Joystick stiffness, movement speed and direction effects on upper limb muscular loading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The manipulation of joysticks to control heavy machinery requires repetitive wrist and upper limb movements which can increase operator susceptibility to repetitive strain injuries. The purpose of this study was to analyse muscle activation using surface electromyography (EMG) on eight muscles of the upper limb during joystick manipulation. Experiments (n=8 subjects) involved a series of 4 motion types (forward, backwards, inwards, outwards) at 2 speeds (fast, slow) using 3 identical joysticks with different stiffnesses (light, regular, heavy). Results showed that all experimental conditions required at least a constant low level (between 2–5% Task Maximal Voluntary Contraction) activation for all muscles. The joystick utilized in this study maintains the wrist in a more neutral posture, however, Integrated EMG (iEMG) and peakEMG results suggest that the muscle strain is transferred from the wrist to the shoulder. EMG results also suggest that shoulder strain is further exacerbated by the armrest as it forces the operator to elevate the shoulder while pulling the controller backwards and inadequately supporting the forearm while moving it in the forward direction. Muscles involved as prime movers had higher activation levels when joystick stiffness was increased, however, muscles that provided directional, positional or postural support to the prime movers were relatively unaffected by joystick stiffness. Muscle activation was increased for all muscles when the joystick was moved quickly. This finding may be important for work environments using joysticks which require increased precision and fine movements coupled with short, highly repetitious cycle times.
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.
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 it