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Sensor analysis and initial assessment of detectable first hoof contacts and last break-overs as unique signal fluctuations for equine gait analysis

The objective of the control study was to assess 2 prominent fluctuations in a single optical signal as being either a true first hoof contact or a last break-over based on descriptive measures. The study builds on initial findings from a preliminary investigation of the embedded-optical-base system...

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Detalles Bibliográficos
Autores principales: Atkins, Colton A, Pond, Kevin R, Madsen, Christi K, Moorman, Valerie J, Roman-Muniz, Ivette N, Archibeque, Shawn L, Grandin, Temple
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7200566/
https://www.ncbi.nlm.nih.gov/pubmed/32704902
http://dx.doi.org/10.1093/tas/txz089
Descripción
Sumario:The objective of the control study was to assess 2 prominent fluctuations in a single optical signal as being either a true first hoof contact or a last break-over based on descriptive measures. The study builds on initial findings from a preliminary investigation of the embedded-optical-base system’s (EOBS) capabilities in signal capturing and feasibility as potential alternative to existing gait technologies, such as piezoelectric (e.g., load cell) systems. Hoof contacts and break-overs were measured (0 to 1 au; arbitrary units) using a 2.4-m (length) × 0.9-m (width) platform containing 1 EOBS. Three mixed-breed horses (n = 3) were injected with saline or either 100 IU or 200 IU Botox (i.e., onabotulinumtoxinA) with a 2.5-mL final volume. Injections were made into the deep digital flexor muscle at the motor end plates, with electromyography and ultrasound guidance. Horses were observed for 3 time points (pre-, post-, and recovery test days) over the span of a 4-mo period. Signal fluctuations [i.e., amplitude of hoof impacts based on true first hoof contacts (ΔS(TS)) and true last break-overs (ΔS(TL))] and kinematics [i.e., complete gait pass (CGP) time duration (T)] were recorded from each horse. Visual observations and video analysis were used for determining gait pattern categories. Individual horse measurements were analyzed for each trial, compared with video data and classified. Comparison of primary signal fluctuations (i.e., ΔS(TS) vs. ΔS(TL); forelimb vs. hindlimb) exhibited significant differences between hoof contacts and break-overs (P < 0.05). Right and left forelimb hoof contacts and hindlimb break-overs were not significantly different (P = 0.966; 0.063 ± 0.135; Estimate ± SE; P = 0.606; 0.176 ± 0.142; Estimate ± SE, respectively). Additionally, treatment vs. saline forelimbs did not exhibit significant difference (P = 0.7407; −0.098 ± 0.279; Estimate ± SE). Overall, data showed that the EOBS can collect repeatable and unique primary signal fluctuations as prominent and different gait measurements providing evidence to further development and research of the sensing system.