Task-oriented and Purposeful Robot-Assisted Therapy
Bibliographic record
Abstract
This chapter discussed the development of a task-oriented therapy robot focused on real ADL training and performance. The development of the software, HERALD, and the hardware platform and FES grasp glove has been discussed. We also presented training and trajectory planning models that have been implemented with the system along with defining the pros and cons of three possible models that can be used to accurately reflect natural movement of the wrist during ADL task. We presented three case studies that briefly examined how the system has repeatable performance and has the ability to train stroke survivors. A low functioning stroke survivor was successfully trained on the system using PTP ADL-like movement. The subject's kinematics, especially movement time and movement smoothness decreased reflecting motor impairment reduction and increased motor control. The ADL functioning was improved on tasks involving more shoulder and elbow function but not on task involving grasp. In the future, the FES glove will be fully integrated in the ADLER system to allow stroke clients on all levels of motor function and ADL ability to be trained in both reaching and grasping ADLs. As such we anticipate that a major impact will then be seen on both motor impairment and functional scales. We also presented via case study 2 different trajectory models for planning and assisting movement of the wrist using ADLER. We demonstrated that models that included curvature and had customized inputs can improve a subject's movement kinematics for an ADL tasks such as drinking and can affect their perception of the ease or difficulty of the movement. In the case study performed on the ADLER system, the subject reported a "more natural" feel when operating with the new model rather than the old model. This shows that the model appears to meet the goal of providing a more natural prediction of functional
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".