https://github.com/fhstp/intellevent
IntellEvent is a robust and accurate overground gait event detection algorithm for various pathologies. Here you can find a pipeline for Vicon Nexus to implement in your routine which automatically detects initial contact and foot off events during walking.
https://github.com/fhstp/intellevent
gait-analysis gait-event-detection machine-learning vicon-nexus
Last synced: 21 days ago
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IntellEvent is a robust and accurate overground gait event detection algorithm for various pathologies. Here you can find a pipeline for Vicon Nexus to implement in your routine which automatically detects initial contact and foot off events during walking.
- Host: GitHub
- URL: https://github.com/fhstp/intellevent
- Owner: fhstp
- License: cc-by-4.0
- Created: 2022-12-07T15:53:19.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-24T09:35:37.000Z (4 months ago)
- Last Synced: 2025-03-31T18:51:18.636Z (about 2 months ago)
- Topics: gait-analysis, gait-event-detection, machine-learning, vicon-nexus
- Language: Python
- Homepage:
- Size: 43.4 MB
- Stars: 8
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IntellEvent
## Introduction
*IntellEvent* is a robust deep learning-based framework for gait event detection across various pathologies for 3D motion capture data. By leveraging deep learning models, *IntellEvent* accurately detects gait events (initial contact (IC) and foot off (FO)) in patients with different clinical conditions, including malrotation deformities and/or frontal malalignments of the lower extremities, club foot, cerebral palsy, drop foot, and healthy participants. *IntellEvent* ensures reliable and precise gait events even in complex pathological cases (IC: < 5.5 ms @150 Hz, FO: < 11.4 ms @150 Hz ). For more detailed information, refer to the original paper: [Robust deep learning-based gait event detection across various pathologies](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288555).
## Dataset
The dataset used for *IntellEvent* consists of a comprehensive retrospective clinical 3D gait analysis (3DGA) dataset:
- **Total Subjects**: 1211 patients and 61 healthy controls
### Categories
- **Malrotation deformities of the lower limbs (MD)**: 730 subjects
- **Club foot (CF)**: 120 subjects
- **Cerebral palsy (CP)**: 344 subjects
- **Cerebral palsy with only drop foot characteristics (DF)**: 17 subjects
- **Healthy controls (HC)**: 61 subjects
## Marker Inputs
- The current model for IC detection uses the anterior-posterior (plane of motion) and vertical velocity of the left and right `HEEL`, `ANKLE`, and `TOE` markers.
- The current model for FO detection uses the anterior-posterior (plane of motion), medio-lateral, and vertical velocity of the left and right `HEEL`, `ANKLE`, and `TOE` markers.
# Requirements
**This framework has been tested with Vicon Nexus version 2.14 and higher. No installation required!**
If you would like to use Vicon Nexus 2.12.1, please get in touch, we will find a solution.
# Vicon Nexus Usage
1) Download the `25_IntellEvent.zip` folder from the release **[here](https://github.com/fhstp/IntellEvent/releases/download/v2.0/v2.0_IntellEvent.zip)**.
2) Extract the files to a folder of your choice. **Note:** All files must be loacated in the same folder.
3) Start the `vicon_server.exe`.
4) Create a new `Run Python Operation` in a Vicon Nexus pipeline from the operation `Data Processing` tab. Add the `vicon_intellevent.py` to the `Python script file` path.
5) Run the pipeline and save time!
# Future Developments
- **Integrating Further Movement Tasks**:
- Turning
- Running
- **Ensuring Robustness for Different Laboratory Settings**:
- Standardize data preprocessing from multiple laboratory sources
- Utilize data from different labs
- **Integrating Fine-Tuning Pipeline**
- **Implement a Pipeline for Continuous Refinement and Optimization**
# Current Results
The current model achieves the following Mean Absolute Errors (MAE) in milliseconds for different pathologies:
| Category | MD | CF | DF | CP | HC |
|-----------|-------|-------|-------|-------|-------|
| **IC MAE [ms]** | 2.7 | 3.5 | 5.4 | 4.9 | 2.5 |
| **FO MAE [ms]** | 7.9 | 8.7 | 9.9 | 11.3 | 8.3 |
# Citation
If you are using *IntellEvent* in your research we would appreciate a citation.
> [1] B. Dumphart et al., ‘Robust deep learning-based gait event detection across various pathologies’, *PLOS ONE*, vol. 18, no. 8, p. e0288555, Aug. 2023, doi: 10.1371/journal.pone.0288555.
```
@article{dumphartRobustDeepLearningbased2023,
title = {Robust Deep Learning-Based Gait Event Detection across Various Pathologies},
author = {Dumphart, Bernhard and Slijepcevic, Djordje and Zeppelzauer, Matthias and Kranzl, Andreas and Unglaube, Fabian and Baca, Arnold and Horsak, Brian},
year = {2023},
journal = {PLOS ONE},
volume = {18},
number = {8},
pages = {e0288555},
publisher = {{Public Library of Science}},
issn = {1932-6203},
doi = {10.1371/journal.pone.0288555},
keywords = {Algorithms,Cerebral palsy,Feet,Gait analysis,Machine learning algorithms,Neural networks,Recurrent neural networks,Toes}
}
```
# Contact
If you need any help, have further ideas, or have questions regarding *IntellEvent* please feel free to contact me!
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[email protected]
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[email protected]
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[email protected]
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# License
Creative Commons Attribution 4.0 International Public License