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

Method comparison using the threshold-based kinematic footfall event detection methods (Sections A and B) compared to kinetic footfall events.Boxplots show accuracy (A) and within-horse precision (B) across the eight horse-based mean values (dots). Dashed line (- - -): zero difference between kinematic and kinetic method. Dotted line (…  …): a single frame of the kinematic datastream, equivalent to 10 ms at the 100 Hz sampling rate. Condition codes: Sw, straight walk (black); St, straight trot (green); CIw, walk on the circle with the foot on the inside (blue); COw, walk on the circle with the foot on the outside (red); CIt, trot on the circle with the foot on the inside (cyan); COt, trot on the circle with the foot on the outside (magenta).
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fig-4: Method comparison using the threshold-based kinematic footfall event detection methods (Sections A and B) compared to kinetic footfall events.Boxplots show accuracy (A) and within-horse precision (B) across the eight horse-based mean values (dots). Dashed line (- - -): zero difference between kinematic and kinetic method. Dotted line (…  …): a single frame of the kinematic datastream, equivalent to 10 ms at the 100 Hz sampling rate. Condition codes: Sw, straight walk (black); St, straight trot (green); CIw, walk on the circle with the foot on the inside (blue); COw, walk on the circle with the foot on the outside (red); CIt, trot on the circle with the foot on the inside (cyan); COt, trot on the circle with the foot on the outside (magenta).

Mentions: Limits of agreement ranged from −26 to 17 ms (Table 2) depending on limb, gait and direction. Accordingly, accuracy showed a bias of −11 to 0 ms depending on limb, gait and direction (Table 3). Precision across and within horses ranged from 2 to 8 ms (Table 3). Horse-based means for accuracy and within-horse precision are shown in Fig. 4.


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

Starke SD, Clayton HM - PeerJ (2015)

Method comparison using the threshold-based kinematic footfall event detection methods (Sections A and B) compared to kinetic footfall events.Boxplots show accuracy (A) and within-horse precision (B) across the eight horse-based mean values (dots). Dashed line (- - -): zero difference between kinematic and kinetic method. Dotted line (…  …): a single frame of the kinematic datastream, equivalent to 10 ms at the 100 Hz sampling rate. Condition codes: Sw, straight walk (black); St, straight trot (green); CIw, walk on the circle with the foot on the inside (blue); COw, walk on the circle with the foot on the outside (red); CIt, trot on the circle with the foot on the inside (cyan); COt, trot on the circle with the foot on the outside (magenta).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-4: Method comparison using the threshold-based kinematic footfall event detection methods (Sections A and B) compared to kinetic footfall events.Boxplots show accuracy (A) and within-horse precision (B) across the eight horse-based mean values (dots). Dashed line (- - -): zero difference between kinematic and kinetic method. Dotted line (…  …): a single frame of the kinematic datastream, equivalent to 10 ms at the 100 Hz sampling rate. Condition codes: Sw, straight walk (black); St, straight trot (green); CIw, walk on the circle with the foot on the inside (blue); COw, walk on the circle with the foot on the outside (red); CIt, trot on the circle with the foot on the inside (cyan); COt, trot on the circle with the foot on the outside (magenta).
Mentions: Limits of agreement ranged from −26 to 17 ms (Table 2) depending on limb, gait and direction. Accordingly, accuracy showed a bias of −11 to 0 ms depending on limb, gait and direction (Table 3). Precision across and within horses ranged from 2 to 8 ms (Table 3). Horse-based means for accuracy and within-horse precision are shown in Fig. 4.

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