The Relationship Between User Perceptions of Exoskeletons and Changes in Muscle Activity and Range of Motion
Bibliographic record
Abstract
While through the literature different objective metrics, such as kinematics and muscle activity, have been used for the evaluation of exoskeletons performance, there is less research on how these metrics represent users’ perceptions. This study aimed to find the relationships between muscle activity and joint kinematics with user perception. Muscle activity was measured using electromyography sensors, focusing on the Latissimus and Thoracolumbar muscles. Body joint kinematics measurements were taken for the knee and trunk joints. The study also investigated how users’ overall selections of exoskeletons, considering all aspects, align with their choices based on comfort and biomechanical support. A similarity index and point biserial correlation coefficient were used for finding the relationships. Four individuals performed trunk bending and weight lifting while wearing different exoskeleton configurations. Both perceived comfort (similarity index: 57%) and biomechanical support (similarity index: $62.5 \%$) played a role in users’ overall preferences, and users prioritized one of them depending on the exoskeleton setting. Comparing the objective and subjective results revealed that muscle activity represented human perception of support to some extent (average similarity index and correlation coefficient of $\mathbf{4 9 \%}$ and 0.27 across static and dynamic tasks), while trunk range of motion had a high similarity and correlation with users’ perceived comfort (similarity index: $\mathbf{7 4 . 2 \%}$ and correlation coefficient: 0.53). In summary, this study contributed to understanding the rationale behind users’ perception across different aspects. The results highlight the necessity of future research on finding more sensitive objective metrics, leading us toward obtaining the objective function underlying users’ preferences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".