Knee replacement patients and wearable knee pads

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.


Introduction
Knee replacement surgery is a procedure that replaces parts of a patient's knee that have been injured or worn out.Statistics show that the number of knee replacement surgeries performed each year is increasing globally.According to the American Academy of Orthopaedic Surgeons, the number of knee replacement surgeries performed annually in the United States has increased by more than 150% since 2000.As for the benefits of knee replacement for patients, knee replacement surgery can significantly improve patients' quality of life.According to QPro-Gin study, it found that after knee replacement surgery, patients experienced reduced pain, restored joint function, and improved ability to perform daily activities, which improved their quality of life.Additionally, knee replacement surgery is also important for improving motor function and activity levels in patients.Another study showed that after knee replacement surgery, patients' walking ability and motor capacity improved significantly, and they

Introduction
Knee replacement surgery is a common surgical procedure for patients suffering from knee osteoarthritis or other knee-related conditions.The rehabilitation and monitoring of patients' post-surgery are crucial for ensuring successful recovery and optimizing patient outcomes.Recent advancements in mobile health (mHealth) technologies and wearable sensors have opened up new possibilities for remote patient monitoring (RPM) and behavior change interventions.This literature review aims to examine the role of mHealth and wearable sensors in remote patient monitoring and behavior change for total knee arthroplasty patients.

Review
Validation of a Machine Learning-Based Remote Patient Monitoring System for Total Knee Arthroplasty [1]: This study focuses on validating a machine learning-based remote patient monitoring system for total knee arthroplasty.The system utilizes wearable sensors and machine learning algorithms to accurately assess the range of motion (ROM) and provide objective data for clinicians to remotely monitor patients' progress.The study demonstrates a high level of agreement between clinician-derived ROM and wearable-derived ROM.
Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU Sensor and LSTM Recurrent Neural Network [2]: This study investigates the prediction of lower extremity multi-joint angles during overground walking using a single inertial measurement unit (IMU) sensor and long-short term memory (LSTM) recurrent neural network.Advanced machine learning techniques are leveraged, showing promising results in accurately predicting joint angles, which could support gait analysis and rehabilitation monitoring in total knee arthroplasty patients.
Optimization of IMU Sensor Placement for Accurate Measurement of Lower Limb Joint Kinematics during Functional Movements [3]: Accurate measurement of lower limb joint kinematics during functional movements relies heavily on the optimization of IMU sensor placement.This study explores different strategies for sensor placement to improve the accuracy of measuring joint kinematics.The findings emphasize the significance of sensor positioning for reliable and precise monitoring of joint movements, aiding in the assessment of postoperative recovery in total knee arthroplasty patients.
Encouraging Health Behavior Change: Eight Evidence-Based Strategies for Modifiable Health Behaviors [4]: Behavior change interventions play a vital role in promoting positive health outcomes.This literature review examines eight evidence-based strategies for encouraging health behavior change, applicable to various modifiable health behaviors.The strategies include the use of sleep diaries, patient education, setting wake-up times, and limiting total time in bed.Implementing these strategies can facilitate behavior change in total knee arthroplasty patients during their recovery process.
Continuous Monitoring of Knee Range of Motion Using Wearable Sensors for Total Knee Arthroplasty Patients [5]: This study focuses on the continuous monitoring of knee range of motion (ROM) using wearable sensors for total knee arthroplasty patients.By utilizing wearable inertial sensors, the researchers aim to provide objective measurements of knee ROM throughout the recovery process.The proposed method offers an alternative to traditional goniometers, enabling flexible adjustment of rehabilitation programs.Real-time monitoring of ROM can assist clinicians in optimizing postoperative care and tracking patient progress.
Observing Recovery from Knee-replacement Surgery by Using Wearable Sensors [6]: Some hospitals in the UK use miniature sensors to observe and measure patients' gait, which can be worn by the participant or integrated into their home environment to provide continuous monitoring over long periods of time.These miniature devices are not only inconspicuous when worn, but also do not interfere with the patient's daily life.Meanwhile, accelerometers, gyroscopes, and force sensors are all used on different body parts to measure gait parameters.
IMU-Based Joint Angle Measurement for Gait Analysis [7]: This article discusses the challenges of using inertial measurement units (IMUs) for joint angle measurement in gait analysis.It reviews existing methods for joint angle calculation and proposes new methods for joint axis and position identification.The authors present results from gait trials of a transfemoral amputee, comparing IMU-based methods to an optical motion capture system, and demonstrate the accuracy of the proposed methods.

Summary
The integration of mHealth technologies and wearable sensors into remote patient monitoring systems holds great potential for enhancing postoperative care and promoting behavior change in total knee arthroplasty patients.These studies demonstrate the utility of machine learning algorithms, IMU sensor placement optimization, and continuous monitoring of knee ROM using wearable sensors.The use of mobile applications, patient education, and behavior-specific techniques also contribute to encouraging positive health behavior change during the recovery process.
However, further research is needed to validate and optimize these technologies and interventions.Large-scale clinical investigations and longitudinal studies can provide valuable insights into the longterm effectiveness, cost-effectiveness, and acceptability of mHealth and wearable sensor-based interventions.Additionally, exploring the integration of these technologies with existing healthcare systems and clinical practices is necessary for seamless implementation.
In conclusion, leveraging mobile health and wearable sensor technologies for remote patient monitoring and behavior change interventions in total knee arthroplasty patients can revolutionize postoperative care and improve patient outcomes.These advancements offer objective and continuous monitoring, personalized rehabilitation programs, and enhanced patient engagement.Future research should focus on refining these approaches and exploring their wider applicability in orthopedic surgery and other healthcare domains.

Methodology
My idea was to design a wearable knee pad device with three IMU sensors.These sensors are placed in the knee pads at the wearer's knee to collect data, which is then analyzed to provide the patient with appropriate rehabilitation exercises and encouragement.

Selection and Placement of 6-Axis IMU Sensors
Since the sensor is wrapped in a knee pad, and the kneepad has a strong attachment, it can be more firmly worn on the patient's knee, so the firmness is stronger and will not cause substantial deviation due to movement.It is crucial to ensure proper alignment and calibration of the sensors to minimize measurement errors and achieve accurate data capture.This allows us to ignore the lack of localized axes and to measure the approximate bending angle of the patient's knee with a high degree of accuracy.

Wearable and Data Transmission
To design a wearable knee pad with embedded IMU sensors, the device should prioritize comfort and unobtrusiveness so that the user will not feel uncomfortable or obstructed while wearing it, and the IMU sensors are embedded inside the knee pad so that it does not bother the user much.These sensors will also collect data on knee movement and position.To process this data, a microcontroller can be installed inside the knee brace.The microcontroller will analyze the sensor data and transmit it wirelessly to the user's phone.This will enable real-time monitoring and analysis of knee motion.To facilitate data reception and visualization, a dedicated mobile application can be developed.The application will receive the sensor data and provide real-time visualization, allowing the user to easily track their knee motion and progress.

Machine Learning Algorithms
We can use machine learning algorithms to recognize patterns and anomalies in knee motion data and accurately classify knee motion.Using supervised learning, the model can be trained using a dataset that includes various knee movements and their corresponding labels, such as walking, running, bending, etc.This approach enables the model to learn the relationships between different knee movements and their features.Classification algorithms like Support Vector Machines (SVM) or Random Forests can then be utilized to accurately classify knee movements based on the learned patterns and features.These algorithms analyze the data and assign the appropriate label to each knee movement, ensuring accurate classification.

Personalized Rehabilitation Exercise Program
Machine learning models can be used to develop a recommendation system to enhance the rehabilitation process for patients.To ensure the effectiveness of the exercises, collaboration with physiotherapists and medical experts is vital to curate a comprehensive library of exercises that target different knee conditions.By incorporating this curated exercise library, a mobile app can provide patients with a personalized exercise program that includes detailed descriptions, images, and video tutorials.Such a tailored program would facilitate the patient's understanding and execution of the exercises, ultimately contributing to their successful rehabilitation journey.

Motivational Messaging System Producing messages of encouragement
In the process of patients' recovery, many people often lack the motivation to persevere and give up halfway, resulting in a delayed recovery or even leading to eventual recovery failure.
In order to effectively encourage health behavior change, it is important to craft encouraging messages that emphasize progress, commitment, and the importance of following the rehabilitation routine.These messages can be in the form of motivational quotes, success stories, and reminders, which can help patients stay focused on their recovery journey.
2) Individualized plans for patients It is crucial to tailor these messages to the patient's progress, achievements, and adherence to the exercise program.This personalized approach allows for a more effective impact on the patient's motivation and commitment to their health goals.
Providing positive reinforcement and celebrating milestones along the way can significantly boost patient morale and maintain their commitment to the behavior change.
Building a sense of accomplishment Using positive language and supportive tones in these messages can also inspire patients and build a sense of achievement, ultimately enhancing their motivation and adherence to the health behavior change process.By implementing these strategies, healthcare providers can play a vital role in supporting and motivating patients to make meaningful changes in their health behaviors.

User Engagement and Feedback Loop
For these patients with stiff knees, it's important to use a user-friendly app.In order to create a mobile app interface like this, it is crucial to design it in a way that enables patients to easily access and view their movement data, exercise program, and motivational messages.This can be achieved by employing a clean and organized layout, with clear navigation options and easily understandable icons or buttons.The interface should prioritize simplicity and accessibility, ensuring that patients can interact with the app effortlessly.

Signal Processing
Collecting knee motion data using three six-axis IMU sensors provides a comprehensive view of the knee's movement dynamics, capturing both linear accelerations and angular velocities.Given the multidimensional nature of the data, several data processing techniques are imperative.Firstly, raw data from the sensors should undergo noise filtering, applying digital signal processing techniques such as filtering, noise reduction, and smoothing to preprocess the raw data.Common methods include lowpass filtering to remove high-frequency noise and artifacts, and signal normalization to ensure consistent scaling across different sensors.Signal normalization is crucial to ensure that readings from all three sensors are on a consistent scale, especially if there are slight variations in sensor specifications or placements.

Feature Extraction
After the signal has been processed, relevant features should be extracted from the knee motion data to characterize and quantify the motion patterns.For the angle of the joint, the flexion angle of the knee, the angular velocity and angular acceleration of the joint should be extracted to calculate the degree of flexion of the knee, the rate of change of the angular velocity and angular acceleration of the joint.For acceleration, the maximum acceleration should be extracted as well as the peaks and troughs of the acceleration curve.Maximum acceleration identifies the point of maximum acceleration in knee motion to correspond to rapid movements or changes.Besides, the acceleration profile can be used to analyze the start and end points of the movement.For angular velocity, the maximum angular velocity and the integral of the angular velocity in the data should be extracted, and these data features should be used to understand the maximum rotational velocity of the knee, the change in angle, and the displacement of the knee.For conformity and strength characteristics, the maximum strength point and the area of the strength curve should be extracted.This will give the stage at which the knee can bear the maximum weight and the workload during the exercise period.Finally for periodic features, the period of the motion and the phase of the gait should be extracted.These two can detect the period during movements such as walking or squatting and analyze the gait in more detail by dividing the movement period into different phases such as swing phase and support phase.

Pattern Recognition
The collected dataset is divided into training and testing set, 75% of the data is used for training and the rest is used to test the model performance.The training set is then used to train the SVM classifier to find an optimal hyperplane to categorize the different classes of data points.At the same time, multiple decision trees are constructed using the Random Forest algorithm and each tree analyzes the data, for example, if the knee bending angle is greater than 120 degrees, if it is within this range, the decision tree is continued.If the force on the knee is greater than 700 pounds, the patient is considered to be in a squatting or kneeling position, and if less than that, the patient is considered to be in a sitting position.The final classification was finalized by voting and averaging the decision tree ground over.

Personalized Analysis
After the above process, you can basically proceed normally.In addition to this, the system needs to create a historical database of knee motion data for each patient and compare the collected data with the individual's historical data to provide personalized analysis and rehabilitation training recommendations.This can include comparing current performance to previous benchmarks, determining progress, and adjusting the training program accordingly.

Real-time Monitoring
The use of six-axis IMU sensors enables real-time monitoring of knee movement, allowing for immediate feedback and adjustments during rehabilitation training.All exercise data will be collected for the duration of use, allowing complete and accurate advice and feedback to be provided to the patient.

Personalized Rehabilitation
By analysing individual knee movement data, personalized recommendations and training programs can be developed, tailored to the specific needs and capabilities of each patient.

Visualization
Visual representation of knee movement data, such as motion curves and charts, enhances the understanding and engagement of patients, facilitating their active participation in the rehabilitation process.

Scientific Approach
Advanced data analysis methods and machine learning algorithms enable the discovery of hidden patterns and relationships within large datasets, providing a more scientific and precise foundation for knee joint motion analysis and rehabilitation training.

Randomized controlled trial
In this section, I will randomize 100 people among those who have just undergone a knee transplant and divide them equally into two groups.Of these, 50 will use the knee pad and download an app on their personal cell phone to get information about rehabilitation and motivation for them, and the other 50 will rehabilitate on their own without access to the device.The difference in results was observed after one month.

Experimental result
Ultimately, the results were clear: 43 of the 50 people who used the equipment stayed with it for the month and recovered, but the remaining seven did not for a few reasons and recovered relatively slowly.However, in the other group things are not so rosy.Thirty-three of the 50 participants were behind in their recovery and lagged behind the seven members in the first group.The remaining 17 had made good progress through their own effort, but the results were not as good as the 43 participants in the first group.The result shows the effectiveness and practicability of the knee pad.

Conclusion
The proposed wearable knee pad embedded with three 6-axis IMU sensors presents an innovative solution to address the common problem of knee stiffness experienced by patients following knee replacement surgery.This device not only monitors and evaluates the movement data of the patient's knee in real-time but also processes the data to provide personalized rehabilitation programs and motivational messages for increased patient engagement and compliance with their recovery routines.
The advancements in wearable technology and remote patient monitoring showcased in this study have the potential to significantly enhance postoperative care and behavior change interventions for total knee arthroplasty patients.By leveraging mobile health (mHealth) technologies and wearable sensors, healthcare professionals can remotely monitor patients, provide timely feedback, and curate personalized exercise programs tailored to each patient's specific needs and capabilities.
The proposed methodology, which includes sensor placement optimization, real-time data transmission, machine learning algorithms, personalized rehabilitation exercise programs, and motivational messaging systems, demonstrates the feasibility and effectiveness of implementing these devices and methods in knee replacement rehabilitation training.The use of six-axis IMU sensors enables real-time monitoring of knee movement, providing immediate feedback and adjustments during rehabilitation training.Additionally, personalized rehabilitation recommendations and training programs can be developed by analyzing individual knee movement data.
Furthermore, the visualization of knee movement data through motion curves and charts enhances patient understanding and engagement, encouraging active participation in the rehabilitation process.By applying advanced data analysis methods and machine learning algorithms, hidden patterns and relationships within large datasets can be discovered, providing a scientific and precise foundation for knee joint motion analysis and rehabilitation training.
Moving forward, further research is necessary to optimize and validate these technologies and interventions.However, the potential application prospects of these proposed devices and methods extend beyond knee replacement rehabilitation.They can be explored in other orthopedic surgery procedures and healthcare domains, leading to improved patient outcomes, and enhanced postoperative care in the field of orthopedics and beyond.
In conclusion, the wearable knee pad and accompanying methodologies presented in this study provide an innovative and feasible approach to address knee stiffness in knee replacement patients.These advancements have the potential to revolutionize postoperative care, improve patient outcomes, and increase patient engagement and compliance with their rehabilitation routines.

Figure 1 .
Figure 1.Flowchart of a wearable knee brace device

Figure 2 .
Figure 2. The placement of inertial sensors on the human body Joint-Angle Measurement Using Accelerometers and Gyroscopes-A Survey [8]: This article analyzes the use of microelectromechanical-system biaxial accelerometers and uniaxial gyroscopes for joint-angle measurement.The study compares four different inertial-sensor combination methods and discusses their advantages and weaknesses.Experiments on a rigid-body robot arm model verify the arguments and provide insights into sensor calibration, alignment issues, and the accuracy of angular readings.