Human and Animal Motion Tracking Using Inertial Sensors

Marin, F.

doi:10.3390/s20216074

Cited by 17 publications

(19 citation statements)

References 13 publications

(23 reference statements)

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“…Recently, other studies proposed an investigation into the protraction and retraction angles of horse limbs by the use of IMUs [ 12 , 15 , 24 ]. Inertial sensors, i.e., IMUs, are more versatile [ 28 ] and they can allow recordings in real equestrian conditions. However, they require the use of specific methods to estimate the spatial orientation [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…Inertial sensors, i.e., IMUs, are more versatile [ 28 ] and they can allow recordings in real equestrian conditions. However, they require the use of specific methods to estimate the spatial orientation [ 28 ]. In this study, using a strapdown inertial navigation system approach [ 47 ], the orientation of the IMU positioned on the horse’s canon bone was computed by the integration of the gyrometer data.…”

Section: Discussionmentioning

confidence: 99%

“…In the majority of studies, the limb’s protraction and retraction measurements are performed using a motion capture system (MOCAP) [ 13 , 14 , 25 ]. Although precise, these systems involve laboratory use, which limits their applications [ 28 ]. More recently, studies have focused on the use of inertial measurement units (IMUs) to measure the protraction and retraction angles of horse limbs [ 12 , 15 , 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, studies have focused on the use of inertial measurement units (IMUs) to measure the protraction and retraction angles of horse limbs [ 12 , 15 , 22 , 23 , 24 ]. The use of IMUs, more versatile and pervasive [ 28 ], can then allow these measurements under the normal working conditions of the horse [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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The Protraction and Retraction Angles of Horse Limbs: An Estimation during Trotting Using Inertial Sensors

Sapone

Martin

Mansour

et al. 2021

Sensors

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The protraction and retraction angles of horse limbs are important in the analysis of horse locomotion. This study explored two methods from an IMU positioned on the canon bone of eight horses to estimate these angles. Each method was based on a hypothesis in order to define the moment corresponding with the verticality of the canon bone: (i) the canon bone is in a vertical position at 50% of the stance phase or (ii) the verticality of the canon bone corresponds with the moment when the horse’s withers reach their lowest point. The measurements were carried out on a treadmill at a trot and compared with a standard gold method based on motion capture. For the measurement of the maximum protraction and retraction angles, method (i) had average biases (0.7° and 1.7°) less than method (ii) (−1.3° and 3.7°). For the measurement of the protraction and retraction angles during the stance phase, method (i) had average biases (4.1° and −3.3°) higher to method (ii) (2.1° and −1.3°). This study investigated the pros and cons of a generic method (i) vs. a specific method (ii) to determine the protraction and retraction angles of horse limbs by a single IMU.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Protraction and Retraction Angles of Horse Limbs: An Estimation during Trotting Using Inertial Sensors

Sapone

Martin

Mansour

et al. 2021

Sensors

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“…Recently, it was demonstrated that inertial measurement units (IMUs) are a real alternative to performing motion capture [ 18 ]. The use of IMUs allows recording the horse locomotion with on-board technologies and in field conditions [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles

Hatrisse

Macaire

Sapone³

et al. 2022

Sensors

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The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to compare the accuracy of different methods for an automatic gait events detection from inertial measurement units (IMUs). IMUs were positioned on the cannon bone, hooves, and withers of seven horses trotting on hard and soft straight lines and circles. Longitudinal acceleration and angular velocity around the latero-medial axis of the cannon bone, and withers dorso-ventral displacement data were identified to tag the foot-on and a heel-off events. The results were compared with a reference method based on hoof-mounted-IMU data. The developed method showed bias less than 1.79%, 1.46%, 3.45% and −1.94% of stride duration, respectively, for forelimb foot-on and heel-off, and for hindlimb foot-on and heel-off detection, compared to our reference method. The results of this study showed that the developed gait-events detection method had a similar accuracy to other methods developed for straight line analysis and extended this validation to other types of exercise (circles) and ground surface (soft surface).

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A guide to sampling design for GPS-based studies of animal societies

Klarevas‐Irby

Papageorgiou

et al. 2022

Preprint

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GPS-based tracking is a widely used automated data collection method for studying wild animals. Much like traditional observational methods, using GPS devices to study social animals requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across many distinct social groups may not be informative enough for inferring behavioural patterns at a finer social organizational scale, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations. Here, we integrate previous insights from animal social network analysis with simulated and empirical data to provide quantitative recommendations when designing GPS-based tracking studies of animal societies. We outline the trade-offs faced by researchers, and how these trade-offs should vary across social organizational scales and social systems in relation to questions of interest, such as those defined within versus among groups, and spanning from cohesive, stable groups through to more open societies. We discuss three fundamental axes of sampling effort requiring consideration when deploying GPS devices to study animal societies: 1) Sampling coverage—the number and allocation of GPS devices among individuals in one or more social groups; 2) Sampling duration—the total amount of time over which devices collect data; 3) Sampling frequency—the temporal resolution at which GPS devices record data. Exploring each axis, we quantify the relationship between sampling effort and sampling error, giving recommendations on GPS sampling design to address research questions across social organizational scales and social systems—from group movement to social network structure and collective decision-making. In doing so, we also highlight practical limitations on deploying GPS tracking. Our study provides practical advice for empiricists to navigate their decision-making processes when designing GPS-based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions.

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Human and Animal Motion Tracking Using Inertial Sensors

Cited by 17 publications

References 13 publications

The Protraction and Retraction Angles of Horse Limbs: An Estimation during Trotting Using Inertial Sensors

The Protraction and Retraction Angles of Horse Limbs: An Estimation during Trotting Using Inertial Sensors

Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles

A guide to sampling design for GPS-based studies of animal societies

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