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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
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.
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)
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
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.
Overview of the proposed system
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
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
Application
Application
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
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).
THANKS!
Application description
Application implementation
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.

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

  • 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.
  • 3. 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.
  • 4. 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)
  • 5. 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
  • 6. 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.
  • 7. Overview of the proposed system
  • 8. 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
  • 9. 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
  • 12. 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
  • 13. 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).
  • 17. 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.

Editor's Notes

  1. Kinect allows for the automatic labeling of each marker Depth camera is more robust to lighting conditions than RGB cameras, and does not need to shine on the markers with special light