Robotic Identification of Kinesthetic Deficits After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Kinesthesia, the sense of body motion, is essential to proper control and execution of movement. Despite its importance for activities of daily living, no current clinical measures can objectively measure kinesthetic deficits. The goal of this study was to use robotic technology to quantify prevalence and severity of kinesthetic deficits of the upper limb poststroke. METHODS: Seventy-four neurologically intact subjects and 113 subjects with stroke (62 left-affected, 51 right-affected) performed a robot-based kinesthetic matching task with vision occluded. The robot moved the most affected arm at a preset speed, direction, and magnitude. Subjects were instructed to mirror-match the movement with their opposite arm (active arm). RESULTS: A large number of subjects with stroke were significantly impaired on measures of kinesthesia. We observed impairments in ability to match movement direction (69% and 49% impaired for left- and right-affected subjects, respectively) and movement magnitude (42% and 31%). We observed impairments to match movement speed (32% and 27%) and increased response latencies (48% and 20%). Movement direction errors and response latencies were related to clinical measures of function, motor recovery, and dexterity. CONCLUSIONS: Using a robotic approach, we found that 61% of acute stroke survivors (n=69) had kinesthetic deficits. Additionally, these deficits were highly related to existing clinical measures, suggesting the importance of kinesthesia in day-to-day function. Our methods allow for more sensitive, accurate, and objective identification of kinesthetic deficits after stroke. With this information, we can better inform clinical treatment strategies to improve poststroke rehabilitative care and outcomes.
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) | 0.001 | 0.001 |
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".