15 results on '"Roepstorff, C"'
Search Results
2. Author Correction: Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
- Author
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Serra Bragança, F. M., Broomé, S., Rhodin, M., Björnsdóttir, S., Gunnarsson, V., Voskamp, J. P., Persson‑Sjodin, E., Back, W., Lindgren, G., Novoa‑Bravo, M., Gmel, A. I., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., and Hernlund, E.
- Published
- 2021
- Full Text
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3. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
- Author
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Serra Bragança, F. M., Broomé, S., Rhodin, M., Björnsdóttir, S., Gunnarsson, V., Voskamp, J. P., Persson-Sjodin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Gmel, A. I., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., and Hernlund, E.
- Published
- 2020
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4. Quantitative lameness assessment in the horse based on upper body movement symmetry: The effect of different filtering techniques on the quantification of motion symmetry
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Bragança, F.M. Serra, Roepstorff, C., Rhodin, Marie, Pfau, T., Weeren, P.R. van, Roepstorff, Lars, LS Equine Muscoskeletal Biology, dES RMSC, Geneeskunde van gezelschapsdieren, Dep Gezondheidszorg Paard, LS Equine Muscoskeletal Biology, dES RMSC, Geneeskunde van gezelschapsdieren, and Dep Gezondheidszorg Paard
- Subjects
Signal processing ,filter ,lameness ,Computer science ,business.industry ,0206 medical engineering ,Butterworth filter ,Health Informatics ,Pattern recognition ,02 engineering and technology ,Filter (signal processing) ,Horse ,020601 biomedical engineering ,Signal ,Chebyshev filter ,03 medical and health sciences ,0302 clinical medicine ,Gait (human) ,Moving average ,Artificial intelligence ,business ,signal processing ,Infinite impulse response ,030217 neurology & neurosurgery ,asymmetry - Abstract
Quantitative gait analysis in horses is rapidly gaining importance, both clinically and in research. The number of available systems is increasing, but the methods of signal analysis differ between systems and research groups. Our objectives are to describe and evaluate the effects of different methods of signal analysis for processing of data from equine kinematic gait analysis. To this end, we use theoretical signals based on previously published work, followed by the evaluation of the performance of each technique using real data from horses with induced lameness. Two infinite impulse response (IIR), high-pass filters (Butterworth and Chebyshev), a signal decomposition method and a moving average filtering technique were evaluated. First, we describe methods to fine-tune each filter to the optimal settings based on residual analysis. Second the performance of each filter is evaluated based on differences in calculated symmetry parameters from horses with induced lameness. We show that optimisation of filtering techniques is crucial when processing signals used for objective lameness quantification. Improper selection of the cut-off frequency for IIR filters can result in false negative results (average values above or below predefined reference values). The IIR Butterworth filter and the signal decomposition method achieved the best reduction of unwanted signal components. Knowledge of the available filtering techniques is a pre-requisite for adequate signal processing of gait data from quantitative analysis systems in horses.
- Published
- 2020
5. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
- Author
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Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, Hernlund, E, Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, and Hernlund, E
- Abstract
For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.
- Published
- 2020
6. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
- Author
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Braganca, F. M. Serra, Broomé, Michael, Rhodin, M., Bjornsdottir, S., Gunnarsson, V, Voskamp, J. P., Persson-Sjödin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., Hernlund, E., Braganca, F. M. Serra, Broomé, Michael, Rhodin, M., Bjornsdottir, S., Gunnarsson, V, Voskamp, J. P., Persson-Sjödin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Roepstorff, C., van der Zwaag, B. J., Van Weeren, P. R., and Hernlund, E.
- Abstract
For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms., QC 20201127Correction: DOI:10.1038/s41598-021-88880-7
- Published
- 2020
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7. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning
- Author
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Equine Musculoskeletal Biology, dES RMSC, Hafd Onderwijsadvies en training, dES AVR, Dep Clinical Sciences, Afd Algemeen Paard, Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, Hernlund, E, Equine Musculoskeletal Biology, dES RMSC, Hafd Onderwijsadvies en training, dES AVR, Dep Clinical Sciences, Afd Algemeen Paard, Serra Bragança, F M, Broomé, S, Rhodin, Marie, Björnsdóttir, S, Gunnarsson, V, Voskamp, J P, Persson-Sjodin, E, Back, W, Lindgren, G, Novoa-Bravo, M, Roepstorff, C, van der Zwaag, B J, Van Weeren, P R, and Hernlund, E
- Published
- 2020
8. Quantitative lameness assessment in the horse based on upper body movement symmetry: The effect of different filtering techniques on the quantification of motion symmetry
- Author
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LS Equine Muscoskeletal Biology, dES RMSC, Geneeskunde van gezelschapsdieren, Dep Gezondheidszorg Paard, Bragança, F.M. Serra, Roepstorff, C., Rhodin, Marie, Pfau, T., Weeren, P.R. van, Roepstorff, Lars, LS Equine Muscoskeletal Biology, dES RMSC, Geneeskunde van gezelschapsdieren, Dep Gezondheidszorg Paard, Bragança, F.M. Serra, Roepstorff, C., Rhodin, Marie, Pfau, T., Weeren, P.R. van, and Roepstorff, Lars
- Published
- 2020
9. Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture.
- Author
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Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M, Andersen PH, Kjellström H, and Hernlund E
- Abstract
Computer vision is a subcategory of artificial intelligence focused on extraction of information from images and video. It provides a compelling new means for objective orthopaedic gait assessment in horses using accessible hardware, such as a smartphone, for markerless motion analysis. This study aimed to explore the lameness assessment capacity of a smartphone single camera (SC) markerless computer vision application by comparing measurements of the vertical motion of the head and pelvis to an optical motion capture multi-camera (MC) system using skin attached reflective markers. Twenty-five horses were recorded with a smartphone (60 Hz) and a 13 camera MC-system (200 Hz) while trotting two times back and forth on a 30 m runway. The smartphone video was processed using artificial neural networks detecting the horse's direction, action and motion of body segments. After filtering, the vertical displacement curves from the head and pelvis were synchronised between systems using cross-correlation. This rendered 655 and 404 matching stride segmented curves for the head and pelvis respectively. From the stride segmented vertical displacement signals, differences between the two minima (MinDiff) and the two maxima (MaxDiff) respectively per stride were compared between the systems. Trial mean difference between systems was 2.2 mm (range 0.0-8.7 mm) for head and 2.2 mm (range 0.0-6.5 mm) for pelvis. Within-trial standard deviations ranged between 3.1-28.1 mm for MC and between 3.6-26.2 mm for SC. The ease of use and good agreement with MC indicate that the SC application is a promising tool for detecting clinically relevant levels of asymmetry in horses, enabling frequent and convenient gait monitoring over time.
- Published
- 2023
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10. Modelling fore- and hindlimb peak vertical force differences in trotting horses using upper body kinematic asymmetry variables.
- Author
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Roepstorff C, Imogen Gmel A, Arpagaus S, Manuel Serra Bragança F, Hernlund E, Roepstorff L, Rhodin M, and Andreas Weishaupt M
- Subjects
- Animals, Biomechanical Phenomena, Forelimb physiology, Gait physiology, Hindlimb physiology, Horses, Retrospective Studies, Horse Diseases, Lameness, Animal
- Abstract
Differences in peak vertical ground reaction forces (dFz
peak ) between contralateral forelimbs and hindlimbs are considered the gold standard for quantifying weight-bearing lameness. However, measuring kinematics for the same purpose is more common and practical. Vertical movement asymmetries (VMA) of the horse's upper body have previously been correlated to fore- and hindlimb lameness. But the combined response of head, withers and pelvis VMA to fore- and hindlimb dFzpeak has not yet been thoroughly investigated. Deriving the kinetic responses from kinematics would help the interpretation and understanding of quantified weight-bearing lameness. In this retrospective study, 103 horses with a wide range of fore- and hindlimb dFzpeak had been trotted on a force-measuring treadmill synchronized with an optical motion capture system. VMA of the head, withers and pelvis as well as dFzpeak were extracted. Multiple linear mixed models and linear regressions of kinematic variables were used to model the dFzpeak . It was hypothesised that all included VMA would have a significant influence on the dFzpeak outcome variables. The results showed a complex relationship between VMA and dFzpeak where both amplitude and timing of the VMA were of importance. On average, the contribution percentage of VMA to fore/hind dFzpeak were 66/34% for head, 76/24% for withers and 33/67% for pelvis. The linear regressions for the fore/hindlimb models achieved mean measurement root mean squared errors of 0.83%/0.82% dFzpeak . These results might help determine the clinical relevance of upper body VMA and distinguish between primary fore, hind, ipsilateral and diagonal weight-bearing lameness., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)- Published
- 2022
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11. Roll And Pitch of the Rider's Pelvis During Horseback Riding at Walk on a Circle.
- Author
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Egenvall A, Clayton H, Engell MT, Roepstorff C, Engström H, and Byström A
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- Animals, Biomechanical Phenomena, Horses, Humans, Physical Conditioning, Animal, Walking, Gait, Pelvis
- Abstract
The study investigated between-rider differences in pelvic roll and pitch motion during horseback riding as the horse walked around circles without rein contact (walk on long reins), with rein contact, and with moderate collection. Ten horses were ridden by five riders on left and right 10 m circles, in a partly crossed design, yielding 14 trials. Each trial included each of the three walk variations in both directions. Riders wore an inertial measurement unit (IMU), logging at 100 Hz, dorsally on the pelvis. Pelvic roll and pitch data were split into strides based on data from IMU-sensors on the horse's hind cannons. Data were analyzed using signal decomposition into the fundamental frequency (the stride frequency) and its first two harmonics. Mixed models accounting for the type of walk were used to analyze how riders differed in roll and pitch pelvic motion in two ways: comparing amplitudes of the frequency components and comparing whole stride mean data. Graphically pelvic pitch showed substantial timing and amplitude differences between riders, and this was confirmed statistically. Pelvic roll timing was similar, but amplitude varied between the riders, both graphically and statistically. Individual rider patterns tended to persist across different horses and all exercises. These results suggest that exercises at walk can be ridden with different pelvis pitch timing, a fact that has so far not been discussed in the equestrian literature. Whether pelvic pitch timing affects the horse's performance remains to be investigated., (Copyright © 2021. Published by Elsevier Inc.)
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- 2022
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12. Reliable and clinically applicable gait event classification using upper body motion in walking and trotting horses.
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Roepstorff C, Dittmann MT, Arpagaus S, Serra Bragança FM, Hardeman A, Persson-Sjödin E, Roepstorff L, Gmel AI, and Weishaupt MA
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- Animals, Biomechanical Phenomena, Forelimb, Gait, Horses, Motion, Hoof and Claw, Walking
- Abstract
Objectively assessing horse movement symmetry as an adjunctive to the routine lameness evaluation is on the rise with several commercially available systems on the market. Prerequisites for quantifying such symmetries include knowledge of the gait and gait events, such as hoof to ground contact patterns over consecutive strides. Extracting this information in a robust and reliable way is essential to accurately calculate many kinematic variables commonly used in the field. In this study, optical motion capture was used to measure 222 horses of various breeds, performing a total of 82 664 steps in walk and trot under different conditions, including soft, hard and treadmill surfaces as well as moving on a straight line and in circles. Features were extracted from the pelvis and withers vertical movement and from pelvic rotations. The features were then used in a quadratic discriminant analysis to classify gait and to detect if the left/right hind limb was in contact with the ground on a step by step basis. The predictive model achieved 99.98% accuracy on the test data of 120 horses and 21 845 steps, all measured under clinical conditions. One of the benefits of the proposed method is that it does not require the use of limb kinematics making it especially suited for clinical applications where ease of use and minimal error intervention are a priority. Future research could investigate the extension of this functionality to classify other gaits and validating the use of the algorithm for inertial measurement units., Competing Interests: Declaration of Competing Interest The salary of Christoffer Roepstorff was partially funded by Qualisys AB. However, Qualisys AB had no influence on the outcome of this study. No other authors declare any conflicts of interest., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2021
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13. Riding Soundness-Comparison of Subjective With Objective Lameness Assessments of Owner-Sound Horses at Trot on a Treadmill.
- Author
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Müller-Quirin J, Dittmann MT, Roepstorff C, Arpagaus S, Latif SN, and Weishaupt MA
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- Animals, Biomechanical Phenomena, Forelimb, Gait, Hindlimb, Horses, Horse Diseases diagnosis, Lameness, Animal diagnosis
- Abstract
Lameness is a symptom indicative of pain or injury of the locomotor apparatus. Lame horses generally should not be ridden. However, owners' ability to assess lameness has been questioned. This study's aim was to use subjective lameness assessments and objective gait analysis to generate a descriptive overview of movement and weight-bearing asymmetries of owner-sound riding horses. 235 horses were subjectively assessed in a field study, and the owner's perception of their horse's orthopedic health was recorded through an online survey. 69 horses were re-evaluated by gait analysis at an equine hospital. During trot on an instrumented treadmill, the gait was scored by a veterinarian using lameness grades from 0/5 (sound) to 3/5 (moderate lameness visible at trot). Movement asymmetry of the head (HDmin) and pelvis (PDmin) and weight-bearing asymmetry were quantified simultaneously. The prevalence of subjectively scored lameness grade ≥2/5 in one or more limbs was 55% during study part 1 and 74% during study part 2. Movement asymmetry of the head and/or pelvis exceeding HDmin ≥12 mm and/or PDmin ≥6 mm was found in 57% of the horses. 58% showed weight-bearing asymmetries between contralateral front and/or hind limbs of ≥3% body mass. Gait analysis showed considerable variability of movement and weight-bearing asymmetry values, sometimes independent of the clinical lameness grade, especially in the forehand. Several horses with lameness grade ≤1/5 had asymmetry values greater than mentioned thresholds. The analysis of movement and weight-bearing asymmetry revealed that these objective variables did not necessarily act uniformly and therefore should be interpreted with caution., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)
- Published
- 2020
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14. Influence of Functional Rider and Horse Asymmetries on Saddle Force Distribution During Stance and in Sitting Trot.
- Author
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Gunst S, Dittmann MT, Arpagaus S, Roepstorff C, Latif SN, Klaassen B, Pauli CA, Bauer CM, and Weishaupt MA
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- Animals, Biomechanical Phenomena, Horses, Movement, Posture, Back, Sitting Position
- Abstract
Asymmetric forces exerted on the horse's back during riding are assumed to have a negative effect on rider-horse interaction, athletic performance, and health of the horse. Visualized on a saddle pressure mat, they are initially blamed on a nonfitting saddle. The contribution of horse and rider to an asymmetric loading pattern, however, is not well understood. The aim of this study was to investigate the effects of horse and rider asymmetries during stance and in sitting trot on the force distribution on the horse's back using a saddle pressure mat and motion capture analysis simultaneously. Data of 80 horse-rider pairs (HRP) were collected and analyzed using linear (mixed) models to determine the influence of rider and horse variables on asymmetric force distribution. Results showed high variation between HRP. Both rider and horse variables revealed significant relationships to asymmetric saddle force distribution (P < .001). During sitting trot, the collapse of the rider in one hip increased the force on the contralateral side, and the tilt of the rider's upper body to one side led to more force on the same side of the pressure mat. Analyzing different subsets of data revealed that rider posture as well as horse movements and conformation can cause an asymmetric force distribution. Because neither horse nor rider movement can be assessed independently during riding, the interpretation of an asymmetric force distribution on the saddle pressure mat remains challenging, and all contributing factors (horse, rider, saddle) need to be considered., (Copyright © 2019 Elsevier Inc. All rights reserved.)
- Published
- 2019
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15. Steady state is reached within 2-3 days of once-daily administration of degludec, a basal insulin with an ultralong duration of action.
- Author
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Heise T, Korsatko S, Nosek L, Coester HV, Deller S, Roepstorff C, Segel S, Kapur R, Haahr H, and Hompesch M
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- Adolescent, Adult, Black or African American, Aged, Area Under Curve, Blood Glucose drug effects, Diabetes Mellitus, Type 1 drug therapy, Diabetes Mellitus, Type 1 ethnology, Diabetes Mellitus, Type 2 drug therapy, Diabetes Mellitus, Type 2 ethnology, Double-Blind Method, Drug Administration Schedule, Female, Hispanic or Latino, Humans, Hypoglycemic Agents administration & dosage, Injections, Subcutaneous, Insulin, Long-Acting administration & dosage, Male, Middle Aged, Time Factors, Young Adult, Diabetes Mellitus, Type 1 metabolism, Diabetes Mellitus, Type 2 metabolism, Hypoglycemic Agents pharmacokinetics, Insulin, Long-Acting pharmacokinetics
- Abstract
Background: Various factors influence the pharmacokinetic and pharmacodynamic properties of insulin analogs. The aim of the present study was to determine time to steady state of insulin degludec (IDeg), a basal insulin analog with an ultralong duration of action, after once-daily subcutaneous administration in subjects of varying age, diabetes type, and ethnicity., Methods: Time to steady state was analyzed in 195 subjects across five Phase I randomized single-center double-blind studies: three in subjects with type 1 diabetes (T1DM), including one in elderly subjects, and two in subjects with type 2 diabetes (T2DM), including one with African American and Hispanic/Latino subpopulations. Subjects received once-daily IDeg (100 U/mL, s.c.) at doses of 0.4-0.8 U/kg for 6-12 days. Time to clinical steady state was measured from first dose until the serum IDeg trough concentration exceeded 90% of the final plateau level. The IDeg concentrations were log-transformed and analyzed using a mixed-effects model with time from first dose and dose level (where applicable) as fixed effects, and subject as a random effect., Results: Steady state serum IDeg concentrations were reached after 2-3 days in all subjects. In trials with multiple dose levels, time to steady state was independent of dose level in T1DM (P = 0.51) and T2DM (P = 0.75)., Conclusions: Serum IDeg concentrations reached steady state within 2-3 days of once-daily subcutaneous administration in all subjects with T1DM or T2DM, including elderly and African American and Hispanic/Latino subjects. At steady state, serum IDeg concentrations were unchanged from day to day., (© 2015 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd.)
- Published
- 2016
- Full Text
- View/download PDF
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