gaitsetpy 0.1.4

A Python package for gait analysis using sensor data.

GaitSetPy

GaitSetPy is a Python package for gait analysis and recognition. This package provides tools and algorithms to process and analyze gait data, enabling researchers and developers to build applications for gait recognition and clinical gait assessment.

Features

• Gait data preprocessing
• Feature extraction
• Gait recognition algorithms
• Visualization tools

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 Sensor Based

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

Installation

You can install GaitSetPy using pip:

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

Optionally, also install requirements

pip install -r requirements.txt

Usage

Here is a simple example to get you started with GaitSetPy:

Daphnet Dataset Example

import gaitsetpy as gsp

# Load gait data
daphnet, names = gsp.load_daphnet_data("")

# Preprocess data
sliding_windows = gsp.create_sliding_windows(daphnet, names)
freq = 64
# Extract features
features = gsp.extract_gait_features(sliding_windows[0]['windows'], freq, True, True, True)

# Visualize gait features
gsp.plot_sensor_with_features(sliding_windows[0]['windows'], features, sensor_name="shank", num_windows=15)

HAR-UP Dataset Example

import gaitsetpy as gsp

# Load HAR-UP data
data_dir = "data/harup"
harup_data, harup_names = gsp.load_harup_data(data_dir)

# Create sliding windows
window_size = 100  # 1 second at 100Hz
step_size = 50     # 0.5 second overlap
windows = gsp.create_harup_windows(harup_data, harup_names, window_size, step_size)

# Extract features
features_data = gsp.extract_harup_features(windows)

# For more advanced usage, see examples/harup_example.py

# Train a Random Forest
rf_model = gsp.RandomForestModel(n_estimators=50, random_state=42, max_depth=10)
rf_model.train(features)

# Load a pretrained model
rf_model.load_pretrained_weights("random_forest_model_40_10.pkl")

# Evaluate Model
gsp.evaluate_model(rf_model.model, features) # Assuming 'rf_model' is your trained RandomForestModel instance

Documentation

For detailed documentation and API reference, please visit the official documentation.

Contributing

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

License

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

Contact

For any questions or inquiries, please contact us at [e-mail] or [e-mail].