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Abstract
We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
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Simanungkalit G, Barwick J, Cowley F, Dobos R, Hegarty R. A Pilot Study Using Accelerometers to Characterise the Licking Behaviour of Penned Cattle at a Mineral Block Supplement. Animals (Basel) 2021; 11:ani11041153. [PMID: 33920600 PMCID: PMC8073741 DOI: 10.3390/ani11041153] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 12/14/2022] Open
Abstract
Simple Summary Quantifying mineral block supplement intake by individual beef cattle is a challenging task but may enable improved efficiency of supplement use particularly in a grazed system. Estimating time spent licking when cattle access the mineral block supplement can be useful for predicting intake on an individual basis. The advancement of sensor technology has facilitated collection of individual data associated with ingestive behaviours such as feeding and licking duration. This experiment was intended to investigate the effectiveness of wearable tri-axial accelerometers fitted on both neck-collar and ear-tag to identify the licking behaviour of beef cattle by distinguishing it from eating, standing and lying behaviours. The capability of tri-axial accelerometers to classify licking behaviour in beef cattle revealed in this study would offer the possibility of measuring time spent licking and further developing a practical method of estimating mineral block supplement intake by individual grazing cattle. Abstract Identifying the licking behaviour in beef cattle may provide a means to measure time spent licking for estimating individual block supplement intake. This study aimed to determine the effectiveness of tri-axial accelerometers deployed in a neck-collar and an ear-tag, to characterise the licking behaviour of beef cattle in individual pens. Four, 2-year-old Angus steers weighing 368 ± 9.3 kg (mean ± SD) were used in a 14-day study. Four machine learning (ML) algorithms (decision trees [DT], random forest [RF], support vector machine [SVM] and k-nearest neighbour [kNN]) were employed to develop behaviour classification models using three different ethograms: (1) licking vs. eating vs. standing vs. lying; (2) licking vs. eating vs. inactive; and (3) licking vs. non-licking. Activities were video-recorded from 1000 to 1600 h daily when access to supplement was provided. The RF algorithm exhibited a superior performance in all ethograms across the two deployment modes with an overall accuracy ranging from 88% to 98%. The neck-collar accelerometers had a better performance than the ear-tag accelerometers across all ethograms with sensitivity and positive predictive value (PPV) ranging from 95% to 99% and 91% to 96%, respectively. Overall, the tri-axial accelerometer was capable of identifying licking behaviour of beef cattle in a controlled environment. Further research is required to test the model under actual grazing conditions.
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Affiliation(s)
- Gamaliel Simanungkalit
- Ruminant Research Group (RRG), School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia; (F.C.); (R.H.)
- Correspondence: ; Tel.: +61-2-6773-3929
| | - Jamie Barwick
- Precision Agriculture Research Group (PARG), School of Science and Technology, University of New England, Armidale, NSW 2351, Australia; (J.B.); (R.D.)
| | - Frances Cowley
- Ruminant Research Group (RRG), School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia; (F.C.); (R.H.)
| | - Robin Dobos
- Precision Agriculture Research Group (PARG), School of Science and Technology, University of New England, Armidale, NSW 2351, Australia; (J.B.); (R.D.)
- Livestock Industries Centre, NSW Department of Primary Industries, University of New England, Armidale, NSW 2351, Australia
| | - Roger Hegarty
- Ruminant Research Group (RRG), School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia; (F.C.); (R.H.)
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Meschenmoser P, Buchmuller JF, Seebacher D, Wikelski M, Keim DA. MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2021; 27:1623-1633. [PMID: 33052856 DOI: 10.1109/tvcg.2020.3030386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.
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Monitoring canid scent marking in space and time using a biologging and machine learning approach. Sci Rep 2020; 10:588. [PMID: 31953418 PMCID: PMC6969016 DOI: 10.1038/s41598-019-57198-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/21/2019] [Indexed: 11/12/2022] Open
Abstract
For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species’ morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.
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Chakravarty P, Cozzi G, Ozgul A, Aminian K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13172] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Pritish Chakravarty
- Interfaculty Institute of Bioengineering (IBI‐STI)Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Gabriele Cozzi
- Institute of Evolutionary Biology and Environmental StudiesUniversity of Zurich Zurich Switzerland
- Kalahari Research CentreKuruman River Reserve Van Zylsrus South Africa
| | - Arpat Ozgul
- Institute of Evolutionary Biology and Environmental StudiesUniversity of Zurich Zurich Switzerland
- Kalahari Research CentreKuruman River Reserve Van Zylsrus South Africa
| | - Kamiar Aminian
- Interfaculty Institute of Bioengineering (IBI‐STI)Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland
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Wijers M, Trethowan P, Markham A, du Preez B, Chamaillé-Jammes S, Loveridge A, Macdonald D. Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance. Front Ecol Evol 2018. [DOI: 10.3389/fevo.2018.00171] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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7
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Hao T, Zhu C, Mu Y, Liu G. A user-oriented semantic annotation approach to knowledge acquisition and conversion. J Inf Sci 2017. [DOI: 10.1177/0165551516642688] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Semantic annotation on natural language texts labels the meaning of an annotated element in specific contexts, and thus is an essential procedure for domain knowledge acquisition. An extensible and coherent annotation method is crucial for knowledge engineers to reduce human efforts to keep annotations consistent. This article proposes a comprehensive semantic annotation approach supported by a user-oriented markup language named UOML to enhance annotation efficiency with the aim of building a high quality knowledge base. UOML is operable by human annotators and convertible to formal knowledge representation languages. A pattern-based annotation conversion method named PAC is further proposed for knowledge exchange by utilizing automatic pattern learning. We designed and implemented a semantic annotation platform Annotation Assistant to test the effectiveness of the approach. By applying this platform in a long-term international research project for more than three years aiming at high quality knowledge acquisition from a classical Chinese poetry corpus containing 52,621 Chinese characters, we effectively acquired 150,624 qualified annotations. Our test shows that the approach has improved operational efficiency by 56.8%, on average, compared with text-based manual annotation. By using UOML, PAC achieved a conversion error ratio of 0.2% on average, significantly improving the annotation consistency compared with baseline annotations. The results indicate the approach is feasible for practical use in knowledge acquisition and conversion.
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Affiliation(s)
- Tianyong Hao
- School of Informatics, Guangdong University of Foreign Studies, China
| | - Chunshen Zhu
- Department of Chinese and History, City University of Hong Kong, Hong Kong
| | - Yuanyuan Mu
- Center for Corpus-based Translation Studies, Hefei University of Technology, China
| | - Gang Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong
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Mahoney PJ, Young JK. Uncovering behavioural states from animal activity and site fidelity patterns. Methods Ecol Evol 2016. [DOI: 10.1111/2041-210x.12658] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Peter J. Mahoney
- Department of Wildland Resources and The Ecology Center Utah State University Logan UT 84322 USA
| | - Julie K. Young
- USDA‐WS‐NWRC‐Predator Research Facility Logan UT 84322 USA
- Department of Wildland Resources Utah State University Logan UT 84322 USA
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Allen AN, Goldbogen JA, Friedlaender AS, Calambokidis J. Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales. Ecol Evol 2016; 6:7522-7535. [PMID: 28725418 PMCID: PMC5513260 DOI: 10.1002/ece3.2386] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Revised: 07/11/2016] [Accepted: 07/18/2016] [Indexed: 11/29/2022] Open
Abstract
The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.
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Affiliation(s)
- Ann N Allen
- Cascadia Research Collective 218 1/2 W. 4th Avenue Olympia Washington 98501
| | - Jeremy A Goldbogen
- Department of Biology Hopkins Marine Station Stanford University Pacific Grove California 93950
| | - Ari S Friedlaender
- Department of Fisheries and Wildlife Marine Mammal Institute Hatfield Marine Science Center Oregon State University Newport Oregon 97365
| | - John Calambokidis
- Cascadia Research Collective 218 1/2 W. 4th Avenue Olympia Washington 98501
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Hammond TT, Springthorpe D, Walsh RE, Berg-Kirkpatrick T. Using accelerometers to remotely and automatically characterize behavior in small animals. ACTA ACUST UNITED AC 2016; 219:1618-24. [PMID: 26994177 DOI: 10.1242/jeb.136135] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 03/09/2016] [Indexed: 12/25/2022]
Abstract
Activity budgets in wild animals are challenging to measure via direct observation because data collection is time consuming and observer effects are potentially confounding. Although tri-axial accelerometers are increasingly employed for this purpose, their application in small-bodied animals has been limited by weight restrictions. Additionally, accelerometers engender novel complications, as a system is needed to reliably map acceleration to behaviors. In this study, we describe newly developed, tiny acceleration-logging devices (1.5-2.5 g) and use them to characterize behavior in two chipmunk species. We collected paired accelerometer readings and behavioral observations from captive individuals. We then employed techniques from machine learning to develop an automatic system for coding accelerometer readings into behavioral categories. Finally, we deployed and recovered accelerometers from free-living, wild chipmunks. This is the first time to our knowledge that accelerometers have been used to generate behavioral data for small-bodied (<100 g), free-living mammals.
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Affiliation(s)
- Talisin T Hammond
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Dwight Springthorpe
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Rachel E Walsh
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Taylor Berg-Kirkpatrick
- Language Technologies Institute, 5000 Forbes Ave., Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Barbuti R, Chessa S, Micheli A, Pucci R. Localizing Tortoise Nests by Neural Networks. PLoS One 2016; 11:e0151168. [PMID: 26985660 PMCID: PMC4795789 DOI: 10.1371/journal.pone.0151168] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 02/24/2016] [Indexed: 11/30/2022] Open
Abstract
The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.
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Affiliation(s)
- Roberto Barbuti
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Stefano Chessa
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Rita Pucci
- Department of Computer Science, University of Pisa, Pisa, Italy
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Lush L, Ellwood S, Markham A, Ward AI, Wheeler P. Use of tri-axial accelerometers to assess terrestrial mammal behaviour in the wild. J Zool (1987) 2015. [DOI: 10.1111/jzo.12308] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- L. Lush
- Centre for Environmental and Marine Sciences; University of Hull; Scarborough UK
| | - S. Ellwood
- Wildlife Conservation Research Unit; Department of Zoology; University of Oxford; Recanati-Kaplan Centre; Abingdon UK
| | - A. Markham
- Department of Computer Science; University of Oxford; Oxford UK
| | - A. I. Ward
- National Wildlife Management Centre; Animal and Plant Health Agency; York UK
| | - P. Wheeler
- Centre for Environmental and Marine Sciences; University of Hull; Scarborough UK
- Department of Environment, Earth and Ecosystems; The Open University; Milton Keynes UK
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13
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Walker JS, Jones MW, Laramee RS, Holton MD, Shepard ELC, Williams HJ, Scantlebury DM, Marks NJ, Magowan EA, Maguire IE, Bidder OR, Di Virgilio A, Wilson RP. Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags. MOVEMENT ECOLOGY 2015; 3:29. [PMID: 26392863 PMCID: PMC4576376 DOI: 10.1186/s40462-015-0056-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 09/06/2015] [Indexed: 05/02/2023]
Abstract
BACKGROUND Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. DESCRIPTION This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space. CONCLUSIONS Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.
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Affiliation(s)
- James S. Walker
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark W. Jones
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Robert S. Laramee
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark D. Holton
- />College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Emily LC Shepard
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Hannah J. Williams
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - D. Michael Scantlebury
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Nikki, J. Marks
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Elizabeth A. Magowan
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Iain E. Maguire
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Owen R. Bidder
- />Institut für Terrestrische und Aquatische Wildtierforschung, Stiftung Tierärztliche Hochschule, Werfstr. 6, 25761, Hannover, Büsum Germany
| | - Agustina Di Virgilio
- />Laboratorio Ecotono, INIBIOMA-CONICET, National University of Comahue, Quintral 1250, (8400), Bariloche, Argentina
| | - Rory P. Wilson
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
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Bidder O, Arandjelović O, Almutairi F, Shepard E, Lambertucci S, Qasem L, Wilson R. A risky business or a safe BET? A Fuzzy Set Event Tree for estimating hazard in biotelemetry studies. Anim Behav 2014. [DOI: 10.1016/j.anbehav.2014.04.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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15
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Bidder OR, Campbell HA, Gómez-Laich A, Urgé P, Walker J, Cai Y, Gao L, Quintana F, Wilson RP. Love thy neighbour: automatic animal behavioural classification of acceleration data using the K-nearest neighbour algorithm. PLoS One 2014; 9:e88609. [PMID: 24586354 PMCID: PMC3931648 DOI: 10.1371/journal.pone.0088609] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2013] [Accepted: 01/11/2014] [Indexed: 11/19/2022] Open
Abstract
Researchers hoping to elucidate the behaviour of species that aren't readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research.
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Affiliation(s)
| | - Hamish A. Campbell
- School of Biological Sciences, University of Queensland Brisbane, Queensland, Australia
| | - Agustina Gómez-Laich
- Centro Nacional Patagónico - Consejo Nacional de Investigaciones Cientificas y Técnias, Puerto Madryn, Chubut, Argentina
| | - Patricia Urgé
- College of Science, Swansea University, Swansea, Wales
| | - James Walker
- College of Engineering, Swansea University, Swansea, Wales
| | - Yuzhi Cai
- School of Management, Swansea University, Swansea, Wales
| | - Lianli Gao
- School of Information Technology and Electrical Engineering, The University of Queensland Brisbane, Queensland, Australia
| | - Flavio Quintana
- Centro Nacional Patagónico - Consejo Nacional de Investigaciones Cientificas y Técnias, Puerto Madryn, Chubut, Argentina
- Wildlife Conservation Society, Ciudad de Buenos Aires, Argentina
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Campbell HA, Gao L, Bidder OR, Hunter J, Franklin CE. Creating a behavioural classification module for acceleration data: using a captive surrogate for difficult to observe species. ACTA ACUST UNITED AC 2013; 216:4501-6. [PMID: 24031056 DOI: 10.1242/jeb.089805] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Distinguishing specific behavioural modes from data collected by animal-borne tri-axial accelerometers can be a time-consuming and subjective process. Data synthesis can be further inhibited when the tri-axial acceleration data cannot be paired with the corresponding behavioural mode through direct observation. Here, we explored the use of a tame surrogate (domestic dog) to build a behavioural classification module, and then used that module to accurately identify and quantify behavioural modes within acceleration collected from other individuals/species. Tri-axial acceleration data were recorded from a domestic dog whilst it was commanded to walk, run, sit, stand and lie-down. Through video synchronisation, each tri-axial acceleration sample was annotated with its associated behavioural mode; the feature vectors were extracted and used to build the classification module through the application of support vector machines (SVMs). This behavioural classification module was then used to identify and quantify the same behavioural modes in acceleration collected from a range of other species (alligator, badger, cheetah, dingo, echidna, kangaroo and wombat). Evaluation of the module performance, using a binary classification system, showed there was a high capacity (>90%) for behaviour recognition between individuals of the same species. Furthermore, a positive correlation existed between SVM capacity and the similarity of the individual's spinal length-to-height above the ground ratio (SL:SH) to that of the surrogate. The study describes how to build a behavioural classification module and highlights the value of using a surrogate for studying cryptic, rare or endangered species.
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Affiliation(s)
- Hamish A Campbell
- School of Biological Sciences, The University of Queensland Brisbane, QLD 4072, Australia
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