[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125579-en":3,"doc-seo-125579-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125579,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Quantifying allogrooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning","Quantifying activity budgets is pivotal for understanding how animals respond to environmental change. Social grooming supports multiple social processes with health and fitness consequences, yet traditional focal observations are sparse and limited in wild settings. Tri-axial accelerometers enable continuous activity monitoring, but social grooming had not been quantified this way. Machine learning, trained on labelled acceleration data from wild chacma baboons, accurately identifies giving and receiving grooming and reveals distinct acceleration patterns.","Downloaded from [https://royalsocietypublishing.org/ on 13 April 2023](https://royalsocietypublishing.org/ on 13 April 2023)  \n[royalsocietypublishing.org/journal/rsos](royalsocietypublishing.org/journal/rsos)  \nResearch  \nCite this article: Christensen C, Bracken AM, O’Riain MJ, Fehlmann G, Holton M, Hopkins P, King AJ, Fürtbauer I. 2023 Quantifying allogrooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning. R. Soc. Open Sci. 10: 221103 .  \n[https://doi.org/10.1098/rsos.221103](https://doi.org/10.1098/rsos.221103)  \nReceived: 22 September 2022  \nAccepted: 15 March 2023  \nSubject Category:  \nOrganismal and Evolutionary Biology  \nSubject Areas: biomechanics/behaviour  \nKeywords:  \nmachine learning, tri-axial accelerometers, random forest models, allo-grooming, activity budgets, primates  \nAuthor for correspondence: Charlotte Christensen  \ne-mail: charlotte.christensen@uzh.ch  \nElectronic supplementary material is available online at [https://doi.org/10.6084/m9.figshare.c](https://doi.org/10.6084/m9.figshare.c). 6495471.  \nQuantifying allo-grooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning  \n\n| Charlotte Christensen1,2, Anna M. Bracken1,3,\u003Cbr>M. Justin O’Riain4, Gaëlle Fehlmann5, Mark Holton 1, Phillip Hopkins1, Andrew J. King 1 and Ines Fürtbauer 1 |\n| --- |\n| 1Faculty of Science and Engineering, Swansea University, Swansea SA2 8PP, UK |\n| 2Department of Evolutionary Biology and Environmental Science, University of Zurich, |\n| Zurich 8057, Switzerland\u003Cbr>3School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, |\n| Glasgow G12 8QQ, UK\u003Cbr>4Institute for Communities and Wildlife in Africa, Department of Biological Science, |\n| University of Cape Town, Rondebosch, 7701, South Africa\u003Cbr>5Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany\u003Cbr> CC, 0000-0001-7697-9903; AMB, 0000-0002-5183-3139; MJO'R, 0000-0001-5233-8327; GF, 0000-0001-7981-5728; MH, 0000-0001-8834-3283; PH, 0009-0005-6570-6236;\u003Cbr>AJK, 0000-0002-6870-9767; IF, 0000-0003-1404-6280 |\n| Quantification of activity budgets is pivotal for understanding how animals respond to changes in their environment. Social grooming is a key activity that underpins various social processes with consequences for health and fitness. Traditional methods use direct (focal) observations to calculate grooming rates, providing systematic but sparse data. Accelerometers, in contrast, can quantify activity budgets continuously but have not been used to quantify social grooming. We test whether grooming can be accurately identified using machine learning (random forest model) trained on labelled acceleration data from wild chacma baboons (Papio ursinus) . We successfully identified giving and receiving grooming with high precision (81% and 91%) and recall (87% and 79%). Giving grooming was associated with a distinct rhythmical signal along the surge axis. Receiving grooming had similar acceleration signals to resting, and thus was more difficult to assign. We applied our machine learning model to n = 680 collar data days from n = 12 baboons and found that grooming rates obtained from accelerometers were significantly and positively correlated with |\n| \u003Cbr>© 2023 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, provided the original author and source are credited. |\n\nDownloaded from [https://royalsocietypublishing.org/ on 13 April 2023](https://royalsocietypublishing.org/ on 13 April 2023)  \ndirect observation rates for giving but not receiving grooming. The ability to collect continuous grooming data in wild populations will allow researchers to re-examine and expand upon long-standing questions regarding the formation and function of grooming bonds.  \n1 . Introduction  \nHow anima","cbCaiaE7BHej2yhn","https://ap.wps.com/l/cbCaiaE7BHej2yhn","pdf",1115074,1,18,"English","en",105,"# Introduction\n## Allogrooming and its role in social bonding\n## Limits of traditional behavioural observation\n## Accelerometer-based approaches and research gap","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"The study aims to quantify allo-grooming in wild chacma baboons using tri-axial acceleration data combined with machine learning.\"},{\"question\":\"How is grooming identified from accelerometer data?\",\"answer\":\"A random forest machine learning model is trained on labelled acceleration data, enabling automated identification of giving and receiving grooming.\"},{\"question\":\"What performance was achieved for identifying giving versus receiving grooming?\",\"answer\":\"The model identified giving grooming with high precision (81%) and recall (87%), while receiving grooming showed precision of 91% and recall of 79%.\"}]","Quantifying allogrooming in wild chacma baboons (Papio ursinus) using tri-axial acceleration data and machine learning | 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