Cognition-Aware Object Detection for Physical Education

# CogniAthlete: Integrating Psychological State Modeling into Object Detection for Physical Education ## 🧠 Overview **CogniAthlete** is a novel computational framework designed to enhance object detection tasks in physical education by incorporating **psychological state modeling**. Traditional methods often overlook mental conditions like attention, fatigue, motivation, or stress. CogniAthlete bridges this gap through **multi-modal learning**, **introspective decoding**, and a **perturbation-aware training strategy** to enable more personalized, robust, and interpretable systems. --- ## 📌 Key Features - 🎯 **Psychological-Aware Detection:** Embeds mental states directly into object detection pipelines using latent cognitive-affective modeling.- 🔀 **Multimodal Encoder:** Integrates biometric, kinematic, environmental, and vocal data.- 🧩 **Graphical Propagation Layer:** Models temporal evolution of psychological states (e.g., motivation, fatigue, attention).- 🔄 **Mental Perturbation Decoding (MPD):** Simulates internal disruptions like stress spikes to improve model robustness.- 🪞 **Introspective Decoding:** Offers behavioral predictions and interpretable attributions from psychological latent space.- 💡 **Flow-State Recognition:** Detects cognitive "flow" moments for real-time training adaptation.- 🧬 **Subject Personalization:** Embeds athlete-specific factors for individualized feedback. --- ## 🚀 Architecture The core architecture consists of: - A **Transformer-based multi-modal encoder** that captures diverse input signals.- A **psychologically structured latent state processor** with gated updates.- A **domain-specific decoding strategy** to explain predictions through latent factors.- An **MPD training loop** that enhances resilience to psychological variability (see Figures 1–4 in the paper). --- ## 📊 Benchmark Performance | Dataset | Accuracy | F1 Score | AUC ||---------------|----------|----------|--------|| LibriSpeech | **88.92%** | 87.59% | 90.23% || TIMIT | 89.44% | **87.12%** | 91.02% || Common Voice | **85.96%** | 84.18% | 88.03% || TED-LIUM | 87.20% | **85.92%** | 89.45% | 🏅 *Outperforms SOTA methods (YOLOv5, Faster R-CNN, RetinaNet) across clean and noisy conditions.* --- ## 🧪 Datasets Used - [LibriSpeech](https://www.openslr.org/12/)- [TIMIT](https://catalog.ldc.upenn.edu/LDC93S1)- [Common Voice](https://commonvoice.mozilla.org/)- [TED-LIUM v3](https://www.openslr.org/51/) --- ## 🛠️ Implementation Details - Framework: PyTorch + Fairseq- Features: 80-dim log-mel spectrograms- Text Tokenization: BPE (10k vocab)- Hardware: 4× NVIDIA A100 GPUs- Optimization: Beam Search + Dropout + Curriculum Sampling --- ## 🧪 How to Run ```bashgit clone https://github.com/<your-username>/CogniAthlete.gitcd CogniAthletepip install -r requirements.txt # Train with MPDpython train.py --config configs/cogniathlete.yaml # Evaluate on LibriSpeechpython evaluate.py --dataset librispeech --checkpoint checkpoints/best_model.pth