Robot Interactive Motion Design for Enhancing Mental Well-being
Bibliographic record
Abstract
In today's society, characterized by complexity and the pervasive use of technology, individuals often find themselves under constant pressure in various aspects of life, whether at work or school.This continual stress can take a toll on mental well-being, leading to conditions like depression and anxiety.Fortunately, numerous therapies exist to alleviate these stresses, one of which involves the use of mental care robots designed to respond to human emotions.In this paper, we propose an auxiliary remedy for individuals experiencing depression, employing gestures and interactions facilitated by a robotic system.To address the emotional needs of individuals, we utilize a programmable mental care assistant robot named 'MIRO.'Unlike static therapeutic interventions, MIRO offers dynamic and responsive interactions tailored to the user's emotional state.By leveraging its array of exteroceptive sensors, including touch sensors, ultrasonic sonar sensors, and microphones, MIRO can interpret and respond to user input effectively.Furthermore, MIRO incorporates a sound localization scheme, utilizing dual sound sensors to locate and engage with users, akin to the responsiveness of a trained therapy animal.Understanding human emotions is paramount in providing effective support.We employ a valence and arousal model to categorize emotional states, enabling MIRO to adapt its interactions accordingly.When users exhibit signs of sadness or depression, MIRO employs various therapeutic behaviors aimed at alleviating distress.By mimicking the empathetic responses of a companion, MIRO enhances the efficacy of psychological counseling sessions, providing users with a sense of comfort and support.We propose using robots as emotional companions to alleviate depression.Our approach employs MIRO, a programmable robot, which adapts interactions based on user emotions, detected through sensors.MIRO's responsiveness, including sound localization, mimics therapy animals, enhancing psychological counseling effectiveness.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".