Leveraging GCN-based Action Recognition for Teleoperation in Daily Activity Assistance
Bibliographic record
Abstract
Caregiving for older adults is an urgent global challenge, with many preferring to age in place rather than enter residential care. However, providing adequate home-based assistance is difficult, particularly in geographically vast regions. Teleoperated robots offer a promising solution, but conventional motion-mapping teleoperation imposes unnatural movement constraints, causing operator fatigue and reducing usability. This paper presents a novel teleoperation framework that leverages action recognition for intuitive remote robot control. A simplified Spatio-Temporal Graph Convolutional Network (S-ST-GCN) recognizes human actions and executes preset robot trajectories, eliminating the need for direct motion synchronization. A finite-state machine (FSM) further enhances reliability by filtering misclassified actions. Experiments demonstrate that the framework enables effortless operator movement and accurate robot execution. This proof-of-concept study highlights the potential of action-recognition-based teleoperation to help caregivers remotely assist older adults with daily activities. Future work will focus on improving the S-ST-GCN’s recognition accuracy and generalization, integrating advanced motion planning techniques for greater robot autonomy, and conducting user studies to evaluate telepresence and usability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".