Comparison of Optical and Inertial Tracking of Full Golf Swings
Bibliographic record
Abstract
We compare optical tracking with inertial tracking of a golf club for full golf swings. Previous work on tracking golf clubs with inertial measurement units (IMUs) was limited to putting applications [1]. Using a Vicon optical motion capture system and a commercial micro-electromechanical-system (MEMS) IMU, we performed simultaneous optical and inertial measurements of golf swings by one of our varsity golf team members. The IMU and five optical tracking markers were attached to a club shaft just under the grip. The position and orientation data of the grip can be used with a dynamics simulation of the club shaft and head to yield information about the club face launch conditions at the moment of ball impact [2]. Vicon marker position data, and IMU accelerations and rotation rates, were converted into laboratory frame position and orientation data for the grip of the club and compared with one another. The results point to challenges with commercial-grade IMUs that use MEMS for tracking golf swings. Strapdown inertial navigation is very sensitive to errors in the rotation rate and acceleration sensors. Due to the large dynamic ranges involved in a golf swing, and the poor noise, stability, and dynamic range of the MEMS IMUs, high-fidelity tracking of the 6-degree-of freedom grip motion could not be achieved. We suggest that a way to overcome these challenges could be by including magnetometer data with an extended Kalman filter to provide the necessary accuracy in orientation to reconstruct the position of the club accurately in three dimensions.
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 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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".