Development of a system for automatic measurements of force and visual stance variables for objective lameness detection in sows: SowSIS

Pluym, Liesbet; Maes, Dominiek; Vangeyte, Jürgen; Mertens, Kristof; Baert, Jeroen; Weyenberg, Stéphanie Van; Millet, Sam; Nuffel, Annelies Van

doi:10.1016/j.biosystemseng.2013.06.009

Cited by 41 publications

(39 citation statements)

References 39 publications

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“…Evaluating changes to the locomotion biomechanical parameters provides an objective, sensitive, and precise means to detect animals in lame states (Pluym et al, 2013b). Lameness detection utilizing static and dynamic gait variables has been proven effective in assessing lameness in several species, including dairy cattle (Flower et al, 2005;Maertens et al, 2011), chickens (Corr et al, 2007), horses (Weishaupt et al, 2006), and swine (Gregoire et al, 2013;Pluym et al, 2013b;Conte et al, 2014;Meijer et al, 2014;Stavrakakis et al, 2014). Gait variables primarily focused to detect swine lameness pain in previous studies include changes in stance time, absolute weight distributed per leg, leg symmetry, joint angle, and maximum pressure applied per leg.…”

Section: Objective Lameness Assessment Using An Embedded Microcomputementioning

confidence: 99%

Measuring the efficacy of flunixin meglumine and meloxicam for lame sows using a GAITFour pressure mat and an embedded microcomputer-based force plate system1

Pairis‐Garcia¹,

Johnson²,

Abell³

et al. 2015

Journal of Animal Science

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Pain associated with lameness on farm is a negative affective state and has a detrimental impact on individual farm animal welfare. Animal pain can be managed utilizing husbandry tools and through pharmacological approaches. Nonsteroidal anti-inflammatory drugs including meloxicam and flunixin meglumine are compounds used in many species for pain management because they are easy to administer, long lasting, and cost-effective. Assessing an animal's biomechanical parameters using such tools as the embedded microcomputer-based force plate system and GAITFour pressure mat gait analysis walkway system provides an objective, sensitive, and precise means to detect animals in lame states. The objectives of this study were to determine the efficacy of meloxicam and flunixin meglumine for pain mitigation in lame sows using the embedded microcomputer-based force plate system and GAITFour pressure mat gait analysis walkway system. Lameness was induced in 24 mature mixed-parity sows using a chemical synovitis model and compared 3 treatments: meloxicam (1.0 mg/kg per os), flunixin meglumine (2.2 mg/kg intramuscular) and sterile saline (intramuscular). Weight distribution (kg) for each foot was collected twice per second for a total of 5 min for each time point using the embedded microcomputer-based force plate system. Stride time, stride length, maximum pressure, activated sensors, and stance time were collected using 3 quality walks (readings) for each time point using the GAITFour pressure mat gait analysis walkway system. Sows administered flunixin meglumine or meloxicam tolerated more weight on their lame leg compared with saline sows (P < 0.005). Sows administered flunixin meglumine or meloxicam had smaller differences in stance time, maximum pressure, and activated sensors between the sound and lame legs compared with saline-treated sows between 37 and 60 h after lameness induction (P < 0.03). In conclusion, flunixin meglumine and meloxicam administration mitigated pain sensitivity in sows after lameness induction when pain sensitivity was evaluated with the embedded microcomputer-based force plate system and GAITFour pressure mat gait analysis walkway system. Analgesic drugs may be a key tool to manage negative pain affective states associated with lameness.

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Section: Objective Lameness Assessment Using An Embedded Microcomputementioning

confidence: 99%

Measuring the efficacy of flunixin meglumine and meloxicam for lame sows using a GAITFour pressure mat and an embedded microcomputer-based force plate system1

Pairis‐Garcia¹,

Johnson²,

Abell³

et al. 2015

Journal of Animal Science

View full text Add to dashboard Cite

show abstract

“…In an attempt to achieve objectivity and to automate lameness detection, various researchers have recently used biomechanical and computer vision techniques to assess lameness in a range of species including horses (Pfau et al, 2007), cattle (Viazzi et al, 2014a;van Hertem et al, 2013) and pigs (Meijer et al, 2014;Pluym et al, 2013). Temporal gait variables (stance times), measures of asymmetry between left and right limbs and the arching of the back are the most widely used gait variables for automated lameness detection in cows (Viazzi et al, 2014a;van Nuffel et al, 2009).…”

Section: Introductionmentioning

confidence: 98%

Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs

Stavrakakis

Wei

Guy

et al. 2015

Computers and Electronics in Agriculture

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“…In recent years, sensor technologies have been introduced in livestock production, and some of them are well suited for objective locomotion scoring like force platforms, cameras, and accelerometers. Force platform systems have been used for locomotion phenotyping in experimental setups (Pastell et al, 2006;Nääs et al, 2010;Maertens et al, 2011;Pluym et al, 2013), but applications on farms are limited because they are expensive and require frequent maintenance. Cameras are upcoming tools in cattle and pigs, with the advantage that they do not disturb the animal (Kashiha et al, 2014;Viazzi et al, 2014;Kuan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Automated Step Detection in Inertial Measurement Unit Data From Turkeys

et al. 2020

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Locomotion is an important welfare and health trait in turkey production. Current breeding values for locomotion are often based on subjective scoring. Sensor technologies could be applied to obtain objective evaluation of turkey gait. Inertial measurement units (IMUs) measure acceleration and rotational velocity, which makes them attractive devices for gait analysis. The aim of this study was to compare three different methods for step detection from IMU data from turkeys. This is an essential step for future feature extraction for the evaluation of turkey locomotion. Data from turkeys walking through a corridor with IMUs attached to each upper leg were annotated manually. We evaluated change point detection, local extrema approach, and gradient boosting machine in terms of step detection and precision of start and end point of the steps. All three methods were successful in step detection, but local extrema approach showed more false detections. In terms of precision of start and end point of steps, change point detection performed poorly due to significant irregular delay, while gradient boosting machine was most precise. For the allowed distance to the annotated steps of 0.2 s, the precision of gradient boosting machine was 0.81 and the recall was 0.84, which is much better in comparison to the other two methods (<0.61). At an allowed distance of 1 s, performance of the three models was similar. Gradient boosting machine was identified as the most accurate for signal segmentation with a final goal to extract information about turkey gait; however, it requires an annotated training dataset.

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Development of a system for automatic measurements of force and visual stance variables for objective lameness detection in sows: SowSIS

Cited by 41 publications

References 39 publications

Measuring the efficacy of flunixin meglumine and meloxicam for lame sows using a GAITFour pressure mat and an embedded microcomputer-based force plate system1

Measuring the efficacy of flunixin meglumine and meloxicam for lame sows using a GAITFour pressure mat and an embedded microcomputer-based force plate system1

Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs

Automated Step Detection in Inertial Measurement Unit Data From Turkeys

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