ArticlePloS one2016
A Markerless 3D Computerized Motion Capture System Incorporating a Skeleton Model for Monkeys.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 45 citations in OpenAlex.
- Realistic monkey body animation reveals an uncanny valley in macaque body perception.PLoS biology · 2026Article
- Three-dimensional markerless motion capture of multiple freely behaving monkeys toward automated characterization of social behavior.Science advances · 2025Article
- OpenLabCluster: active learning based clustering and classification of animal behaviors based on kinematic body keypoints.Frontiers in systems neuroscience · 2025Article
- Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.PLoS computational biology · 2024Article
- A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior.eNeuro · 2023Article
- Non-human primate models and systems for gait and neurophysiological analysis.Frontiers in neuroscience · 2023Review
- Automated Video-Based Analysis Framework for Behavior Monitoring of Individual Animals in Zoos Using Deep Learning-A Study on Polar Bears.Animals : an open access journal from MDPI · 2022Article
- MouseVenue3D: A Markerless Three-Dimension Behavioral Tracking System for Matching Two-Photon Brain Imaging in Free-Moving Mice.Neuroscience bulletin · 2022Article
- Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography.Nature communications · 2022Article
- Chemogenetic activation of nigrostriatal dopamine neurons in freely moving common marmosets.iScience · 2021Article
- Computer Vision Applied to Detect Lethargy through Animal Motion Monitoring: A Trial on African Swine Fever in Wild Boar.Animals : an open access journal from MDPI · 2020Article
- Article
- Article
- MacaquePose: A Novel "In the Wild" Macaque Monkey Pose Dataset for Markerless Motion Capture.Frontiers in behavioral neuroscience · 2020Article
- Circuit Investigations With Open-Source Miniaturized Microscopes: Past, Present and Future.Frontiers in cellular neuroscience · 2019Review
- DeepLabCut: markerless pose estimation of user-defined body parts with deep learning.Nature neuroscience · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we propose a novel markerless motion capture system (MCS) for monkeys, in which 3D surface images of monkeys were reconstructed by integrating data from four depth cameras, and a skeleton model of the monkey was fitted onto 3D images of monkeys in each frame of the video. To validate the MCS, first, estimated 3D positions of body parts were compared between the 3D MCS-assisted estimation and manual estimation based on visual inspection when a monkey performed a shuttling behavior in which it had to avoid obstacles in various positions. The mean estimation error of the positions of body parts (3-14 cm) and of head rotation (35-43°) between the 3D MCS-assisted and manual estimation were comparable to the errors between two different experimenters performing manual estimation. Furthermore, the MCS could identify specific monkey actions, and there was no false positive nor false negative detection of actions compared with those in manual estimation. Second, to check the reproducibility of MCS-assisted estimation, the same analyses of the above experiments were repeated by a different user. The estimation errors of positions of most body parts between the two experimenters were significantly smaller in the MCS-assisted estimation than in the manual estimation. Third, effects of methamphetamine (MAP) administration on the spontaneous behaviors of four monkeys were analyzed using the MCS. MAP significantly increased head movements, tended to decrease locomotion speed, and had no significant effect on total path length. The results were comparable to previous human clinical data. Furthermore, estimated data following MAP injection (total path length, walking speed, and speed of head rotation) correlated significantly between the two experimenters in the MCS-assisted estimation (r = 0.863 to 0.999). The results suggest that the presented MCS in monkeys is useful in investigating neural mechanisms underlying various psychiatric disorders and developing pharmacological interventions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.