Evaluating the User Experience of Exercising Reaching Motions With a Robot That Predicts Desired Movement Difficulty
Why this work is in the frame
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Bibliographic record
Abstract
The notion of an optimal difficulty during practice has been articulated in many areas of cognitive psychology: flow theory, the challenge point framework, and desirable difficulties. Delivering exercises at a participant's desired difficulty has the potential to improve both motor learning and users' engagement in therapy. Motivation and engagement are among the contributing factors to the success of exercise programs. The authors previously demonstrated that error amplification can be used to introduce levels of challenge into a robotic reaching task, and that machine-learning algorithms can dynamically adjust difficulty to the desired level with 85% accuracy. Building on these findings, we present the results of a proof-of-concept study investigating the impacts of practicing under desirable difficulty conditions. A control condition with a predefined random order for difficulty levels was deemed more suitable for this study (compared to constant or continuously increasing difficulty). By practicing the task at their desirable difficulties, participants in the experimental group perceived their performance at a significantly higher level and reported lower required effort to complete the task, in comparison to a control group. Moreover, based on self-reports, participants in the experimental group were willing, on average, to continue the training session for 4.6 more training blocks (∼45 min) compared to the control group's average. This study demonstrates the efficiency of delivering the exercises at the user's desired difficulty level to improve the user's engagement in exercise tasks. Future work will focus on clinical feasibility of this approach in increasing stroke survivors' engagement in their therapy programs.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 it