Quantification of Lower Extremity Kinesthesia Deficits Using a Robotic Exoskeleton in People With a Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Our ability to sense movement is essential for motor control; however, the impact of kinesthesia deficits on functional recovery is not well monitored in the spinal cord injury (SCI) population. One problem is the lack of accurate and reliable tools to measure kinesthesia. OBJECTIVE: The purpose of this study was to establish the validity and reliability of a quantitative robotic assessment tool to measure lower limb kinesthesia in people with SCI. METHODS: Seventeen individuals with an incomplete SCI and 17 age-matched controls completed 2 robotic-based assessments of lower limb kinesthesia sense, separated by at least 1 week. The Lokomat, a lower limb robotic exoskeleton, was used to quantify the movement detection score bilaterally for the hip and knee joints. Four passive movement speeds (0.5, 1.0, 2.0, and 4.0 deg/s) were applied in both flexion and extension directions. Participants responded via pressing a joystick button when movement was felt. RESULTS: The movement detection score was significantly greater in people with SCI compared with the control group, particularly at the slowest movement speed. The difference between groups was more pronounced among those classified as ASIA (American Spinal Injury Association) Impairment Scale B. Our measure showed high test-retest reliability and good internal consistency for the hip and knee joints. CONCLUSIONS: Our findings demonstrated that lower limb kinesthesia deficits are common in the SCI population and highlighted the importance of valid and reliable tools to monitor sensory function. Future studies need to examine changes in sensory function in response to therapy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".