FST.ai 2.0: Explainable AI for Taekwondo Officiating

• FST.ai 2.0 is an explainable, real-time AI ecosystem for Olympic and Paralympic Taekwondo that integrates pose-based recognition, uncertainty modeling, and interpretable decision overlays.
• It leverages advanced pose estimation via spatial–temporal GCNs and Monte Carlo dropout to achieve high classification accuracy and dramatically reduce decision latency.
• Interactive dashboards combined with governance-aware design provide transparent performance assessments and foster referee trust with accountable, human-in-the-loop overrides.

FST.ai 2.0 is an explainable, real-time artificial intelligence ecosystem designed to facilitate fair, fast, and inclusive decision-making in Olympic and Paralympic Taekwondo. The framework incorporates pose-based action recognition, epistemic uncertainty modeling, and interpretable decision overlays to support referees, coaches, and athletes during competition and training scenarios. It distinguishes itself through modular design, governance-aware analytics, and comprehensive experimental validation, establishing new standards for transparent and accountable officiating and athlete assessment.

1. Pose-Based Action Recognition

FST.ai 2.0 processes live match footage using advanced pose estimation algorithms such as OpenPose and HRNet to extract skeletal keypoints of athletes. These keypoints are structured into graphs, enabling the application of spatial–temporal Graph Convolutional Networks (ST-GCNs). The input to the network is a tensor $\mathbf{X} \in \mathbb{R}^{T \times N \times C}$, where $T$ is the temporal length (number of frames), $N$ is the number of joints, and $C$ represents the features per joint.

Each layer of the ST-GCN updates node embeddings as:

$$\mathbf{H}^{(l+1)} = \sigma\left( \sum_{k=0}^{K} A_k \cdot \mathbf{H}^{(l)} \cdot W_k \right)$$

where $A_k$ are normalized adjacency matrices reflecting anatomical and temporal relationships, $W_k$ are learnable parameters, and $\sigma(\cdot)$ is a nonlinear activation.

This graph-based approach enables classification of Taekwondo actions (e.g., valid head kicks) with clear confidence scores. For example, after softmax, the output $[0.15, 0.80, 0.05]$ may indicate 80% confidence in the class “valid head kick.” The method is flexible for handling varying input sizes and missing joint data, particularly benefiting Para-Taekwondo athlete scenarios.

2. Epistemic Uncertainty Modeling

FST.ai 2.0 integrates explicit uncertainty quantification to ensure reliability in high-stakes contexts. The system captures both aleatoric uncertainty (from noisy sensor data or ambiguous views) and epistemic uncertainty (reflecting model ignorance in rare or borderline cases).

Epistemic uncertainty is modeled using approximate Bayesian inference—specifically, Monte Carlo Dropout. During inference, the network performs $M$ stochastic forward passes, aggregating predictive mean and variance for outcome $f(x)$:

$$\mathbb{E}[f(x)] \approx \frac{1}{M} \sum_{m=1}^M f_{\theta_m}(x), \quad \text{Var}[f(x)] \approx \frac{1}{M} \sum_{m=1}^M \left(f_{\theta_m}(x)\right)^2 - \left(\mathbb{E}[f(x)]\right)^2$$

For critical cases, particularly Para-Taekwondo classification, the system provides interval-valued probabilities via credal sets:

$P \in [\underline{P}, \overline{P}]$

where the bounds represent the minimum and maximum plausible probabilities. This enables automatic flagging of decisions with high uncertainty for human review, enhancing fairness and decision safety.

3. Explainability Overlays

To address transparency, FST.ai 2.0 generates explainability overlays using methods such as Grad-CAM and transformer-based attention maps. Grad-CAM creates heatmaps over input frames that visualize key regions influencing the model’s prediction:

$$L_{\text{Grad-CAM}}^c = \text{ReLU}\left( \sum_{k} \alpha_k^c A^k \right)$$

where $A^k$ is the $k$th feature map, $\alpha_k^c$ is the gradient importance for class $c$, and $\text{ReLU}$ ensures only positive contributions are shown.

Overlays are superimposed onto video data to highlight relevant body parts involved in scoring actions, such as the kicking leg and opponent’s head zone. Attention maps produced by transformer modules show which joints and sequence segments are most relevant. This visual rationale is presented to referees and coaches, making the decision process interpretable and facilitating human-machine collaboration.

4. Interactive Dashboards

FST.ai 2.0 incorporates interactive dashboards for real-time and retrospective evaluation. For referees, dashboards display metrics such as decision latency, scoring consistency, and breakdowns of contested events. Latency is computed as:

$$\Delta t_{ij} = t^{\text{score}}_{ij} - t^{\text{kick}}_{ij}, \quad \bar{\Delta t}_i = \frac{1}{n_i} \sum_{j=1}^{n_i} \Delta t_{ij}$$

enabling quantification of system responsiveness.

Dashboards for coaches and athletes provide longitudinal analyses of performance—including technique consistency, scoring rates, and replay with uncertainty overlays. For Para-Taekwondo, special dashboard modules handle automatic classification, mapping kinematic features and deep learning embeddings to predicted athlete classes and uncertainty measures.

These platforms allow filtering and comparative analysis across athletes, techniques, events, and referee decisions, supporting detailed performance assessment and governance.

5. Experimental Validation

Empirical validation during pilot deployments (e.g., 2025 World Cadet Championships) supports several operational claims:

• Decision review time decreased from ~89 seconds (manual/IVR) to 4.6 seconds (AI-assisted), representing an 85–95% reduction.
• Referee trust in AI decisions reached 93%, attributed to transparency from overlays.
• Classification accuracy exceeded 90%, with jury overrides reduced by 42%.
• System latency remained low, typically below 300 ms end-to-end, supporting live use.

These results were achieved with real-time edge deployment (NVIDIA RTX GPUs), human-in-the-loop workflows for override and annotation, and rigorous uncertainty modeling. The outcomes demonstrate substantial improvements in officiating speed, trust, and transparency.

6. Governance-Aware Design

FST.ai 2.0 embeds governance features to promote fairness and accountability across all system interactions. Decision logs record timestamp, input features, model outputs, uncertainty metrics, and override status:

$$\text{AuditLog}_i = \{ t_i, \mathbf{x}_i, \hat{y}_i, \mathcal{H}_i, \text{DecisionFlow}_i \}$$

Human-in-the-loop override gates ensure that referees always retain final control:

$$y^{\text{final}} = \begin{cases} y_\text{AI}, & \text{if } \mathcal{O}_j = 0 \ y_\text{human}, & \text{if } \mathcal{O}_j = 1 \end{cases}$$

Systematic fairness monitoring is performed using disparity metrics across demographic groups:

$$\text{Disparity}_{i,j} = \left| \mathbb{E}[\hat{y} \mid G_i] - \mathbb{E}[\hat{y} \mid G_j] \right|$$

and retraining is automatically triggered when disparities exceed set thresholds.

All data is processed in compliance with privacy regulations (e.g., GDPR), ensuring safe handling and access control.

7. Significance and Outlook

FST.ai 2.0 defines a comprehensive, modular AI ecosystem for sporting officiating, integrating advanced pose-based recognition, uncertainty quantification, explainability methods, and governance-aware workflows. Experimental evidence demonstrates dramatic reductions in decision latency and high referee trust. Built-in human-in-the-loop and fairness mechanisms support accountable, inclusive AI decision-making in both Olympic and Paralympic contexts. The framework is extensible to long-term athlete assessment, referee training, and policy analytics, marking a substantial advancement toward equitable, human-aligned sport AI.