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Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning


Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations—mutations that are associated with autism—on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions.

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Fig. 1: Tracking method and validation.
Fig. 2: Automatically labelled behaviours and an example of their representation.
Fig. 3: Behavioural profiles of Shank2−/− mice and Shank3−/− mice.
Fig. 4: Group of three and four dynamics for Shank2−/−, Shank3−/− and their respective wild-type littermate mice.
Fig. 5: Hyperactivity and atypical exploration strategy in Shank2−/− and Shank3−/− female mice.

Data availability

The authors declare that all the data supporting the findings of this study are available within the paper and Supplementary Information. Full datasets (databases and films) generated during and/or analysed during the current study are available in the LMT website repository,

Code availability

Full source code is available at This includes Java code and also CAD hardware resource files. Python analysis scripts are available at


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The authors thank Y. Archambeau and P. Ollivon at the workshop of the Institut Pasteur for building the first 12 setups and advising on hardware, W. Meiniel for the mathematical proof for decisions of head/tail probability, Microsoft France for their technical support, P. Spinicelli for optical engineering and reading of the paper, R. Marée for machine learning support, B. König for advice and reading of biological experiments, J. N. Crawley for reading and providing comments on the manuscript, A. Barmpoutis for providing us with the early Kinect 2 driver and support, N. Chenouard for driving the use of the machine learning solution, P. Dugast for drawing the mice in the different behavioural events, A. Engelberg for checking the English, S. Wagner and R. Accardi for RFID advice, M. Marim for website development, and X. Montagutelli and M. Bérard for animal facility support. This work was partially funded by the Institut Pasteur, the Bettencourt-Schueller Foundation, the Cognacq–Jay Foundation, the Conny–Maeva Foundation, the ERANET–NEURON SYNPATHY program, the Agence Nationale de la Recherche through grant number ANR-10-LABX-62-IBEID, France-BioImaging infrastructure through grant number ANR-10-INBS-04 and the INCEPTION program through grant number ANR-16-CONV-0005, the Centre National de la Recherche Scientifique, the University Paris Diderot, the BioPsy Labex, the Institut National du Cancer through grant number TABAC-16–022, the Foundation for Medical Research (Equipe DEQ20130326488), the Innovative Medicines Initiative Joint Undertaking through grant agreement number 115300, resources of which are composed of financial contributions from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies in kind contribution. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information




F.d.C. created the tracking and analysis methods, developed the open-hardware setup and software. E.E. performed and analysed experiments. N.T. analysed experiments. S.D. provided real-time programming advice. T. Lagache provided the probabilistic framework for machine learning decision-making. T. Legou provided electronic support. A.I. created the blueprints. A.-M.L.S. performed the genotyping of the mice. F.d.C., E.E., N.T., P.F., T.B. and J.-C.O.-M. conceived the project and wrote the manuscript.

Corresponding authors

Correspondence to Fabrice de Chaumont or Elodie Ey or Jean-Christophe Olivo-Marin.

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Competing interests

The authors declare no competing interests.

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Supplementary Information

Supplementary Information

Supplementary figures, tables, methods, results, video captions and references.

Reporting Summary

Assembly instructions

Assembly instructions for the mouse live tracker.

Supplementary Video 1

The different steps of the method, and a general overview of the system.

Supplementary Video 2

3D animations of the assembly, and how to perform the calibration of the system.

Supplementary Video 3

A case study of a typical problem: a nest that is built by the animal at an unexpected location.

Supplementary Video 4

Background height map (a view of the cage without the animals).

Supplementary Video 5

Live 3D rendering demo.

Supplementary Video 6

Data used to validate the method.

Supplementary Video 7

How the system copes with different appearances, and also with a mix of animals having different coat colours.

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de Chaumont, F., Ey, E., Torquet, N. et al. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning. Nat Biomed Eng 3, 930–942 (2019).

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