没有量化的“不稳定”存在，这项工作使不稳定能够更准确和定量定义比二分法，自我报告，是/否变量，为理解不稳定的机械病因，从而潜在的干预措施的显着分歧。然而，这项工作集中在一个加速度维度：内外侧方向。通常包括陀螺仪的IMU设备使得能够更容易地从源自单独的3D加速度计的加速度数据中去除重力的影响。因此，IMU数据有助于对膝关节功能进行多维频域分析，提高诊断和病因学性能的预期，不仅与不稳定相关，还可能与其他机械问题相关，如无菌性松动、部件对线不良和关节纤维化。如果被证明是成功的，任何由此产生的算法可以被包括在商业设备的固件，提供，第一次一个简单的生物力学，诊断测试异常膝关节功能后TKA.AimThe建议博士旨在验证临床IMU数据对黄金标准措施，并开发创新的频域分析三轴数据从IMU。在此过程中，我们的目标是识别和量化TKA膝关节的肌肉骨骼病变，并相应地提供这些病变的机制和病因描述，为未来的临床干预提供信息。