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Computer-assisted 3D kinematic analysis of all leg joints in walking insects.

Bender JA, Simpson EM, Ritzmann RE - PLoS ONE (2010)

Bottom Line: We improved the legs' visibility by painting white dots on the joints, similar to techniques used for digitizing human motion.Our experimental design reduced the complexity of the tracking problem by tethering the insect and allowing it to walk in place on a lightly oiled glass surface, but in principle, the algorithms implemented are extensible to free walking.We encourage collaborative enhancements to make this tool both better and widely utilized.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America. jbender@case.edu

ABSTRACT
High-speed video can provide fine-scaled analysis of animal behavior. However, extracting behavioral data from video sequences is a time-consuming, tedious, subjective task. These issues are exacerbated where accurate behavioral descriptions require analysis of multiple points in three dimensions. We describe a new computer program written to assist a user in simultaneously extracting three-dimensional kinematics of multiple points on each of an insect's six legs. Digital video of a walking cockroach was collected in grayscale at 500 fps from two synchronized, calibrated cameras. We improved the legs' visibility by painting white dots on the joints, similar to techniques used for digitizing human motion. Compared to manual digitization of 26 points on the legs over a single, 8-second bout of walking (or 106,496 individual 3D points), our software achieved approximately 90% of the accuracy with 10% of the labor. Our experimental design reduced the complexity of the tracking problem by tethering the insect and allowing it to walk in place on a lightly oiled glass surface, but in principle, the algorithms implemented are extensible to free walking. Our software is free and open-source, written in the free language Python and including a graphical user interface for configuration and control. We encourage collaborative enhancements to make this tool both better and widely utilized.

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Related in: MedlinePlus

Our user-assisted digitization process yields results with accuracy comparable to manual digitization.(A) Each colored bar indicates the mean and standard deviation of the 3D Euclidean distance from the manually digitized point to the same point extracted by our software, for a single walking bout (4096 frames). The colored dots on the scale drawings of the legs have a radius corresponding to approximately the average positional error. (B) Each bar shows the mean and standard deviation of the absolute joint angle error. Joint angle errors were distributed log-normally.
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pone-0013617-g004: Our user-assisted digitization process yields results with accuracy comparable to manual digitization.(A) Each colored bar indicates the mean and standard deviation of the 3D Euclidean distance from the manually digitized point to the same point extracted by our software, for a single walking bout (4096 frames). The colored dots on the scale drawings of the legs have a radius corresponding to approximately the average positional error. (B) Each bar shows the mean and standard deviation of the absolute joint angle error. Joint angle errors were distributed log-normally.

Mentions: No additional assumptions were made about the behavioral patterns of leg movement. Some such constraints could be potentially valuable as tools for more effective tracking (e.g., the relative positions of the joints during stride versus during stance, the joints' rotation axes, the general forward-back temporal pattern of motion, or the correlations between legs). However, we chose not to make use of these sources of information to avoid fitting our data to our assumptions, some of which remain to be tested explicitly. We quantified the reliability of our tracking method by manually digitizing one 8 s trial, or 106,496 three-dimensional positions. Our software's averaging tracking error relative to the human “gold standard” was generally less than about 1 mm, except for the TiTa points of the front legs, which were slightly worse (Fig. 4). These translated into joint angle errors of 2–4°, except for somewhat larger errors for the FTi joints of the front legs, caused by the TiTa tracking errors. Overall, this seems to be a reasonable compromise for digitizing each movie in hours instead of days, and is of the same order as previous approaches [17].


Computer-assisted 3D kinematic analysis of all leg joints in walking insects.

Bender JA, Simpson EM, Ritzmann RE - PLoS ONE (2010)

Our user-assisted digitization process yields results with accuracy comparable to manual digitization.(A) Each colored bar indicates the mean and standard deviation of the 3D Euclidean distance from the manually digitized point to the same point extracted by our software, for a single walking bout (4096 frames). The colored dots on the scale drawings of the legs have a radius corresponding to approximately the average positional error. (B) Each bar shows the mean and standard deviation of the absolute joint angle error. Joint angle errors were distributed log-normally.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2964314&req=5

pone-0013617-g004: Our user-assisted digitization process yields results with accuracy comparable to manual digitization.(A) Each colored bar indicates the mean and standard deviation of the 3D Euclidean distance from the manually digitized point to the same point extracted by our software, for a single walking bout (4096 frames). The colored dots on the scale drawings of the legs have a radius corresponding to approximately the average positional error. (B) Each bar shows the mean and standard deviation of the absolute joint angle error. Joint angle errors were distributed log-normally.
Mentions: No additional assumptions were made about the behavioral patterns of leg movement. Some such constraints could be potentially valuable as tools for more effective tracking (e.g., the relative positions of the joints during stride versus during stance, the joints' rotation axes, the general forward-back temporal pattern of motion, or the correlations between legs). However, we chose not to make use of these sources of information to avoid fitting our data to our assumptions, some of which remain to be tested explicitly. We quantified the reliability of our tracking method by manually digitizing one 8 s trial, or 106,496 three-dimensional positions. Our software's averaging tracking error relative to the human “gold standard” was generally less than about 1 mm, except for the TiTa points of the front legs, which were slightly worse (Fig. 4). These translated into joint angle errors of 2–4°, except for somewhat larger errors for the FTi joints of the front legs, caused by the TiTa tracking errors. Overall, this seems to be a reasonable compromise for digitizing each movie in hours instead of days, and is of the same order as previous approaches [17].

Bottom Line: We improved the legs' visibility by painting white dots on the joints, similar to techniques used for digitizing human motion.Our experimental design reduced the complexity of the tracking problem by tethering the insect and allowing it to walk in place on a lightly oiled glass surface, but in principle, the algorithms implemented are extensible to free walking.We encourage collaborative enhancements to make this tool both better and widely utilized.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Case Western Reserve University, Cleveland, Ohio, United States of America. jbender@case.edu

ABSTRACT
High-speed video can provide fine-scaled analysis of animal behavior. However, extracting behavioral data from video sequences is a time-consuming, tedious, subjective task. These issues are exacerbated where accurate behavioral descriptions require analysis of multiple points in three dimensions. We describe a new computer program written to assist a user in simultaneously extracting three-dimensional kinematics of multiple points on each of an insect's six legs. Digital video of a walking cockroach was collected in grayscale at 500 fps from two synchronized, calibrated cameras. We improved the legs' visibility by painting white dots on the joints, similar to techniques used for digitizing human motion. Compared to manual digitization of 26 points on the legs over a single, 8-second bout of walking (or 106,496 individual 3D points), our software achieved approximately 90% of the accuracy with 10% of the labor. Our experimental design reduced the complexity of the tracking problem by tethering the insect and allowing it to walk in place on a lightly oiled glass surface, but in principle, the algorithms implemented are extensible to free walking. Our software is free and open-source, written in the free language Python and including a graphical user interface for configuration and control. We encourage collaborative enhancements to make this tool both better and widely utilized.

Show MeSH
Related in: MedlinePlus