Division of IT Convergence Engineering
Related Work
Knee Rehabilitation Using Range of Motion Exercise FeedbackYeongrak Choi1, Sangwook Bak1, Sungbae Cho1, Changsuk Yoon2, John Strassner1, M. Jamal Deen1 and James Won-Ki Hong1
1 Division of IT Convergence Engineering, POSTECH, Pohang, Korea2 Department of Computer Science and Engineering, POSTECH, Pohang, Korea
Motivation Our Design
Conclusion
Overview
Results
• 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
• 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?
• Our work– Better accuracy - Uses 3-axis accelerometer and gyroscope– Popular technology - Uses Bluetooth to communicate– Light-weight, less than 400g
• 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
• 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
Implemented server-based user-interface
Sensor DataMonitoring Results / Inference
Patient Doctor
Sensor data Symptoms
Ontology forKnee Rehab.
xy
z
gxy
z
• 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
• 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
Daily Result
Exercise Guid-ance(from Dr.)
• Installation & Implementation
Examples of Inferring Rules
Ability if (Max-Min > Guided An-gle) Good
Intensity if (Daily Set > Guided Set) Enough
Bluetooth(PAN)
LAN / WLAN(Socket Programming)
Knee sensorsMeasuring data
Local Server
Infer resultsAnalyzes dataReceives data
Receiver(PDA / Smartphone)
Communicationwith user
Display data & info.
g
xyz
xyz
Top Related