ProprioSuite

Overview

ProprioSuite is an open-source package designed for two proprioceptive testing paradigms: the pointing task and the weight discrimination task. The pointing task measures proprioception, while the weight discrimination task measures whether we can discriminate between different weights. This project aims to provide a standardized pipeline for the intricate data analysis following these tests, especially in scenarios where others may conduct the same tests and require analysis.

For instructions on how the tasks are performed, see their documentation:

Pointing Task

Weight Discrimination Task

Features

• An independent 2D smoothing module with the most commonly used smoothing methods (see instructions)
• Modular statistical analysis for raw data from the tests.
• Publicly available database for subjects without a known neurological condition.
• Comparison baseline for others to analyze their data and compare subjects to the normal range.
• Performance report generation.
• Modules: PointingTask and WeightDiscrimination.

Application in Industry

This project is primarily targeted at healthcare and somatosensory research.

Technical Details

• Programming Language: Python
• Dependencies: List of packages (to be updated later)
• Architecture: The package consists of two modules - PointingTask and WeightDiscrimination. Each module contains analyses, figures, comparison tools, and a performance report generator specific to its task.

Getting Started

Prerequisites

• Python (preferably 3.6 or higher)
• Ensure you have pip and setuptools installed. Most Python installations already include them, but if not, you can install them using:

pip install pip setuptools --upgrade

Installation

You can install the package directly from GitHub using pip:

pip install git+https://github.com/yurisugano/ProprioSuite.git

After the installation, you can import the package in your Python code:

import ProprioSuite

Usage

ProprioSuite contains a module with several smoothing methods for 2D data, which can useful in different applications. This module offers various smoothing methods to refine your data, including Moving Average, Weighted Moving Average, Exponential Moving Average, Gaussian Smoothing, Savitzky Golay, and Median Filter.

Clone this repository following the instructions above, make sure to install all dependencies

pip install scipy numpy pandas matplotlib

Sample data should a 2D numpy array where:

First column: Time

Second column: X coordinate

Third column: Y coordinate

data = np.array([
    [0.0, -0.778, -0.475],
    [0.04, -0.811, -0.449],
    ...
])

smoothed_data_object = smooth_data(data, MovingAverage, window_size=100)

You can then retrieve the smoothed data with the method .get_data() and see the results of smoothing with .show_plot()

smoothed_data = smoothed_data_object.get_data()
smoothed_data_object.show_plot()

Project Status

The codebase is complete, but the project is in the process of being organized into a package for publication. All analyses can currently be performed using the scripts locally.

Milestones

• Raw data analysis
• Feature extraction
• Comparison to normal range
• Performance reports

Collaboration

Open to collaborations. If interested in using the package for tests and analysis, please reach out for a smooth integration and direct bug resolution.

Additional Information

• Publicly available, unidentifable data on 60 subjects without a known neurological condition.
• Raw data details:
  • PointingTask: Finger position across time database with time intervals (typically 1/30 s for 30fps recordings), x and y position.
  • WeightDiscrimination: See the sample data

For more information, questions, or collaborations, visit my home page.