UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures
Bibliographic record
Abstract
= 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a scale of [0-10]. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated optical motion capture system of 11 cameras that tracks 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless time-of-flight depth sensor that tracks 25 locations of a person׳s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this dataset. This article details the recording protocol as well as the processing and annotation procedures. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) [1] on a part of the dataset to extract morphology-independent motion features for skill evaluation. Results of this analysis are presented in their communication: "Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation" (10.1145/3077981.3078037) [1]. Data are available for research purpose (license CC BY-NC-SA 4.0), at https://github.com/numediart/UMONS-TAICHI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".