Knee Rehabilitation Using Range of Motion Exercise Feedback

1. Local Server Receives data Knee sensors Analyzes data Knee Rehabilitation Using Range of Motion Exercise Feedback Measuring data Infer results z z z z y y y y x x x x Receiver (PDA / Smartphone) Display data & info. • Yeongrak Choi1, Sangwook Bak1, Sungbae Cho1, Changsuk Yoon2, John Strassner1, M. Jamal Deen1 and James Won-KiHong1 • 1Division of IT Convergence Engineering, POSTECH, Pohang, Korea • 2Department of Computer Science and Engineering, POSTECH, Pohang, Korea Communicationwith user • Installation & Implementation • Implemented server-based user-interface • Conclusion • Our system provides better knee rehabilitation – accurate, light weight and cheap • Filtering technique to calibrate the data from different sensors • Future Work • Enhance the accuracy of measuring knee angles • Develop ontologies with rules to augment knowledge • Improve user interface - smart phone application and better server interface • Apply to other joints and new situations • Sensor Experiment • Scenario: Regularly bends and unbends leg for 10 times (30-40˚ to 120˚) • Evaluation: Use of Kalman filter minimizes errors from rapid movement • Importance of Knee Rehabilitation • Difficult to return to its original state after injury or operation • Stable, enduring and customized rehabilitations required • Feedback on the health of knee required • Accuracy of monitored data is essential for customized knee exercise planand to ensure the overall safety of the knee rehabilitation process • Beneficial to both patients and doctors • Knee Joint ROM (Range of Motion) Exercise • Helpful for knee rehabilitation • Criteria for checking the health of knee • Activity sensor using WBAN • Two-axis accelerometer - less accurate • ZigBee used for communication - less popular • AKROD (Active Knee Rehabilitation Orthotic Devices) • Large size and Heavy (3.18kg); no network functionality • Our work • Better accuracy - Uses 3-axis accelerometer and gyroscope • Popular technology - Uses Bluetooth to communicate • Light-weight, less than 400g • Knee Rehabilitation Monitoring and Inference System • Monitor the knee ROM exercise • Maximum/minimum angle, period per ROM activity, moving count, # of sets, … • Analyze exercise data • How much exercise per day? • Infer the health of the knee and recommend changes if necessary • Determine if the health of the knee is improving based on measurement data • Is more exercise needed, or is current exercise sufficient? • Sensors - use two Wiimotes • 3-axis MEMS accelerometer (ADXL330) • Measuring magnitude and direction • 2-axis MEMS gyroscope (IDG-600) in MotionPlus • Gyroscope for tracking movement • Inference using Ontologies • Inferring rules • Ability: Evaluating maximum and minimum angles • Intensity: Checking the number of sets • Design Objectives: Portable, User-friendly and Smart! • Sensors installed into knee support Our Design Related Work Conclusion Results Bluetooth (PAN) LAN / WLAN(Socket Programming) g Exercise Guidance (from Dr.) Daily Result Patient Doctor Overview Motivation g Ontology forKnee Rehab. Symptoms Sensor data Sensor Data Monitoring Results / Inference