Predicting injury propensity in track and field sports by integrating a deep architecture with posture estimation and sequence modeling

Overview IPSNInjuryPropensity is a lightweight, research-oriented PyTorch implementation inspired by the concepts of the Integrated Posture Sequence Network (IPSN), Injury Propensity Prediction Model (IPPM), and Adaptive Injury Prediction Strategy (AIPS).It analyzes athlete motion sequences to estimate injury risk by combining spatial posture features with temporal motion modeling. The framework integrates: A CNN backbone for spatial posture feature extraction An RNN/LSTM module for temporal sequence modeling A multi-task prediction head for injury propensity and movement classification An adaptive thresholding strategy to generate dynamic risk alerts Features CNN + RNN/LSTM architecture for integrated posture and sequence modeling Binary injury propensity prediction and multi-class movement classification Variable-length sequence support with padding and masks Modular dataset layer for generic motion/kinematic features Multi-task training with weighted composite loss Evaluation utilities for accuracy and macro F1 Simple adaptive threshold-based alert generation Methodology Integrated Posture Sequence Network (IPSN) IPSN applies a two-stage encoding process: A posture encoder (CNN) extracts spatial representations from per-frame posture features A sequence encoder (RNN/LSTM) captures temporal dependencies and motion evolution across frames This design enables joint learning of spatial cues and temporal patterns linked to injury risk. Injury Propensity Prediction Model (IPPM) Based on the sequence representation from IPSN, IPPM includes two prediction heads: A binary injury head using a sigmoid output A movement classification head using a softmax output Joint multi-task learning improves representation quality and supports more robust injury prediction. Adaptive Injury Prediction Strategy (AIPS) AIPS introduces adaptive mechanisms: Gradient-based parameter updating as new data arrives A dynamic risk threshold τ that triggers alerts when injury propensity is high Integration of multimodal contextual signals to form a risk embedding In this repository, the adaptive behavior is reflected as standard training plus a user-configurable risk threshold during inference. Datasets The original research leverages four complementary datasets: Dataset Type Description APAD Images/Videos Posture analysis with joint annotations TFIR Tabular Training load and injury event records SSM Temporal Sequences Long kinematic motion sequences DAT Multimodal Vision, inertial, and physiological data Training Configuration A simplified but flexible training setup includes: Component Default Optimizer AdamW Learning rate 1e-3 Weight decay 1e-4 Batch size 64 Epochs 50 Loss Weighted sum of BCE (injury) + CE (movement) Metrics Accuracy, Macro F1 Repository Structure File Description train.py Training entry point model.py CNN–RNN backbone with multi-task heads dataset.py Dataset loader and collate utilities utils.py Seeding, devices, metrics, checkpoints inference.py Prediction and alert generation Results Summary Based on the referenced research: Spatial posture models outperform typical CNN/Transformer baselines Adding temporal modeling (IPSN Full) improves F1 by ~2–3% Combining AIPS + multi-task learning yields the best performance: ~0.93 F1 on in-domain datasets ~0.91 F1 on cross-domain sets (SSM, DAT) The model demonstrates strong robustness and transferability. Applications Injury risk monitoring for athletes Coaching decision support for training optimization Automated detection of high-risk movement patterns Research in posture sequence modeling and sports analytics Future Work Integration with full posture-estimation pipelines More advanced adaptive/online learning strategies Incorporating contextual inputs (e.g., surface, weather, workload) Model compression and deployment for real-time edge use Extension to other sports and multi-athlete interactions License A permissive license such as MIT or Apache 2.0 is recommended.