Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

First Approach to Automatic Measurement of Frontal Plane Projection Angle During Single Leg Landing Based on Depth Video


Published on

Knee alignment measurements are one of the most extended indicators of knee-complex injuries such as anterior cruciate ligament injury and patellofemoral pain syndrome. The Frontal Plane Projection Angle (FPPA) is widely used as a 2-D estimation of knee alignment. How- ever, traditional procedures to measure this angle suffer from practical limitations, which leads to huge time investments when evaluating mul- tiple subjects. This work presents a novel video analysis system aimed at supporting experts in the dynamic measurement of the FPPA in a cost-effective and easy way. The system employs Kinect V2 depth sensor to track reflective markers attached to the patient leg joints to provide an automatic estimation of the angle formed by the hip, knee and ankle joints. Information registered by the sensor is processed and managed by a computer application that simplifies expert’s work and expedites the analysis of the test results.

Published in: Science
  • I'd advise you to use this service: ⇒ ⇐ The price of your order will depend on the deadline and type of paper (e.g. bachelor, undergraduate etc). The more time you have before the deadline - the less price of the order you will have. Thus, this service offers high-quality essays at the optimal price.
    Are you sure you want to  Yes  No
    Your message goes here

First Approach to Automatic Measurement of Frontal Plane Projection Angle During Single Leg Landing Based on Depth Video

  1. 1. First Approach to Automatic Measurement of Frontal Plane Projection Angle During Single Leg Landing Based on Depth Video UCAmI 2016 (Las Palmas de Gran Canaria, Spain) Carlos Bailon1, Miguel Damas1, Hector Pomares1 and Oresti Banos2 1Department of Computer Architecture and Computer Technology, CITIC-UGR Research Center, University of Granada, Spain 2Telemedicine Cluster of the Biomedical Signal and Systems Group, University of Twente, Netherlands
  2. 2. Knee alignment • Grade of alignment of the hip, knee and ankle joints. • Commonly used as a risk indicator of many biomechanical injuries related to knee joint when measured during the performance dynamic tasks. Anterior Cruciate Ligament (ACL) injuries Patellofemoral Pain Syndrome (PFPS) Potential misalignments during dynamic exercises are the most common injury mechanisms.
  3. 3. Quantification of knee alignment Projection of the angle formed by the hip, knee and ankle joints over the frontal plane of the body. Frontal Plane Projection Angle (FPPA) Wilson et al. “Core strength and lower extremity alignment during single leg squats” Medicine & Science in Sports & Exercise (2006)
  4. 4. Key limitations of existing techniques for FPPA measuring Inertial sensor-based systems 3D motion tracking video systems 2D offline video analysis  Accurate 3D rotations Possible motion restriction Non-deliberated sensor displacement  Tridimensional motion tracking  High sampling rate Need of high number of cameras Costly and space demanding  One camera needed  Portable and easy-to- use equipment Elevated time for analysis Prone to human errors 2D analysis
  5. 5. Objectives of the project • Automatic estimation of FPPA during the performance of dynamic tasks (ideally any 2D biomechanics angle) • Single-camera solution. • No external light sources. • Inexpensive and easy-to-use system. • Real-time visualization of the FPPA. • Automatic analysis of the data.
  6. 6. Overview of the proposed system
  7. 7. Why do we use markers? Although Kinect is well-known for being a markerless system, we introduce the tracking of three retro-reflective markers. This method increases the accuracy of the pose estimation algorithm of Kinect and allows for tracking points that are not necessarily joints. The blue line shows the data registered during a single leg landing using markers. The red line shows the data registered using the Kinect pose estimation algorithm. RMSE = 8.498º
  8. 8. Reflective markers tracking • Kinect’s depth sensor captures the infrared intensity value for each pixel of the image (512 x 424 resolution). • An empirical intensity threshold (high-pass filter) is used to select candidate marker’s pixels. • Kinect’s pose estimation algorithm is used to classify each marker position. • Retro-reflective elements not belonging to a marker are ignored. • Markers coordinates are
  9. 9. Application
  10. 10. Application
  11. 11. Experimental results  High concordance among the measurements  Proposed approach saves up to 10 minutes per assessed subject Comparison between Kinovea (2D offline analysis tool, expert oriented) and the proposed system. FPPA evaluated for 10 healthy subjects from a professional football team
  12. 12. Conclusions • Proposed a novel system to perform an automatic estimation of dynamic FPPA, by a single-camera, cost- effective and portable solution. • The system uses a depth sensor to track the position of three retro-reflective markers attached to the subject’s hip, knee and ankle joints. • Designed a user interface which simplifies the expert’s routine and expedites the analysis of the results. • Experimental results show the interrater reliability of the proposed system, as well as the limitations of the 2D analysis (limited joint rotation measurement).
  13. 13. THANKS!
  14. 14. Application description
  15. 15. Application implementation
  16. 16. Data storage Local database engine Why? • On-disk database file. • Not very large dataset. • No concurrent writers. • Data easily exported to CSV files for external analysis. Data is stored in two tables, differentiating patient personal information and data collected. Both tables are related by a personal ID.