Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys.

ABSTRACT: Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense manual labor and lacks standardized assessment. In this work, we established two standard benchmark datasets of NHPs in the laboratory: MonkeyinLab (MiL), which includes 13 categories of actions and postures, and MiL2D, which includes sequences of two-dimensional (2D) skeleton features. Furthermore, based on recent methodological advances in deep learning and skeleton visualization, we introduced the MonkeyMonitorKit (MonKit) toolbox for automatic action recognition, posture estimation, and identification of fine motor activity in monkeys. Using t

SUBMITTER: Li C 

PROVIDER: S-EPMC10559098 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature