2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) 2023
DOI: 10.1109/dcoss-iot58021.2023.00029
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Terrain Type Detection for Smart Equine Gait Analysis Systems Using Inertial Sensors and Machine Learning

Jeanne I.M. Parmentier,
Filipe M. Serra Bragança,
Elin Hernlund
et al.

Abstract: Lameness, limping due to pain, is a significant welfare issue for horses. Veterinarians typically evaluate horses on two terrain types (hard and soft, e.g., asphalt and sand) that are known to affect the observed degree of lameness based on the origin/location of the pain. In the past years, whole-body inertial measurement units (IMU)-based gait analysis systems were developed to support diagnostics and monitor locomotion changes over time. Movement direction and gait (walk, trot) are automatically labeled, re… Show more

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“…Vertical displacement of head, withers and pelvis was calculated from horses wearing IMUs after lameness induction in single limbs; however, instead of using asymmetry thresholds, the data was fed in the form of different types of input (combining head and pelvis alone, or pelvis and withers alone) into a computer model to evaluate the ability of deep learning networks to classify strides as sound or front or hindlimb lame, concluding that deep learning models can correctly classify the lame limbs. 2 In the future, this should be tried in patients with naturally occurring lameness. Similarly, this research team showed that machine learning models could also reliably discriminate hard and soft surfaces based on IMU signals.…”
Section: Artificial Intelligence and Lamenessmentioning
confidence: 99%
“…Vertical displacement of head, withers and pelvis was calculated from horses wearing IMUs after lameness induction in single limbs; however, instead of using asymmetry thresholds, the data was fed in the form of different types of input (combining head and pelvis alone, or pelvis and withers alone) into a computer model to evaluate the ability of deep learning networks to classify strides as sound or front or hindlimb lame, concluding that deep learning models can correctly classify the lame limbs. 2 In the future, this should be tried in patients with naturally occurring lameness. Similarly, this research team showed that machine learning models could also reliably discriminate hard and soft surfaces based on IMU signals.…”
Section: Artificial Intelligence and Lamenessmentioning
confidence: 99%