gaitsetpy 0.2.2

A Python package for gait analysis using sensor data.

GaitSetPy ✨

GaitSetPy is a Python package for gait analysis and recognition. It provides clean, modern APIs to preprocess, analyze, extract features, classify, and visualize gait data across multiple datasets and modalities.

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📚 Table of Contents

• Features
• Supported Datasets
• Installation
• Quickstart
• Examples: Notebooks and Scripts
• Usage by Dataset (Code Snippets)
  • Daphnet
  • HAR-UP
  • UrFall
  • PhysioNet (VGRF)
  • MobiFall
  • Arduous
• Models
• Visualization
• Documentation
• Contributing
• License
• Citation
• Changelog

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Features

• Gait data preprocessing
• Feature extraction (generic and dataset-specific)
• Machine learning and deep learning models for recognition
• Exploratory data analysis and visualization tools
• Class-based API with legacy compatibility

Supported Datasets

IMU Sensor Based

• Daphnet: https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait
• MobiFall: https://bmi.hmu.gr/the-mobifall-and-mobiact-datasets-2/
• HAR-UP (formerly UPFall): https://sites.google.com/up.edu.mx/har-up/
• UrFall: https://fenix.ur.edu.pl/~mkepski/ds/uf.html
• Activity Net - Arduous: https://www.mad.tf.fau.de/research/activitynet/wearable-multi-sensor-gait-based-daily-activity-data/

Pressure/Force Sensor Based

• PhysioNet Gait in Parkinson's Disease: https://physionet.org/content/gaitpdb/1.0.0/

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Installation

From PyPI:

pip install gaitsetpy

From source:

git clone https://github.com/Alohomora-Labs/gaitSetPy.git
python setup.py install

Optionally, also install requirements:

pip install -r requirements.txt

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Quickstart

import gaitsetpy as gsp

# Daphnet: Load, window, extract features, visualize
loader = gsp.DaphnetLoader()
data, names = loader.load_data("data/daphnet")
windows = loader.create_sliding_windows(data, names, window_size=192, step_size=32)
extractor = gsp.GaitFeatureExtractor()
features = extractor.extract_features(windows[0]['windows'], fs=64)

from gaitsetpy.eda import SensorStatisticsAnalyzer
analyzer = SensorStatisticsAnalyzer()
analyzer.visualize(windows[0]['windows'], features, sensor_name="shank", start_idx=0, end_idx=1000, num_windows=15)

# Train & evaluate a Random Forest
rf = gsp.RandomForestModel(n_estimators=50, random_state=42, max_depth=10)
rf.train(features)
metrics = rf.evaluate(features)
print(metrics.get('accuracy'))

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Examples: Notebooks and Scripts

• Notebooks (examples/notebooks):
  • Daphnet_demo.ipynb, HARUP_demo.ipynb, PhysioNet_demo.ipynb, UrFall_demo.ipynb
  • Open with Jupyter: jupyter notebook examples/notebooks/HARUP_demo.ipynb
• Scripts (examples/scripts):
  • daphnet_all_models_example.py, harup_example.py, physionet_example.py, urfall_example.py
  • Run via Python: python examples/scripts/harup_example.py

These examples mirror the latest APIs and are maintained alongside code updates.

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Usage by Dataset (Code Snippets)

Below are minimal, copy-pasteable examples for each supported dataset.

Daphnet

import gaitsetpy as gsp
loader = gsp.DaphnetLoader()
data, names = loader.load_data("data/daphnet")
windows = loader.create_sliding_windows(data, names, window_size=192, step_size=32)
features = gsp.GaitFeatureExtractor().extract_features(windows[0]['windows'], fs=64)

HAR-UP

import gaitsetpy as gsp
loader = gsp.HARUPLoader()
harup_data, harup_names = loader.load_data("data/harup")
windows = loader.create_sliding_windows(harup_data, harup_names, window_size=100, step_size=50)
# Dataset-specific feature extraction
features_data = loader.extract_features(windows)

UrFall

import gaitsetpy as gsp
loader = gsp.UrFallLoader()
# Load pre-extracted depth-map features
data, names = loader.load_data("data/urfall", data_types=['features'], use_falls=True, use_adls=True)
windows = loader.create_sliding_windows(data, names, window_size=30, step_size=15)
# Filepaths for media modalities
video_paths = loader.get_file_paths("data/urfall", 'video')
depth_paths = loader.get_file_paths("data/urfall", 'depth', sequences=['fall-01'])
print(loader.get_activity_info())
print(loader.get_feature_info())

PhysioNet (VGRF)

import gaitsetpy as gsp
loader = gsp.PhysioNetLoader()
data, names = loader.load_data("data/physionet")
windows = loader.create_sliding_windows(data, names, window_size=600, step_size=100)
features = gsp.PhysioNetFeatureExtractor().extract_features(windows[0]['windows'], fs=100)
labels = loader.get_labels()

MobiFall

import gaitsetpy as gsp
loader = gsp.MobiFallLoader()
# Depending on your local dataset layout
data, names = loader.load_data("data/mobifall")
windows = loader.create_sliding_windows(data, names, window_size=256, step_size=64)
# Use generic gait feature extractor
features = gsp.GaitFeatureExtractor().extract_features(windows[0]['windows'], fs=50)

Arduous

import gaitsetpy as gsp
loader = gsp.ArduousLoader()
# Depending on ActivityNet/Arduous export and local layout
data, names = loader.load_data("data/arduous")
windows = loader.create_sliding_windows(data, names, window_size=256, step_size=64)
features = gsp.GaitFeatureExtractor().extract_features(windows[0]['windows'], fs=50)

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Models

• RandomForestModel (built-in, scikit-learn)
• Optional deep learning models (LSTM, BiLSTM, CNN, GNN) if PyTorch is installed

Factory access via gaitsetpy.classification.models.get_classification_model(name, **kwargs).

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Visualization

• gaitsetpy.eda.SensorStatisticsAnalyzer
• Dataset-specific visualizers for exploratory analysis

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Documentation

For detailed documentation and API reference, visit the official docs:

• Docs: https://alohomora-labs.github.io/gaitSetPy/gaitsetpy.html

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Contributing

We welcome contributions! Please read our contributing guidelines to get started.

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License

This project is licensed under the GNU GPL License. See the LICENSE file for details.

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Citation

If you use GaitSetPy in your research, please cite our work using the DOI badge above.

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Changelog

• 0.2.2: README overhaul (ToC, vibrant styling), per-dataset examples, notebooks/scripts documentation, version sync.
• See GitHub Releases for older entries.