Abstract Animals change their location in space by means of walking, running, swimming, or flying, a series of rhythmic motor behaviours that together are defined as locomotion. Individual types of locomotion require a unique coordinated pattern of muscle contractions that can be inferred by the location of joints across the body. Implementations of recent advancements in machine learning (ML), such as DeepLabCut and Simi Shape, have automated the tracking of body posture, even in markerless subjects. Despite ML algorithms alleviating the tracking effort, making sense of the tracked points still requires substantial amounts of manual labour and lacks standardisation across research labs. To this end, we developed AutoGaitA (Automated Gait Analysis), an open-source Python toolbox designed to automate the analysis of locomotion by normalising the step cycle, extracting meaningful features from the tracked coordinates (e.g. angles, velocity, acceleration) and allowing intra- and inter-animal comparisons. Here, we employed AutoGaitA in a series of proof of principles experiments to show age-dependent changes in locomotion in flies, mice and humans, age-dependent changes in adaptation mechanisms in mice, and to compare the key features of walking across species. AutoGaitA ’s adaptability to any kind of motor behaviour and any species of interest makes it a valuable tool for the motor community to standardise the analysis of rhythmic behaviours across genotypes, disease states and species.

AutoGaitA - Automated Gait Analysis in Python