Video imaging system for automated shaping and analysis of complex locomotory behavior

Publicover, Nelson G.; Hayes, Linda J.; Guerrero, L. Fernando; Hunter, Kenneth W.

doi:10.1016/j.jneumeth.2009.05.016

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Further, the floor of the pen is automatically divided into nine squares, and the time spent by the dog in each grid location can be calculated ( Fig 2 ). A similar approach can be found in [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Quick, Accurate, Smart: 3D Computer Vision Technology Helps Assessing Confined Animals’ Behaviour

et al. 2016

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Mankind directly controls the environment and lifestyles of several domestic species for purposes ranging from production and research to conservation and companionship. These environments and lifestyles may not offer these animals the best quality of life. Behaviour is a direct reflection of how the animal is coping with its environment. Behavioural indicators are thus among the preferred parameters to assess welfare. However, behavioural recording (usually from video) can be very time consuming and the accuracy and reliability of the output rely on the experience and background of the observers. The outburst of new video technology and computer image processing gives the basis for promising solutions. In this pilot study, we present a new prototype software able to automatically infer the behaviour of dogs housed in kennels from 3D visual data and through structured machine learning frameworks. Depth information acquired through 3D features, body part detection and training are the key elements that allow the machine to recognise postures, trajectories inside the kennel and patterns of movement that can be later labelled at convenience. The main innovation of the software is its ability to automatically cluster frequently observed temporal patterns of movement without any pre-set ethogram. Conversely, when common patterns are defined through training, a deviation from normal behaviour in time or between individuals could be assessed. The software accuracy in correctly detecting the dogs’ behaviour was checked through a validation process. An automatic behaviour recognition system, independent from human subjectivity, could add scientific knowledge on animals’ quality of life in confinement as well as saving time and resources. This 3D framework was designed to be invariant to the dog’s shape and size and could be extended to farm, laboratory and zoo quadrupeds in artificial housing. The computer vision technique applied to this software is innovative in non-human animal behaviour science. Further improvements and validation are needed, and future applications and limitations are discussed.

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Section: Methodsmentioning

confidence: 99%

Quick, Accurate, Smart: 3D Computer Vision Technology Helps Assessing Confined Animals’ Behaviour

et al. 2016

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“…By forming interdisciplinary research teams, behavior analysts can demonstrate rsthand the bene ts of operant procedures while also learning more about mice as subjects. Examples of collaborative research with mice that have taken place at the University of Nevada, Reno include work with faculty in physiology (see Publicover et al, 2009); immunology (see Washio, et al, 2011;Munoz-Blanco et al, 2011); pharmacology (see Lewon et al, 2017); and physiology and cell biology (see Lewon et al, in press).…”

Section: Mice As Subjects In the Experimental Analysis Of Behaviormentioning

confidence: 99%

Mice as Subjects in the Experimental Analysis of Behavior

2020

RMAC

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e experimental analysis of behavior (EAB) serves as a critical activity of ongoing scienti c discovery and a means to train future behavior scientists. Despite the importance of EAB, basic research has been under threat for some time. e factors contributing to this deterioration are complicated and related to issues of funding and relevance.e current paper will explore how a shi to mice as subjects may help to ameliorate some of these threats.

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“…Many noninvasive behavioral phenotyping systems have been devised for small animals that use video tracking (Publicover et al 2009), wheel running (Wisor et al 2009), light beam breaking (Nairizi et al 2009), and accelerometry (Venkatraman et al 2010; Brodkin et al 2014) to distinguish gross and subtle awake behaviors such as grooming, feeding, locomotion, rearing, circling, and even quiet wakefulness from sleep. Although these technologies have potential for high-throughput use, they mainly perform actigraphy: none of them reliably separate sleep into its components, namely REM and NREM , and none have been used effectively in high-throughput studies.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

Yaghouby

Donohue

O’Hara

et al. 2016

Journal of Neuroscience Methods

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Background Changes in autonomic control cause regular breathing during NREM sleep to fluctuate during REM. Piezoelectric cage-floor sensors have been used to successfully discriminate sleep and wake states in mice based on signal features related to respiration and other movements. This study presents a classifier for noninvasively classifying REM and NREM using a piezoelectric sensor. New Method Vigilance state was scored manually in 4-second epochs for 24-hour EEG/EMG recordings in twenty mice. An unsupervised classifier clustered piezoelectric signal features quantifying movement and respiration into three states: one active; and two inactive with regular and irregular breathing respectively. These states were hypothesized to correspond to Wake, NREM, and REM respectively. States predicted by the classifier were compared against manual EEG/EMG scores to test this hypothesis. Results Using only piezoelectric signal features, an unsupervised classifier distinguished Wake with high (89% sensitivity, 96% specificity) and REM with moderate (73% sensitivity, 75% specificity) accuracy, but NREM with poor sensitivity (51%) and high specificity (96%). The classifier sometimes confused light NREM sleep—characterized by irregular breathing and moderate delta EEG power—with REM. A supervised classifier improved sensitivities to 90, 81, and 67% and all specificities to over 90% for Wake, NREM, and REM respectively. Comparison with Existing Methods Unlike most actigraphic techniques, which only differentiate sleep from wake, the proposed piezoelectric method further dissects sleep based on breathing regularity into states strongly correlated with REM and NREM. Conclusions This approach could facilitate large-sample screening for genes influencing different sleep traits, besides drug studies or other manipulations.

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Video imaging system for automated shaping and analysis of complex locomotory behavior

Cited by 8 publications

References 38 publications

Quick, Accurate, Smart: 3D Computer Vision Technology Helps Assessing Confined Animals’ Behaviour

Quick, Accurate, Smart: 3D Computer Vision Technology Helps Assessing Confined Animals’ Behaviour

Mice as Subjects in the Experimental Analysis of Behavior

Noninvasive dissection of mouse sleep using a piezoelectric motion sensor

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