Contact Part Detection From 3D Human Motion Data Using Manually Labeled Contact Data and Deep Learning
Bibliographic record
Abstract
Research on the interaction between users and their environment has been conducted in various fields, including human activity recognition (HAR), human-scene interaction (HSI), computer graphics (CG), and virtual reality (VR). Typically, the interaction process commences with a human body part’s movement and involves contact with a target object or the environment. The choice of the body part to make contact depends on the interaction’s purpose and affordance, making contact a fundamental aspect of interaction. However, detecting the specific body parts in contact, especially in the context of 3D motion and complex environments, poses computational challenges. To address this challenge, this study proposes a method for contact detection using motion data. The motion data utilized in this study are limited to actions feasible in an office environment. Since contact states of different body parts are independent, the proposed method comprises two distinct models: a feature model generating common features for each body part and a part model recognizing the contact state of each body part. The feature model employs a bidirectional long-short term memory(Bi-LSTM) structure to capture the sequential nature of motion data, ensuring the incorporation of continuous data characteristics. In contrast, the part model employs separate weights optimized for each body part within the deep neural network. Experimental results demonstrate the proposed method’s high accuracy, recall, and precision, with values of 0.99, 0.97, and 0.95, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".