Engagement Detection Framework for Hand Gesture and Posture Recognition
Bibliographic record
Abstract
Hand gesture and posture recognition play an important role in Human-Computer Interaction (HCI) applications. They are main attributes in object or environment manipulations using vision-based inter- faces. However, before interpreting these gestures and postures as operational activities, a meaningful involvement with the target object should be detected. This meaningful involvement is called engagement. Upper-body posture gives significant information about user engagement. In this research, for our first contribution, a novel multi-modal model for engagement detection, called Disengagement, Attention, Intention, Action (DAIA) framework is presented. Disengagement happens when the user is disengaged from the target object. Attention occurs when user pays attention to the target, but doesn't have the intention to take any actions. In Intention state, the user intends to perform an action, but still does not. Action state is when the user is performing an action with hand. Using DAIA, the spectrum of mental status for performing a manipulative action is quantized in a finite number of engagement states. The second contribution of this research is in designing multiple binary classifiers based on upper-body postures for state detection. 3D skeleton data is extracted from depth image and is used to extract body posture information. Combining the output of all binary classifiers in an order makes engagement feature vector. Moreover, This feature vector could be extended using other channels of biometric information such as voice or gaze. However the engagemnet classifiers recognize the state change with acceptable accuracy, minor changes in body postures or false detection of joint locations for some milliseconds may result in transition to another states. For removing this unwanted noise and increasing the accuracy of the system, an Finite State Machine (FSM) is designed based on the properties of human activities. The design of Engagement FSM is our third major contribution. Finally, rotation matrix is used to increase the number of samples for training the deep learning classifier for hand posture recognition.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".