A novel acoustic evaluation system of scratching in mouse dermatitis: Rapid and specific detection of invisibly rapid scratch in an atopic dermatitis model mouse

Umeda, Koji; Noro, Yuichi; Murakami, Takaaki; Tokime, Kazuya; Sugisaki, Hitomi; Yamanaka, Keiichi; Kurokawa, Ichiro; Kuno, Kazuhiro; Tsutsui, Hiroko; Nakanishi, Kenji; Mizutani, Hitoshi

doi:10.1016/j.lfs.2006.07.010

Cited by 24 publications

(25 citation statements)

References 17 publications

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“…The template matching method [13] also uses acoustic recordings to detect scratches, but aims to count individual scratches instead of entire bouts. This is more ambitious because individual scratches have irregular waveforms even within bouts.…”

Section: Two-pass Methods For Scratch Detectionmentioning

confidence: 99%

“…In this paper we propose a new method for automation through acoustic detection of scratches which is related to the template matching method of [13], applied to rodents, and [14], applied to humans. Acoustic detection is non-invasive and can be performed in the dark, which is important because mice are nocturnal and lighting conditions that enable the visual assessment of scratching are known to be stressful for mice.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated acoustic detection of mouse scratching

et al. 2017

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Itch is an aversive somatic sense that elicits the desire to scratch. In animal models of itch, scratching behavior is frequently used as a proxy for itch, and this behavior is typically assessed through visual quantification. However, manual scoring of videos has numerous limitations, underscoring the need for an automated approach. Here, we propose a novel automated method for acoustic detection of mouse scratching. Using this approach, we show that chloroquine-induced scratching behavior in C57BL/6 mice can be quantified with reasonable accuracy (85% sensitivity, 75% positive predictive value). This report is the first method to apply supervised learning techniques to automate acoustic scratch detection.

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Section: Two-pass Methods For Scratch Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated acoustic detection of mouse scratching

et al. 2017

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“…Umeda et al have reported on an acoustic scratching counting system K. Umeda (2006). The acoustic system eliminates the necessity of planting markers in animals; it requires a special setting to eliminate sound noise because acoustic information is extremely sensitive.…”

Section: State Of the Artmentioning

confidence: 99%

Machine Vision

Nie¹,

Ishii²,

Tanaka³

et al. 2012

Human-Centric Machine Vision

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“…Based on invasive observation using a magnet sensor, Elliott et al [6] quantified the scratching behavior by tracking a magnet marker embedded under the skin of an animal for inducing stress. Umeda et al [7] introduced an acoustic sensor system for counting repetitive limb movement without planting any marker on animals. LABORAS [8] is a commercial sensor platform for laboratory animals based on force transducers, and it can extract vibrating behaviors of animals with no invasion.…”

Section: Related Workmentioning

confidence: 99%

Behavior recognition in laboratory mice using HFR video analysis

Nie

Takaki

Ishii

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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In this paper, we propose an automatic behavior recognition algorithm for laboratory mice to determine their typical model behaviors and hence detect the degrees of diseases via animal testings for behavioral pharmacology. The algorithm can quantify several model behaviors of mice by detecting the repetitive motions of fore or hind limbs at several hertz; these motions, too rapid for the naked eye and the NTSC cameras, are captured on a high-frame-rate video. Even when a mouse changes its position and orientation, the algorithm can always detect these repetitive motions as periodic frame differential image features on four segmented regions-head, left side, right side, and tail-because the silhouette image of a mouse is converted in the polar coordinate system as its shift-and rotation-invariant shape by detecting the positions of its head and tail. The effectiveness of the algorithm is evaluated for six typical model behaviors, including scratching and grooming, by analyzing the experimental results for several laboratory mice, obtained by capturing long-term videos at 240 fps. We have provided the detection/correction ratios and compared the time durations of the model behaviors of these mice.

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A novel acoustic evaluation system of scratching in mouse dermatitis: Rapid and specific detection of invisibly rapid scratch in an atopic dermatitis model mouse

Cited by 24 publications

References 17 publications

Automated acoustic detection of mouse scratching

Automated acoustic detection of mouse scratching

Machine Vision

Behavior recognition in laboratory mice using HFR video analysis

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