DOI: 10.5281/zenodo.17668273
title: Predicting injury propensity in track and field sports by integrating a deep architecture with posture estimation and sequence modeling

description: 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.