# Psychomotor Skill Benchmarking Eval

> Evaluates the objective quantification and benchmarking of psychomotor execution quality in sports using wearable IMU data. It maps raw 3D motion trajectories into a normalized performance space and uses unsupervised clustering to identify optimal movement patterns and detect technical deviations. Use when the user wants to benchmark on Table Tennis Forehand Stroke (IMU), or asks about evaluating this task. Reports Euclidean distance to ideal performance origin.

- Skill: `qhjqhj00/psychomotor-skill-benchmarking-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/psychomotor-skill-benchmarking-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/psychomotor-skill-benchmarking-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/psychomotor-skill-benchmarking-eval

---


# psychomotor-skill-benchmarking-eval

> Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport — Pandukabhaya et al. (2024) (arXiv:2411.16168, 2024)

## What this evaluates

Evaluates the objective quantification and benchmarking of psychomotor execution quality in sports using wearable IMU data. It maps raw 3D motion trajectories into a normalized performance space and uses unsupervised clustering to identify optimal movement patterns and detect technical deviations.

## Datasets

- **Table Tennis Forehand Stroke (IMU)** — total 18; splits: test (-1)

## Metrics

- `Euclidean distance to ideal performance origin` **(primary)** — range: other
  - Computes the Euclidean distance between a cluster centroid in the 5D performance space and the origin of ideal performance O' ≡ (1, 1, 1, 1, 1). Lower distance indicates closer alignment with optimal psychomotor execution.
- `Mean Euler angle variation` — range: other
  - Calculates the average yaw, pitch, and roll angles across the wrist, elbow, and shoulder joints for each cluster to visualize and compare movement kinematics against the benchmark.

## Input / output format

**Input**: Time-series 3D motion data (Euler angles) captured by wearable IMU sensors attached to the wrist, elbow, and shoulder during table tennis forehand strokes.

**Output**: Cluster assignments for each stroke, centroid coordinates in the 5D performance space, and mean Euler angle trajectories per cluster.

## Scoring recipe

```python
# 1. Normalize raw IMU trajectories into 5D performance space using RBF cost functions
perf_space = [rbf(cost_func, trajectory) for trajectory in trajectories]
# 2. Cluster performance space (e.g., k-means with K=4)
clusters = kmeans(perf_space, k=4)
# 3. Compute centroid for each cluster
centroids = [mean(cluster_points) for cluster_points in clusters]
# 4. Calculate Euclidean distance to ideal origin O'=(1,1,1,1,1)
distances = [euclidean_dist(c, [1,1,1,1,1]) for c in centroids]
# 5. Identify benchmark cluster as the one with minimum distance
benchmark_cluster_idx = argmin(distances)
```

## Common pitfalls

- The benchmark cluster is identified unsupervised via proximity to an idealized origin, not through expert-labeled 'perfect' strokes.
- Cost functions and performance space dimensions are highly domain-specific to table tennis forehand strokes and may not transfer to other sports without redefinition.

## Evidence (verbatim from paper)

> After selecting the 4 primary clusters, the centroid of each cluster was calculated using (19). Subsequently, the distance from the centroid of each cluster to the origin of ideal performance O' ≡ (1, 1, 1, 1, 1) in the performance space was calculated using (20). The results are summarized in Table 5. With the results obtained in Table 5 for the mean Euclidean distances, cluster q = 0 was identified as the benchmark cluster with the lowest distance of d_0 = 0.6284 from O' considering (21).

## Citation

```bibtex
@misc{pandukabhaya2024performance,
  title={Performance Benchmarking of Psychomotor Skills Using Wearable Devices: An Application in Sport},
  author={Pandukabhaya et al. (2024)},
  year={2024},
  note={arXiv:2411.16168}
}
```

- arXiv: 2411.16168