Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

Baratchi, Mitra; Meratnia, Nirvana; Havinga, Paul J.M.; Skidmore, Andrew K.; Toxopeus, Bert

doi:10.3390/s130506054

Cited by 62 publications

(55 citation statements)

References 112 publications

(120 reference statements)

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“…Over the past decade, there has been a huge increase in the use of remote monitoring devices such as global positioning (GPS) trackers, location sensors, proximity loggers and accelerometers for automated recording of both human and animal behaviour [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. By necessity, this has led to the need for more efficient and accurate methods of analysing the vast amounts of movement and behavioural data that are being collected [17].…”

Section: Introductionmentioning

confidence: 99%

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

et al. 2015

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Background: Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Results: Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines.

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Section: Introductionmentioning

confidence: 99%

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

et al. 2015

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“…Motion detectors are limited to a range of a few tens of meters and multimedia sensors can cover areas of at most a few hundred meters due to the limited resolution of low-power hardware [24]. Attaching sensors to mobile objects such as domestic and wild animals has the potential to greatly extend the spatial coverage of fixed sensors.…”

Section: Mobile Sensorsmentioning

confidence: 99%

Autonomous surveillance for biosecurity

Jurdak¹,

Elfes²,

Kusý³

et al. 2015

Trends in Biotechnology

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The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

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“…Capricorn dataset [36,37]: This dataset is composed of GPS data collected by data loggers attached to a Capricorn. The Cretan Capricorn (Capra aegagrus-cretica) lives in the White Mountains and is endemic for Crete.…”

Section: Datasetsmentioning

confidence: 99%

A hierarchical hidden semi-Markov model for modeling mobility data

Baratchi

Meratnia

Havinga

et al. 2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatiotemporal associations in the locational history both at staypoints and on the paths connecting them. We compare the accuracy of our model with a number of other spatiotemporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.

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Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

Cited by 62 publications

References 112 publications

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Autonomous surveillance for biosecurity

A hierarchical hidden semi-Markov model for modeling mobility data

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