Accurate Horse Gait Event Estimation Using an Inertial Sensor Mounted on Different Body Locations

Darbandi, Hamed; Bragança, F. M. Serra; Zwaag, Berend Jan van der; Havinga, Paul J.M.

doi:10.1109/smartcomp55677.2022.00076

Cited by 7 publications

(9 citation statements)

References 21 publications

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“…In short, a semi-supervised approach was used to detect stance and swing from each limb, using a time-series machine learning approach similar to a previously described algorithm. 23 A sequence-to-sequence regression approach using a long-term short-memory neural network was used, using the limb and hoof sensor data as input and the swing/stance phase of each limb as output. The moments of change between the swing and the stance phase were detected and classified as hoof-on and hoof-off moments.…”

Section: Discussionmentioning

confidence: 99%

“…Hoof‐on and hoof‐off events for each limb were calculated based on manually labelled events from hoof‐mounted IMU acceleration and gyroscope data. In short, a semi‐supervised approach was used to detect stance and swing from each limb, using a time‐series machine learning approach similar to a previously described algorithm 23 . A sequence‐to‐sequence regression approach using a long‐term short‐memory neural network was used, using the limb and hoof sensor data as input and the swing/stance phase of each limb as output.…”

Section: Methodsmentioning

confidence: 99%

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Adaptation strategies of the Icelandic horse with induced forelimb lameness at walk, trot and tölt

Smit,

Hernlund,

Persson‐Sjodin

et al. 2023

Equine Veterinary Journal

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Background and objectiveLameness assessment in the gaited Icelandic horse is complex. We aimed to describe their kinematic and temporal adaptation strategies in response to forelimb lameness at walk, trot and tölt.Study designIn vivo experiment.MethodsTen clinically non‐lame Icelandic horses were measured before and after reversible forelimb lameness induction. Upper body and limb kinematics were measured using 11 inertial measurement units mounted on the poll, withers, pelvis (tubera sacrale) and all four limbs and hoofs (Equimoves®, 500 Hz). Horses were measured on a straight line at walk and trot in‐hand and at walk, trot and tölt while ridden. Linear mixed models were used to compare baseline and lame conditions (random factor = ‘horse’), and results are presented as the difference in estimated marginal means or percentage of change.ResultsLameness induction significantly (p < 0.05) increased head vertical movement asymmetry at walk (HDmin/HDmaxHAND: 18.8/5.7 mm, HDmin/HDmaxRIDDEN: 9.8/0.3 mm) and trot (HDmin/HDmaxHAND: 18.1/7.8 mm, HDmin/HDmaxRIDDEN: 24.0/9.3 mm). At the tölt, however, HDmin did not change significantly (1.1 mm), but HDmax increased by 11.2 mm (p < 0.05). Furthermore, pelvis vertical movement asymmetry (PDmax) increased by 4.9 mm, sound side dissociation decreased (−8.3%), and sound diagonal dissociation increased (6.5%). Other temporal stride variables were also affected, such as increased stance duration of both forelimbs at walk, tölt and in‐hand trot.Main limitationsOnly one degree of lameness (mild) was induced with an acute lameness model.ConclusionsClassical forelimb lameness metrics, such as vertical head and withers movement asymmetry, were less valuable at tölt compared to walk and trot, except for HDmax. Therefore, it is advised to primarily use the walk and trot to detect and quantify forelimb lameness in the Icelandic horse.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Adaptation strategies of the Icelandic horse with induced forelimb lameness at walk, trot and tölt

Smit,

Hernlund,

Persson‐Sjodin

et al. 2023

Equine Veterinary Journal

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“…By analyzing its output signals, i.e., acceleration and angular velocity, biomechanical features, specific to the point of attachment on the body, can be calculated [37]. Therefore, IMUs can be used during exercise, with the combination of scientifically validated algorithms, for monitoring the biomechanical features during exercise [38,39].…”

Section: Fatigue Assessment Methodsmentioning

confidence: 99%

“…The raw signals derived from the IMUs (three signals of acceleration and three signals of angular velocity) were low-pass filtered (fourth-order Butterworth filter and 30 Hz cut-off frequency) for noise reduction [38]. Then, the filtered signals were windowed into strides (from hoof-on to next hoof-on of right front limb) by implementing an estimation method on the right front limb IMU signals [39]. The pre-and post-SET data were separated from the start, hence, the strides were automatically labeled as pre-SET or post-SET.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Detecting fatigue of sport horses with biomechanical gait features using inertial sensors

et al. 2023

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Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk and trot before and after high and low-intensity exercises. Then, biomechanical features were extracted from the output signals. A number of features were assigned as important fatigue indicators using neighborhood component analysis. Based on the fatigue indicators, machine learning models were developed for classifying strides to non-fatigue and fatigue. As an outcome, this study confirmed that biomechanical features can indicate fatigue in horses, such as stance duration, swing duration, and limb range of motion. The fatigue classification model resulted in high accuracy during both walk and trot. In conclusion, fatigue can be detected during exercise by using the output of body-mounted inertial sensors.

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Terrain Type Detection for Smart Equine Gait Analysis Systems Using Inertial Sensors and Machine Learning

Parmentier,

Bragança,

Hernlund

et al. 2023

2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

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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, resulting in smart and easy-to-use systems. However, terrain types are not detected, leading to information loss. In this work, we explored terrain classification tasks with equine IMU data and machine and deep learning. Using the data of 111 horses equipped with IMU sensors (withers, pelvis, front, and hind limbs), we compared different features-based (FT) and time-series-based (TS) classifiers (train-test ratio: 0.7-0.3). In order to reduce the computational costs of the future system, we also evaluated the performance (F1 score) of the classifiers with different sampling frequencies (10 to 200Hz) and different IMU combinations (body and limbs). Our Convolutional Neural Network models accurately classified terrain types with only one IMU placed on the front limb. Downsampling the signals led to similar results, thus enabling real-time applications.

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Accurate Horse Gait Event Estimation Using an Inertial Sensor Mounted on Different Body Locations

Cited by 7 publications

References 21 publications

Adaptation strategies of the Icelandic horse with induced forelimb lameness at walk, trot and tölt

Adaptation strategies of the Icelandic horse with induced forelimb lameness at walk, trot and tölt

Detecting fatigue of sport horses with biomechanical gait features using inertial sensors

Terrain Type Detection for Smart Equine Gait Analysis Systems Using Inertial Sensors and Machine Learning

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