Ratpack: Wearable sensor networks for animal observation

Osechas, Okuary; Thiele, Johannes; Bitsch, Jó Ágila; Wehrle, Klaus

doi:10.1109/iembs.2008.4649209

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…The recorded data is stored locally until the animal surfaces, at which point it is uploaded using a 2.4GHz radio. Osechas et al [17] describe a Wireless Sensor Network (WSN) deployment for tracking the movement of rats attached with Mica2dot nodes, along with a sensor suite comprising a microphone and an accelerometer. Johnson and Tyack [11] present DTAG, a device designed to monitor the behaviour of whales during their dive cycle.…”

Section: A Non-gps Animal Trackingmentioning

confidence: 99%

“…Wildlife tracking/monitoring or node localisation applications using WSNs rely on frequent exchanges of beacons or control packets over the radio to establish links between the nodes [6], [10], [12], [17], which has significant implications for the power consumption. For WSN applications which do not have the option of replacing the battery once deployed, optimising the battery consumption is essential by waking up the nodes simultaneously when required to exchange data.…”

Section: B Animal Tracking Solutions Based On Gpsmentioning

confidence: 99%

“…Various deployments have taken place for tracking and monitoring different types of animals on the ground [6], [4], [16], [18], [7], [12], [5], [13], in the air [5], underground [10], [15], [17] and underwater [11]. This paper focuses on the solutions for terrestrial deployments for monitoring animals (especially, tracking solutions that use GPS for determining their locations).…”

Section: Introductionmentioning

confidence: 99%

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Tracking and monitoring horses in the wild using wireless sensor networks

Radoi

Mann

Arvind

2015

2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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The Retuerta is one of the oldest breed of horses in Europe, which roams wild in the Donana National Park, Andalusia, Spain. Thirty-two of these horses were marked with wireless sensors to gather spatio-temporal data on their behaviour over a period of several months. This paper describes our experiences of tracking and monitoring these wild horses attached with body-worn sensors and operating in a harsh and challenging environment. Analysis of this data for the first two months has revealed rare insights into the horses' social behaviour, such as the group dynamics (group sizes and memberships), dispersal and home ranges which are of interest to both animal ethologists and practitioners managing the ecology of their wild habitats. The paper introduces the Virtual Beacon-Time Division Multiple Access (VB-TDMA) protocol for orchestrating the data collection, and describes the choices that were made for addressing the many technical challenges for an extended deployment, such as in the design of the sensor platform, wireless data collection and battery lifetime issues. Our experiences point to the virtue of simplicity in design of wireless sensor networks to support core functionalities for achieving good average case performances.

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Section: A Non-gps Animal Trackingmentioning

confidence: 99%

Section: B Animal Tracking Solutions Based On Gpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking and monitoring horses in the wild using wireless sensor networks

Radoi

Mann

Arvind

2015

2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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“…Many of them can be solved trough the use of wireless sensors. For instance, wireless sensors can be deployed in an undisturbed area in order to monitor invasive [3], [4] or endangered species. In the first case, the aim is to understand why the species spreads in order to regulate the phenomenon.…”

Section: A Environmental Monitoringmentioning

confidence: 99%

Data collection for counting individuals and habitat characterization

Dessart

Hunel

Gros-Désormeaux

2013

2013 IEEE Symposium on Computers and Communications (ISCC)

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Decrease in the number of individuals of a species is an indicator of the state of biodiversity in a region. It is difficult to estimate this decline in the traditional way. We suggest to use ICT to measure this phenomenon. We propose using a distributed algorithm to count individuals in a population and to collect abiotic factors in order to characterize habitat. The first phase consists of detecting the presence of individuals from mapping ecotopes, the second allows counting the number of individuals and the last aggregates the numbers of individuals over several paths. We propose an approach that uses a wireless sensor network built on a linear architecture based on the concept of green tunnel. Here, each mote has a successor and a predecessor.

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“…Animal behavior analysis has been widely used in biological sciences, neuroscience, pathology, and genetics [1][2][3][4] to study neural functions, psychological processes, and drug effects. Traditional behavioral analysis methods mostly use manual observation and sensor methods [5][6][7], such as piezoelectric sensors, infrared sensors, and micro-photoelectric systems, which are time-consuming, laborious, and inflexible. More importantly, the influence of sound, light, electricity, and odor caused by sensors and manual observation can interfere with the natural behavior of experimental animals, resulting in deviations in the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network

Liu¹,

Zhu

Rao

2022

Symmetry

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Murine behavior recognition is widely used in biology, neuroscience, pharmacology, and other aspects of research, and provides a basis for judging the psychological and physiological state of mice. To solve the problem whereby traditional behavior recognition methods only model behavioral changes in mice over time or space, we propose a symmetrical algorithm that can capture spatiotemporal information based on behavioral changes. The algorithm first uses the improved DeepLabCut keypoint detection algorithm to locate the nose, left ear, right ear, and tail root of the mouse, and then uses the ConvLSTM network to extract spatiotemporal information from the keypoint feature map sequence to classify five behaviors of mice: walking straight, resting, grooming, standing upright, and turning. We developed a murine keypoint detection and behavior recognition dataset, and experiments showed that the method achieved a percentage of correct keypoints (PCK) of 87±1% at three scales and against four backgrounds, while the classification accuracy for the five kinds of behaviors reached 93±1%. The proposed method is thus accurate for keypoint detection and behavior recognition, and is a useful tool for murine motion behavior recognition.

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Ratpack: Wearable sensor networks for animal observation

Cited by 8 publications

References 19 publications

Tracking and monitoring horses in the wild using wireless sensor networks

Tracking and monitoring horses in the wild using wireless sensor networks

Data collection for counting individuals and habitat characterization

Murine Motion Behavior Recognition Based on DeepLabCut and Convolutional Long Short-Term Memory Network

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