A Real-Time Rodent Tracking System for Both Light and Dark Cycle Behavior Analysis

Zurn, J.B.; Hohmann, Daniela Michelle; Dworkin, Steven I.; Motai, Yuichi

doi:10.1109/acvmot.2005.9

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…Typically, these systems quantify movement, either by photobeam array [13], [14] or overhead video tracking [15], [16]; feeding, by weighing food hoppers on a precision scale; and drinking, by weighing water bottles on a precision scale or by measuring conductivity across the water bottle sipper tube. However, none of these approaches measure mouse movement, feeding, and drinking with high temporal and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment

Parkison

Carlson

Chaudoin

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Inexpensive, high-throughput, low maintenance systems for precise temporal and spatial measurement of mouse home cage behavior (including movement, feeding, and drinking) are required to evaluate products from large scale pharmaceutical design and genetic lesion programs. These measurements are also required to interpret results from more focused behavioral assays. We describe the design and validation of a highly-scalable, reliable mouse home cage behavioral monitoring system modeled on a previously described, one-of-a-kind system [1]. Mouse position was determined by solving static equilibrium equations describing the force and torques acting on the system strain gauges; feeding events were detected by a photobeam across the food hopper, and drinking events were detected by a capacitive lick sensor. Validation studies show excellent agreement between mouse position and drinking events measured by the system compared with video-based observation – a gold standard in neuroscience.

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Section: Introductionmentioning

confidence: 99%

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment

Parkison

Carlson

Chaudoin

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…We opted to investigate behavior learning because it is useful for many current and potential applications. They range from smart surveillance [5] to remote monitoring of elderly patients in healthcare centers and from building a profile of people manners [6] to elucidating rodent behavior under drug effects [7], and so forth. For illustration purposes, we have applied our technique to learn the behavior of an articulated humanoid through video footage captured by monitoring camera sensors.…”

Section: Introductionmentioning

confidence: 99%

Incremental Support Vector Machine Framework for Visual Sensor Networks

Awad

Jiang

Motai

2006

EURASIP J. Adv. Signal Process.

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Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM) technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM) formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

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“…Additionally, several noncommercial mouse-monitoring systems or methods have been developed (Branson & Belongie, 2005; de Chaumont et al, 2012; Jhuang et al, 2010; Zarringhalam et al, 2012; Zurn, Xianhua, & Motai, 2005b). The noninvasive nature of video-based monitoring systems combined with the precision and granularity of behavioral measures afforded by such systems enables more advanced and thorough studies in comparison to basic systems limited to a running wheel or photobeam detectors (Baker, 2011; Zurn, Hohmann, Dworkin, & Motai, 2005a; Zurn et al 2005b). Studies have shown the importance of monitoring mice in the home-cage to eliminate the stress-induced effects caused by transplanting a mouse into a specialized test environment (Baker, 2011; Goulding et al, 2008; Schaefer & Claridge-Chang, 2012; Spruijt & DeVisser, 2006; Steele, Jackson, King, & Lindquist, 2007).…”

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confidence: 99%

SCORHE: A novel and practical approach to video monitoring of laboratory mice housed in vivarium cage racks

et al. 2014

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The System for Continuous Observation of Rodents in Home-cage Environment (SCORHE) was developed to demonstrate the viability of compact and scalable designs for quantifying activity levels and behavior patterns for mice housed within a commercial ventilated cage rack. The SCORHE in-rack design provides day- and night-time monitoring with the consistency and convenience of the home-cage environment. The dual-video camera custom hardware design makes efficient use of space, does not require home-cage modification, and is animal-facility user-friendly. Given the system’s low cost and suitability for use in existing vivariums without modification to the animal husbandry procedures or housing setup, SCORHE opens up the potential for the wider use of automated video monitoring in animal facilities. SCORHE’s potential uses include day-to-day health monitoring, as well as advanced behavioral screening and ethology experiments, ranging from the assessment of the short- and long-term effects of experimental cancer treatments to the evaluation of mouse models. When used for phenotyping and animal model studies, SCORHE aims to eliminate the concerns often associated with many mouse-monitoring methods, such as circadian rhythm disruption, acclimation periods, lack of night-time measurements, and short monitoring periods. Custom software integrates two video streams to extract several mouse activity and behavior measures. Studies comparing the activity levels of ABCB5 knockout and HMGN1 overexpresser mice with their respective C57BL parental strains demonstrate SCORHE’s efficacy in characterizing the activity profiles for singly- and doubly-housed mice. Another study was conducted to demonstrate the ability of SCORHE to detect a change in activity resulting from administering a sedative.

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A Real-Time Rodent Tracking System for Both Light and Dark Cycle Behavior Analysis

Abstract: Abstract

Cited by 11 publications

References 18 publications

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment

A low-cost, reliable, high-throughput system for rodent behavioral phenotyping in a home cage environment

Incremental Support Vector Machine Framework for Visual Sensor Networks

SCORHE: A novel and practical approach to video monitoring of laboratory mice housed in vivarium cage racks

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