Assessing the Validity and Reliability of using One Inertial Measurement Unit to Measure Wheelchair Kinematics on Elite Wheelchair Court Sport Athlete
Bibliographic record
Abstract
The measure of wheelchair speed and rotation (kinematics), using Inertial Measurement Units (IMU) can help athletes, coaches and practitioners identify important training paradigms in wheelchair court sports. However, there are several different IMU algorithms currently used. One of these algorithms uses three IMU (3IMU), and is considered the “gold standard” for measurements in the field. It has shown high levels of validity in field testing and incorporates methods to handle common errors, but the cost of outfitting an athlete with three sensor may limit sports from using this technology. An algorithm that uses only one IMU (1IMU) placed on the wheel hub of a wheelchair, and uses the Madgwick filter to measure the orientation the sensor, has been developed to measure the speed and rotation of the chair. While some preliminary validation work on 1IMU outputs has been promising, this algorithm not been rigorously tested. However, if this algorithm proves to be reliable and valid, the cost of measuring wheelchair kinematics for a whole team becomes substantially less. The purpose of this investigation was to further assess the ability of 1IMU to accurately and reliability measure continuous and discrete kinematic data, during basic movement patterns in wheelchair court sports. Elite wheelchair basketball and wheelchair rugby players were recruited, and they performed maximal sprint and agility tests. The validity and the reliability of the 1IMU outputs were measured and compared to 3IMU as well as timing gates and video. Further, the impact on these kinematic outputs because of poor measurements in wheel sizes as a reduction in sampling rates from 200Hz to 25Hz was evaluated. It was determined that 1IMU provides good to excellent levels of agreement with 3IMU and video for most kinematic data. Further, it was determined that sampling rates of 100Hz should be used to ensure that kinematic metrics defining peak linear and rotational speeds are preserved. However, it was also determined that further investigations are required to generate more accurate data for linear speed during turns. Overall, it is recommended that 1IMU can be used in testing protocols to measure wheelchair kinematics.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".