Embedded neural network for real-time animal behavior classification

Gutiérrez-Galán, Daniel; Domínguez-Morales, Juan Pedro; Cerezuela-Escudero, Elena; Ríos-Navarro, Antonio; Tapiador-Morales, Ricardo; Rivas-Pérez, M.; Domínguez-Morales, Manuel; Jiménez-Fernández, Ángel; Linares-Barranco, A.

doi:10.1016/j.neucom.2017.03.090

Cited by 51 publications

(36 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After training, the classifier can classify unlabeled raw data-samples into the learned activity categories. Recently, various studies utilized IMUs for AAR regarding: wildlife [3][4][5][6][7][8][9], livestock [1,2,[10][11][12][13][14][15][16][17][18], and pets [19][20][21].…”

Section: Discussionmentioning

confidence: 99%

Horsing Around—A Dataset Comprising Horse Movement

Kamminga

Janßen

Meratnia

et al. 2019

Data

View full text Add to dashboard Cite

Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.

show abstract

Section: Discussionmentioning

confidence: 99%

Horsing Around—A Dataset Comprising Horse Movement

Kamminga

Janßen

Meratnia

et al. 2019

Data

View full text Add to dashboard Cite

show abstract

“…On the other hand, 802.15.4 motes are small solar-powered devices whose purpose is to extend the coverage of the network, being able to receive packets from collars and send them to another mote or directly to the base station if it is close enough. More information about these two systems and the others that have been mentioned can be found in previous publications [23][24][25]. In this work, we focus on the main relevant components for the distance estimation task that we present, which are: collars, the 802.…”

Section: Hardware Infrastructurementioning

confidence: 99%

“…The data obtained from the IMU are processed to extract the relevant information from the sensors, and then sent to a neural network (NN) that is embedded on the collar's micro-controller, which was trained to classify between three different gaits: walking, trotting, and running. More information about the network, the training process, and the accuracy that the system is able to achieve can be found in [23].…”

Section: Collarsmentioning

confidence: 99%

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

et al. 2019

View full text Add to dashboard Cite

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana's National Park. As a supporting feature for that project, this work was implemented for locating animal's collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.Finite-difference time-domain [12], or Artificial Networks [13] are much better when estimating position in terms of accuracy than other models, but, as mentioned above, at the expense of a high computation cost [14]. Other models (e.g., Reference [15]) are well accepted for network deployment of cellular devices (≤1.5 GHz) but not suitable for higher frequencies.The log-normal model, together with the free-space model [16] are some of the most used models operating at 2.5 GHz for being the calculation procedure simple [4] (at distances greater than the length of the antenna and the wavelength) but at the expense of a lower accuracy. One alternative is 2RM (2-Ray Model) [4], which includes this length as one of the parameters to calculate two possible signal paths. An improved version of this model can be found at [17] but the improvements over the log-normal model are only present at d ≥ 4πH rx H tx , which in our case is more than 250 m (when setting the maximum height of the antennas that were used in this work at 160 cm). Further details about the position of the antenna can be found in Section 2.5.This work presents a relative distance estimator for Wireless Sensor Network devices operating at 2.5 GHz using the 802.15.4 standard [18], on which the target device is a wildlife tracking and behavior classifying device that is attached to an animal by means of a collar. The distance estimator is used to locate collars belonging to animals that are not moving, or collars that have been abandoned by the animals to which they belonged, or even collars that have gone outside of the coverage area of the WSN. On the other hand, the locating device (that uses the distance estimator algorithm) is used by an operator that moves overland with a WSN node that acts as a 802.15.4 wireless packet sniffer. To justify this development, the propagation model and filters used are shown together with two ways to deal with signal measurement variability and uncertainty. In Section 3, we present the data obtained fro...

show abstract

“…After formalizing the data, it is processed into information and analyzed by the software that can provide a customized service by the needs from the famers, researchers, government officers, supporters and etc. [33,34]. On a specific basis, the continuous collection of data regarding the amount of activity of the animals of the object has a role in distinguishing the animal from the object, such as fertilization, estrus, disease, fertility and other biological changes (water medicine).…”

Section: Format Of Communication Protocol and Data Analysismentioning

confidence: 99%

The Platform Development of a Real-Time Momentum Data Collection System for Livestock in Wide Grazing Land

Zhang

Kim

Lee³

2018

Electronics

View full text Add to dashboard Cite

In the process of animal husbandry production through grazing, animals are active in large grassland or mountain areas, and it is very difficult to obtain and deal with the information on animal activity and state of life. In this paper, we propose a platform for operation of data transmission and analysis system which gathers activity and status information of livestock. The data collected in real time from integrated livestock sensor modules are anticipated to assist farmers to supervise animal activities and health. While at the same time the improvements of viable farming techniques are expected to reduce the impact of the livestock industry on the environment. For the individual management of target livestock, the data collection system needs a convergence technology with a physical active sensor, a wireless sensor network and solar power technology to cover the wide area of mountains. We implemented a momentum data collection system to collect and transfer the information of ecological and situations of livestock in grazing, which include sensor and communication modules, repeaters with solar panels to cover the problems of communications in wide grazing and a receiver connected to main server. Besides, in order to prevent data collisions and deviations on multiple transmitter operation, we renewed the format of the communication protocol and made a platform to analyze animal activities information by software. Finally, the system and platform were applied and tested in National Rural Development Administration in Republic of Korea.

show abstract

Embedded neural network for real-time animal behavior classification

Cited by 51 publications

References 15 publications

Horsing Around—A Dataset Comprising Horse Movement

Horsing Around—A Dataset Comprising Horse Movement

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

The Platform Development of a Real-Time Momentum Data Collection System for Livestock in Wide Grazing Land

Contact Info

Product

Resources

About