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Real-Time ACL Injury Monitoring and Prevention Using an IoT-Based Wearable Device
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Abstract
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Inventors
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Specification
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ORDINARY APPLICATION
Published
Filed on 16 November 2024
Abstract
The proposed invention is an IoT-based wearable medical device designed for monitoring and assisting in the rehabilitation of anterior cruciate ligament (ACL) injuries. Traditional methods for detecting ACL injuries, such as physical examinations, Lachman tests, and magnetic resonance imaging (MRI), often require clinical environments, specialized equipment, and trained professionals. These methods are not suitable for continuous monitoring and can be costly, time-consuming, and inaccessible for routine rehabilitation, particularly in remote or resource-limited areas. In contrast, this device employs motion sensors placed on the thigh and shin to measure real-time knee flexion angles, processed by an embedded microcontroller. Additionally, the device monitors the electrical activity of surrounding muscles. Real-time data is transmitted to a cloud platform, and a dedicated Android application provides real-time visualization, predefined exercise routines, haptic feedback, and voice guidance for knee angle monitoring. The data is recorded for subsequent analysis and to enable machine learning-based treatment recommendations. The device is ergonomically designed for wear on the thigh and shin, with adjustable settings managed through the mobile application. This approach enables continuous, real-time monitoring outside clinical settings, offering a more cost-effective and accessible alternative for ACL rehabilitation. It is particularly beneficial for sportspersons, as it allows them to track their recovery progress in real-time, perform rehabilitation exercises efficiently, and receive personalized feedback, helping to accelerate recovery and prevent re-injury during training and competition.
Patent Information
Application ID | 202441088812 |
Invention Field | BIO-MEDICAL ENGINEERING |
Date of Application | 16/11/2024 |
Publication Number | 47/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Pratyush | Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bangalore-560111 | India | India |
Dr. Mahesh Kumar N | Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bangalore-560111 | India | India |
R Harinandan | Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bangalore-560111 | India | India |
Puram Vamshi | Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bangalore-560111 | India | India |
Dr. Manasa R | Department of Electronics and Communication Engineering, Dayananda Sagar College of Engineering, Bangalore-560111 | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Dayananda Sagar College of Engineering | Shavige Malleshwara Hills, Kumaraswamy Layout, Bangalore | India | India |
Specification
Description:FIELD OF INVENTION
[001] The field of the invention falls under wearable medical electronics and IoT-based health monitoring systems. It specifically addresses the domain of rehabilitation devices for monitoring knee movements and muscle activity to prevent ACL injuries and aid recovery.
BACKGROUND AND PRIOR ART
[002] Existing rehabilitation devices for ACL injuries often rely on manual monitoring, lack real-time feedback, and are generally not personalized to individual patients. Current solutions do not provide continuous monitoring of knee angles or muscle activity in real-time, limiting their effectiveness in preventing reinjury or optimizing recovery protocols. Some conventional devices also lack connectivity to cloud platforms for data storage and analysis. The present invention overcomes these limitations by integrating IMU and EMG sensors with IoT technology and providing a comprehensive, real-time monitoring system that enhances recovery and prevents re-injury.
[003] Merletti & Farina in "Electromyography: a practical guide" (2016) provide a detailed practical guide on electromyography (EMG) techniques. Their study outlines the use of EMG sensors to measure muscle activation patterns, offering valuable insights into neuromuscular control during motion. Although comprehensive in its coverage of EMG application, the guide is not an empirical study and may lack the latest research updates.
[004] Renström et al. in "To measure ACL strain during hamstring and quadriceps activity" (2017) conducted an experimental cadaveric study to measure ACL strain during hamstring and quadriceps activity. The findings suggest that quadriceps activity increases ACL strain, while hamstring activity decreases it. When both muscle groups are activated, the strain is balanced. Although precise, the study is limited by its use of cadaver models, which may not fully represent real-life conditions.
[005] Lin et al. in "A survey on Internet of Things (IoT) applications for smart healthcare" (2018) developed and evaluated a wearable sensor system to provide real-time feedback on knee joint motion during rehabilitation. The study demonstrated that wearable sensors can be effective in monitoring joint motion, improving rehabilitation outcomes by offering real-time data. This innovation holds promise but is constrained by the practical challenges of wearable sensor technology.
[006] Kevin Cox and Drew Hamrock in "How wearable sensing can be used to monitor patient recovery following ACL reconstruction" (2022) carried out a randomized controlled trial to evaluate specific exercises in ACL rehabilitation. The results showed that incorporating plyometric and neuromuscular training can significantly improve functional outcomes and reduce the risk of re-injury. The study's rigorous design is a strength, though its small sample size and short-term follow-up are limitations.
[007] Waldén et al. in "A 15-year follow-up of ACL reconstruction with a patellar tendon graft: patient satisfaction, activity level, and knee function" (2013) evaluated long-term outcomes of ACL reconstruction using a longitudinal cohort study. Their research highlights that ACL injuries remain a significant concern, with a high recurrence rate in athletes. This long-term study is valuable for its comprehensive follow-up, though it is limited by potential selection bias and loss of participants over 15 years.
[008] Wright et al. in "The economic burden of anterior cruciate ligament injuries in the United States" (2002) focused on the economic burden of ACL injuries in the U.S. through an economic analysis. They estimated that the healthcare system pays around $2 billion annually for ACL-related injuries, emphasizing the need for effective prevention and rehabilitation programs. However, the study relies on assumptions, making cost variability a concern.
[009] Hewett et al. in "Anterior cruciate ligament injuries in female athletes: Part 2, a meta-analysis of neuromuscular interventions aimed at injury prevention" (2005) conducted a meta-analysis of neuromuscular interventions aimed at preventing ACL injuries. Their results demonstrated that such interventions significantly reduce the risk of ACL injuries in athletes. The comprehensive nature of the study is a strength, although variability in intervention protocols and potential publication bias are noted limitations.
[010] Finally, Luinge & Vrahatis in "Physiological signals acquisition & processing" (2009) provided a review article on wearable sensors used for acquiring and processing physiological signals such as joint angles and accelerations. These sensors are commonly used in inertial measurement devices. While broad in scope, the study is not empirical and relies heavily on existing literature.
SUMMARY OF THE INVENTION
[011] The present invention offers a compact, IoT-based wearable device for ACL injury monitoring and rehabilitation, incorporating multiple sensors (IMU and EMG) for real-time knee angle and muscle activity monitoring. The device is processed by an ESP32 microcontroller that calculates knee bending angles using Euler angles and vector theory. Data is sent to a cloud platform and accessed via an Android application, which provides visual feedback, exercise routines, haptic alerts, and voice support. The application records data for each exercise session, aiding in further treatment planning and machine learning-based predictions. The device is designed for easy wear and adjustable settings to personalize the recovery experience.
BRIEF DESCRIPTIONS OF DRAWINGS:
[012] Figure 1: Illustrates the wearable device attached to the thigh and shin using adjustable straps, housing IMU sensors and vibration motors.
[013] Figure 2: Demonstrates the system's data flow and connectivity, from sensors capturing knee angles and muscle activity to the microcontroller processing and sending data to the cloud.
[014] Figure 3: Shows the sequence of operations, starting from sensor data acquisition, processing, data upload to the cloud, fetching to the app, and providing feedback to the user.
[015] Figure 4: Displays different app screens for real-time data monitoring, exercise routines, and alerts for reaching threshold angles.
DETAILED DESCRIPTION OF THE INVENTION
[016] The invention consists of a wearable device designed to be placed on the thigh and shin for ACL injury monitoring and rehabilitation. The device uses two IMU sensors to measure knee bending angles by calculating Euler angles and vector theory. The sensors are connected to a microcontroller, which processes the data and sends it to a cloud platform using IoT. An EMG sensor monitors electrical activity around the muscles closest to the ACL to provide comprehensive feedback on muscle stretching during exercises. The data is visualized in real-time on an Android application that also provides predefined exercises from beginner to expert levels, haptic feedback through a vibration motor, and voice support for users. Doctors can adjust threshold angles for safe rehabilitation. The application stores data in CSV files for analysis and machine learning-based predictions for personalized treatment. The device is lightweight, portable, and designed for comfort with adjustable straps. , C , Claims:1. A novel wearable device that combines knee joint angle sensors and EMG sensors to simultaneously monitor knee movements and muscle activation in real-time for improved ACL injury monitoring and rehabilitation.
2. A system where real-time data from a wearable device is processed locally and transmitted via IoT to a cloud-based platform for storage, retrieval, and analysis through a dedicated Android application.
3. A wearable ACL monitoring device with an integrated haptic feedback system that triggers when knee angles reach user-defined thresholds, customizable via a companion application.
4. A custom mobile application providing real-time data visualization, voice-guided rehabilitation exercises, and difficulty level adjustments for ACL injury recovery, with automated tracking and personalized exercise plans.
5. A wearable ACL rehabilitation device integrated with machine learning algorithms for predictive analysis of recovery progress, based on stored sensor and exercise data.
6. A method for calculating knee bending angles in an ACL rehabilitation device using Euler angles and vector theory for enhanced accuracy and real-time motion analysis.
7. A multi-level rehabilitation system for ACL injury recovery that provides real-time haptic and visual feedback, with adjustable exercise routines and difficulty levels based on user progress.
Documents
Name | Date |
---|---|
202441088812-COMPLETE SPECIFICATION [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-DRAWINGS [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-FORM 1 [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-FORM 18 [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-FORM-9 [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-REQUEST FOR EARLY PUBLICATION(FORM-9) [16-11-2024(online)].pdf | 16/11/2024 |
202441088812-REQUEST FOR EXAMINATION (FORM-18) [16-11-2024(online)].pdf | 16/11/2024 |
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