EMG Pattern Recognition Control of the DEKA Arm: Impact on User Ratings of Satisfaction and Usability
Bibliographic record
Abstract
The DEKA Arm has multiple degrees of freedom which historically have been operated primarily by inertial measurement units (IMUs). However, the IMUs are not appropriate for all potential users; new control methods are needed. The purposes of this study were: 1) to describe usability and satisfaction of two controls methods-IMU and myoelectric pattern recognition (EMG-PR) controls-and 2) to compare ratings by control and amputation level. A total of 36 subjects with transradial (TR) or transhumeral (TH) amputation participated in the study. The subjects included 11 EMG-PR users (82% TR) and 25 IMU users (68% TR). The study consisted of in-laboratory training (Part A) and home use (Part B). The subjects were administered the Trinity Amputation and Prosthesis Experience satisfaction scale and other usability and satisfaction measures. Wilcoxon rank-sum tests compared the differences by control type. The differences were compared for those who did and did not want a DEKA Arm. The preferences for features of the DEKA Arm were compared by control type. The comparisons revealed poorer ratings of skill, comfort, and weight among EMG-PR users. The TR amputees using IMUs rated usability more favorably. TH amputees rated usability similarly. The TR amputees using EMG-PR were less satisfied with weight, pinch grip, and wrist display, whereas the TH amputees were less satisfied with the full system, wires/cables, and battery. Usability and satisfaction declined after Part B for EMG-PR users. Overall, we found that the IMU users rated the DEKA Arm and the controls more favorably than the EMG-PR users. The findings indicate that the EMG-PR system we tested was less well accepted than the IMUs for control of the DEKA Arm.
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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".