This paper proposes a novel solution to the common problem of knee stiffness experienced by patients following knee replacement surgery. The paper suggests designing a wearable knee pad that is fitted with three 6-axis IMU sensors to monitor, evaluate, and process the movement data of the patient’s knee in real-time. The data collected would then be used to provide appropriate recovery methods and encouragement to the patients. This paper highlights the advancements in wearable technology and remote patient monitoring, which allow for the improvement of postoperative care and behavioural change in knee replacement surgery patients. The literature review section examines the role of mHealth technologies and wearable sensors in remote patient monitoring and behaviour change for total knee arthroplasty patients. The research recommends integrating mobile health and wearable sensor technologies for remote patient monitoring and behaviour change interventions in these patients for enhanced postoperative care and improved patient outcomes. The proposed methodology includes user-friendly interfaces that provide continuous monitoring, personalized rehabilitation programs, and enhanced patient engagement using machine learning algorithms to recognize patterns and anomalies in knee motion data. The data analysis section employs various filtering, segmentation, normalization, and statistical methods to ensure accurate and meaningful data analysis. The document concludes by highlighting the need for further research to optimize and validate these technologies and interventions.

中文翻译：

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膝关节置换患者和可穿戴护膝

本文针对膝关节置换手术后患者所经历的膝关节僵硬的常见问题提出了一种新颖的解决方案。论文建议设计一款可穿戴护膝，配备三个 6 轴 IMU 传感器，用于实时监测、评估和处理患者膝盖的运动数据。收集到的数据将用于为患者提供适当的康复方法和鼓励。本文重点介绍了可穿戴技术和远程患者监测的进步，这些技术可以改善膝关节置换手术患者的术后护理和行为改变。文献综述部分探讨了移动医疗技术和可穿戴传感器在远程患者监测和全膝关节置换术患者行为改变中的作用。该研究建议整合移动健康和可穿戴传感器技术，用于远程患者监测和行为改变干预，以加强术后护理并改善患者治疗效果。所提出的方法包括用户友好的界面，可提供持续监测、个性化康复计划，并使用机器学习算法识别膝关节运动数据中的模式和异常来增强患者参与度。数据分析部分采用各种过滤、分段、归一化和统计方法来确保数据分析准确且有意义。该文件最后强调需要进一步研究来优化和验证这些技术和干预措施。