Warner, S. M., Koch, T. O., & Pfau, T. (2010). Inertial sensors for assessment of back movement in horses during locomotion over ground. Equine Veterinary Journal, 42, 417–424.
Abstract: Reasons for performing study: Assessing back movement is an important part of clinical examination in the horse and objective assessment tools allow for evaluating success of treatment. Objectives: Accuracy and consistency of inertial sensor measurements for quantification of back movement and movement symmetry during over ground locomotion were assessed; sensor measurements were compared to optical motion capture (mocap) and consistency of measurements focusing on movement symmetry was measured. Methods: Six nonlame horses were trotted in hand with synchronised mocap and inertial sensor data collection (landmarks: T6, T10, T13, L1 and S3). Inertial sensor data were processed using published methods and symmetry of dorsoventral displacement was assessed based on energy ratio, a Fourier based symmetry measure. Limits of agreement were calculated and visualised to compare mocap and sensor data. Consistency of sensor measurements was assessed using Pearson correlation coefficients and linear regression to investigate the effect of speed on movement symmetry. Results: Dorsoventral and mediolateral sensor displacement was observed to lie within ± 4–5 mm (± 2 s.d., 9–28% of movement amplitude) and energy ratio to lie within ± 0.03 of mocap data. High levels of correlation were found between strides and trials (0.86–1.0) for each horse and each sensor and variability of symmetry was lowest for T13 followed by T10, T6, L1 and S3 with no significant effect of speed at T6, T10 and T13. Conclusions: Inertial sensor displacement and symmetry data showed acceptable accuracy and good levels of consistency for back movement. The small mediolateral movement amplitude means that changes of <25% in mediolateral amplitude (also unlikely to be detected by visual assessment) may go undetected. New sensor generations with improved sensor sensitivity and ease of use of equipment indicate good potential for use in a field situation.
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Groesel, M., Zsoldos, R. R., Kotschwar, A., Gfoehler, M., & Peham, C. (2010). A preliminary model study of the equine back including activity of longissimus dorsi muscle. Equine Veterinary Journal, 42, 401–406.
Abstract: Reasons for performing study: Identifying the underlying problem of equine back pain and diseases of the spine are significant problems in veterinary orthopaedics. A study to validate a preliminary biomechanical model of the equine back based on CT images including longissimus dorsi (LD) muscle is therefore important. Objectives: Validation of the back model by comparing the shortening of LD muscles in the model with integrated EMG (IEMG) at stance during induced lateral flexion of the spine. Methods:Longissimus dorsi muscle activity at stance has been used for validation. EMG electrodes were placed laterally at the level of T12, T16 and L3. Reflective markers have been attached on top of the spinous processes T5, T12, T16, L1 and the sacral bone (OS1, OS2) for motion tracking analysis. A virtual model of the equine's back (T1–S5) was built with inclusion of a simplified LD muscle by 2 separate contours left and right of the spine, starting at tuber coxae laterally and attaching to the spinous process T5 medially. Shortening of LD during induced lateral flexion caused by the kinematic data (input) was compared to the 3 EMG signals (T12, T16 and L3) on the active side via correlation. Results: Pearson correlation coefficient between IEMG and shortening length of LD in the model was (mean ± s.d.) 0.95 ± 0.07 for the left side and 0.91 ± 0.07 for the right side of LD. Conclusions: Activity of the LD muscles is mainly responsible for stabilisation of the vertebral column with isometric muscle contraction against dynamic forces in walk and trot. This validation requires muscle shortening in the back, like induced lateral flexion at stance. The length of the shortening muscle model and the IEMG show a linear relationship. These findings will help to model the LD for forward simulations, e.g. from force to motion.
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