Activity Monitoring

Infrared Activity Frames for Measuring Locomotor Activity in Mice and Rats

Capture Every Move

The ActiMot3 module is designed to study voluntary activity in home cages under stress-free conditions in long-term experiments. Our infrared activity frame uses extra-fast infrared sensors for a comprehensive activity analysis in the XYZ axis. With a unique 5mm sensor spacing, it delivers unparalleled spatial resolution, ideal for mouse studies. Our software offers detailed analysis of fine and ambulatory movements, activity differentiation, and over 100 result parameters.

Explore, discover, and elevate your research like never before!

Finest Infrared Technology

Operate under all light conditions – even in complete darkness

Unique sensor spacing of 5mm in mice (1.25mm digital resolution) – no activity is missed

Differentiate between activity & resting periods

XY level detects stationary and ambulatory movement

The Z sensor level monitors rearing and jumping

Integrated lid holder for comfortable change of cages

The ActiMot3 frames are seamlessly integrated with other PhenoMaster modules like drinking and feeding systems, running wheels, operant walls, and indirect calorimetry setups. With all cage components suspended from the lid, movement detection remains uninhibited. Additionally, the ActiMot frames can also be utilized as stand-alone systems for various behavioral tests, including Open Field, Light-Dark, and Hole-Board tests. This versatility allows for comprehensive behavioral analysis across different testing paradigms.

Extensive Analysis

Regions of interest allow zone-specific analyses

Customized analysis – a variety of movement thresholds

More than 100 parameters are calculated from the raw data

Differentiate between activity and resting periods – precious in metabolic studies

Output speed, latencies, rearing, jumping and more

Detailed graphical analyses: actograms, the pattern of movement, histograms, spatial graphs


Buchenauer, L., Haange, S.-B., Bauer, M., Rolle-Kampczyk, U. E., Wagner, M., Stucke, J., Elter, E., Fink, B., Vass, M., von Bergen, M., Schulz, A., Zenclussen, A. C., Junge, K. M., Stangl, G. I., & Polte, T. (2023). Science of The Total Environment, 905, 167034.

Maity-Kumar, G., Ständer, L., DeAngelis, M., Lee, S., Molenaar, A., Becker, L., Garrett, L., Amerie, O. V., Hoelter, S. M., Wurst, W., Fuchs, H., Feuchtinger, A., Gailus-Durner, V., Garcia-Caceres, C., Othman, A. E., Brockmann, C., Schöffling, V. I., Beiser, K., Krude, H., … Müller, T. D. (2022). Molecular Metabolism, 66, 101616.

Milenkovic, D., Misic, J., Hevler, J. F., Molinié, T., Chung, I., Atanassov, I., Li, X., Filograna, R., Mesaros, A., Mourier, A., Heck, A. J. R., Hirst, J., & Larsson, N.-G. (2023). Cell Metabolism, 35(10), 1799-1813.e7.

Pham, T. C. P., Dollet, L., Ali, M. S., Raun, S. H., Møller, L. L. V., Jafari, A., Ditzel, N., Andersen, N. R., Fritzen, A. M., Gerhart-Hines, Z., Kiens, B., Suomalainen, A., Simpson, S. J., Salling Olsen, M., Kieser, A., Schjerling, P., Nieminen, A. I., Richter, E. A., Havula, E., & Sylow, L. (2023). Science Advances, 9(32), eadf7119.

Xie, K., Fuchs, H., Scifo, E., Liu, D., Aziz, A., Aguilar-Pimentel, J. A., Amarie, O. V., Becker, L., da Silva-Buttkus, P., Calzada-Wack, J., Cho, Y.-L., Deng, Y., Edwards, A. C., Garrett, L., Georgopoulou, C., Gerlini, R., Hölter, S. M., Klein-Rodewald, T., Kramer, M., … Ehninger, D. (2022). Nature Communications, 13(1), Article 1.