Limits...
A universal approach to determine footfall timings from kinematics of a single foot marker in hoofed animals.

Starke SD, Clayton HM - PeerJ (2015)

Bottom Line: While we found that use of velocity thresholds for foot on detection results in biased event estimates for the foot on the inside of the circle at trot, adjusting thresholds for this condition negated the effect.For the final four algorithms, we found no noteworthy bias between conditions or between front- and hind-foot timings.Different force thresholds in the range of 50 to 150 N had the greatest systematic effect on foot-off estimates in the hind limbs (up to on average 16 ms per condition), being greater than the effect on foot-on estimates or foot-off estimates in the forelimbs (up to on average ±7 ms per condition).

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electronic, Electrical and Systems Engineering, University of Birmingham , Edgbaston, Birmingham, West Midlands , UK.

ABSTRACT
The study of animal movement commonly requires the segmentation of continuous data streams into individual strides. The use of forceplates and foot-mounted accelerometers readily allows the detection of the foot-on and foot-off events that define a stride. However, when relying on optical methods such as motion capture, there is lack of validated robust, universally applicable stride event detection methods. To date, no method has been validated for movement on a circle, while algorithms are commonly specific to front/hind limbs or gait. In this study, we aimed to develop and validate kinematic stride segmentation methods applicable to movement on straight line and circle at walk and trot, which exclusively rely on a single, dorsal hoof marker. The advantage of such marker placement is the robustness to marker loss and occlusion. Eight horses walked and trotted on a straight line and in a circle over an array of multiple forceplates. Kinetic events were detected based on the vertical force profile and used as the reference values. Kinematic events were detected based on displacement, velocity or acceleration signals of the dorsal hoof marker depending on the algorithm using (i) defined thresholds associated with derived movement signals and (ii) specific events in the derived movement signals. Method comparison was performed by calculating limits of agreement, accuracy, between-horse precision and within-horse precision based on differences between kinetic and kinematic event. In addition, we examined the effect of force thresholds ranging from 50 to 150 N on the timings of kinetic events. The two approaches resulted in very good and comparable performance: of the 3,074 processed footfall events, 95% of individual foot on and foot off events differed by no more than 26 ms from the kinetic event, with average accuracy between -11 and 10 ms and average within- and between horse precision ≤8 ms. While the event-based method may be less likely to suffer from scaling effects, on soft ground the threshold-based method may prove more valuable. While we found that use of velocity thresholds for foot on detection results in biased event estimates for the foot on the inside of the circle at trot, adjusting thresholds for this condition negated the effect. For the final four algorithms, we found no noteworthy bias between conditions or between front- and hind-foot timings. Different force thresholds in the range of 50 to 150 N had the greatest systematic effect on foot-off estimates in the hind limbs (up to on average 16 ms per condition), being greater than the effect on foot-on estimates or foot-off estimates in the forelimbs (up to on average ±7 ms per condition).

No MeSH data available.


Related in: MedlinePlus

Event-based footfall detection method.(A) Principle for foot on (left, red) and foot off (right, cyan) detection based on distinct events in the acceleration signal for foot on and the vertical velocity signal for foot off. For foot on, generally vertical acceleration was used. Only for the hind limbs during trot on the circle did we use resultant acceleration, as for individual horses there was no distinct event in vertical acceleration. (B) Kinematic (dashed lines) and kinetic (solid lines) event detection for foot on (red) and foot off (cyan) illustrated for a single stride of a forelimb during trot on a circle, the same stride as shown in Fig. 1. Top row: vertical displacement (left) and velocity (right); middle row: vertical acceleration (left) and resultant acceleration (right); bottom row: vertical ground reaction force shown at two magnifications. For further details, refer to Fig. 1.
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fig-2: Event-based footfall detection method.(A) Principle for foot on (left, red) and foot off (right, cyan) detection based on distinct events in the acceleration signal for foot on and the vertical velocity signal for foot off. For foot on, generally vertical acceleration was used. Only for the hind limbs during trot on the circle did we use resultant acceleration, as for individual horses there was no distinct event in vertical acceleration. (B) Kinematic (dashed lines) and kinetic (solid lines) event detection for foot on (red) and foot off (cyan) illustrated for a single stride of a forelimb during trot on a circle, the same stride as shown in Fig. 1. Top row: vertical displacement (left) and velocity (right); middle row: vertical acceleration (left) and resultant acceleration (right); bottom row: vertical ground reaction force shown at two magnifications. For further details, refer to Fig. 1.

Mentions: This foot on detection method was examined to test whether differentiation of displacement data would allow detection of footfall events similar to hoof-mounted accelerometers (Witte, Knill & Wilson, 2004) despite the introduced noise and a different co-ordinate reference system. For the detection of ‘foot on’ events, displacement along each of the three coordinate system axes was double-differentiated to arrive at unfiltered acceleration components. We then tested a variety of low-pass and raw event detection approaches using both vertical acceleration and the resultant acceleration to determine the best settings for impact detection. These tests showed best accuracy and precision (both between and within horses) for events based on vertical acceleration (compare Fig. 2), except for the hind feet during trot on the circle. Here, impact accelerations were often not detectable; however, resultant acceleration proved reliable. The final algorithm we tested is hence a composite: for all conditions except the hind feet during trot on the circle, vertical acceleration was low-pass filtered (4th order, zero-lag Butterworth filter, cut-off frequency 25 Hz for trot and 20 Hz for walk). Foot on (Figs. 2A and 2C) was then identified as the maximum between the pre-segmentation point (see above) and a further 20 frames (0.2 s). For the hind feet during trot on the circle, the same procedure was performed, but in this case the time of foot on was based on the resultant acceleration which was low-pass filtered with a 4th order, zero-lag Butterworth filter with a cut-off frequency of 15 Hz (Figs. 2A and 2C).


A universal approach to determine footfall timings from kinematics of a single foot marker in hoofed animals.

Starke SD, Clayton HM - PeerJ (2015)

Event-based footfall detection method.(A) Principle for foot on (left, red) and foot off (right, cyan) detection based on distinct events in the acceleration signal for foot on and the vertical velocity signal for foot off. For foot on, generally vertical acceleration was used. Only for the hind limbs during trot on the circle did we use resultant acceleration, as for individual horses there was no distinct event in vertical acceleration. (B) Kinematic (dashed lines) and kinetic (solid lines) event detection for foot on (red) and foot off (cyan) illustrated for a single stride of a forelimb during trot on a circle, the same stride as shown in Fig. 1. Top row: vertical displacement (left) and velocity (right); middle row: vertical acceleration (left) and resultant acceleration (right); bottom row: vertical ground reaction force shown at two magnifications. For further details, refer to Fig. 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493675&req=5

fig-2: Event-based footfall detection method.(A) Principle for foot on (left, red) and foot off (right, cyan) detection based on distinct events in the acceleration signal for foot on and the vertical velocity signal for foot off. For foot on, generally vertical acceleration was used. Only for the hind limbs during trot on the circle did we use resultant acceleration, as for individual horses there was no distinct event in vertical acceleration. (B) Kinematic (dashed lines) and kinetic (solid lines) event detection for foot on (red) and foot off (cyan) illustrated for a single stride of a forelimb during trot on a circle, the same stride as shown in Fig. 1. Top row: vertical displacement (left) and velocity (right); middle row: vertical acceleration (left) and resultant acceleration (right); bottom row: vertical ground reaction force shown at two magnifications. For further details, refer to Fig. 1.
Mentions: This foot on detection method was examined to test whether differentiation of displacement data would allow detection of footfall events similar to hoof-mounted accelerometers (Witte, Knill & Wilson, 2004) despite the introduced noise and a different co-ordinate reference system. For the detection of ‘foot on’ events, displacement along each of the three coordinate system axes was double-differentiated to arrive at unfiltered acceleration components. We then tested a variety of low-pass and raw event detection approaches using both vertical acceleration and the resultant acceleration to determine the best settings for impact detection. These tests showed best accuracy and precision (both between and within horses) for events based on vertical acceleration (compare Fig. 2), except for the hind feet during trot on the circle. Here, impact accelerations were often not detectable; however, resultant acceleration proved reliable. The final algorithm we tested is hence a composite: for all conditions except the hind feet during trot on the circle, vertical acceleration was low-pass filtered (4th order, zero-lag Butterworth filter, cut-off frequency 25 Hz for trot and 20 Hz for walk). Foot on (Figs. 2A and 2C) was then identified as the maximum between the pre-segmentation point (see above) and a further 20 frames (0.2 s). For the hind feet during trot on the circle, the same procedure was performed, but in this case the time of foot on was based on the resultant acceleration which was low-pass filtered with a 4th order, zero-lag Butterworth filter with a cut-off frequency of 15 Hz (Figs. 2A and 2C).

Bottom Line: While we found that use of velocity thresholds for foot on detection results in biased event estimates for the foot on the inside of the circle at trot, adjusting thresholds for this condition negated the effect.For the final four algorithms, we found no noteworthy bias between conditions or between front- and hind-foot timings.Different force thresholds in the range of 50 to 150 N had the greatest systematic effect on foot-off estimates in the hind limbs (up to on average 16 ms per condition), being greater than the effect on foot-on estimates or foot-off estimates in the forelimbs (up to on average ±7 ms per condition).

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electronic, Electrical and Systems Engineering, University of Birmingham , Edgbaston, Birmingham, West Midlands , UK.

ABSTRACT
The study of animal movement commonly requires the segmentation of continuous data streams into individual strides. The use of forceplates and foot-mounted accelerometers readily allows the detection of the foot-on and foot-off events that define a stride. However, when relying on optical methods such as motion capture, there is lack of validated robust, universally applicable stride event detection methods. To date, no method has been validated for movement on a circle, while algorithms are commonly specific to front/hind limbs or gait. In this study, we aimed to develop and validate kinematic stride segmentation methods applicable to movement on straight line and circle at walk and trot, which exclusively rely on a single, dorsal hoof marker. The advantage of such marker placement is the robustness to marker loss and occlusion. Eight horses walked and trotted on a straight line and in a circle over an array of multiple forceplates. Kinetic events were detected based on the vertical force profile and used as the reference values. Kinematic events were detected based on displacement, velocity or acceleration signals of the dorsal hoof marker depending on the algorithm using (i) defined thresholds associated with derived movement signals and (ii) specific events in the derived movement signals. Method comparison was performed by calculating limits of agreement, accuracy, between-horse precision and within-horse precision based on differences between kinetic and kinematic event. In addition, we examined the effect of force thresholds ranging from 50 to 150 N on the timings of kinetic events. The two approaches resulted in very good and comparable performance: of the 3,074 processed footfall events, 95% of individual foot on and foot off events differed by no more than 26 ms from the kinetic event, with average accuracy between -11 and 10 ms and average within- and between horse precision ≤8 ms. While the event-based method may be less likely to suffer from scaling effects, on soft ground the threshold-based method may prove more valuable. While we found that use of velocity thresholds for foot on detection results in biased event estimates for the foot on the inside of the circle at trot, adjusting thresholds for this condition negated the effect. For the final four algorithms, we found no noteworthy bias between conditions or between front- and hind-foot timings. Different force thresholds in the range of 50 to 150 N had the greatest systematic effect on foot-off estimates in the hind limbs (up to on average 16 ms per condition), being greater than the effect on foot-on estimates or foot-off estimates in the forelimbs (up to on average ±7 ms per condition).

No MeSH data available.


Related in: MedlinePlus