README – FoG-STAR: Freezing of Gait Severity, Tasks, Activities, and Ratings 

📌 Overview

This dataset contains wearable inertial sensor recordings and clinical/demographic information collected from 22 people with Parkinson’s disease.
It is designed to support research on Freezing of Gait (FoG) detection, severity estimation, activity recognition, and digital biomarkers.

The dataset is organized in two CSV files:

• sensor_data.csv → synchronized inertial sensor signals with FoG labels and task annotations

• clinical_data.csv → subject-level demographic and clinical assessments

📂 Files

• sensor_data.csv → Sensor-based recordings (31 columns, sampled at 60 Hz)

• clinical_data.csv → Demographic and clinical metadata (10 variables × 22 subjects)

• README.md → This documentation

• README.txt → Readme file with summarized information

• LICENSE → Dataset license (CC-BY 4.0)

• FoG-Star_Analytics.ipynb → Example Python utilities for generating statistics and figures

• fogstar_environment.yaml→ Conda environment specification to execute the scripts

📑 1. Sensor Data (sensor_data.csv)

Recording setup

• Sensors: Accelerometer (g) + Gyroscope (°/s)

• Positions: Left ankle, Right ankle, Back, Wrist

• Sampling frequency: 60 Hz

• Recording context: Motor tasks designed to elicit or challenge gait

Column definitions

📑 2. Clinical Data (clinical_data.csv)

Population

• 22 subjects with Parkinson’s disease

• Each row corresponds to one subject (linked via subjectID)

Column definitions

👩‍⚕️ Study Protocol

• Participants: 22 people with PD

• Tasks performed: 7 mobility tasks (see taskID) designed to elicit FoG

• Annotations: FoG presence and severity labeled by experts via video analysis

• Clinical scales: Hoehn & Yahr, MDS-UPDRS III, FoG-Q, MoCA, FES-I, PDQ-8

📊 Example Usage

import pandas as pd

# Load data
df_sensors = pd.read_csv("fog_star.csv")
df_clinical = pd.read_csv("clinical_data.csv")

# Merge datasets
df = df_sensors.merge(df_clinical, on="subjectID")

# Example: Average FoG severity per subject
print(df.groupby("subjectID")["fog_severity"].mean())

# Example: Correlation between UPDRS-III score and FoG proportion
fog_ratio = df.groupby("subjectID")["fog"].mean().reset_index()
merged = fog_ratio.merge(df_clinical, on="subjectID")
print(merged[["subjectID","fog","updrs_iii"]])

📈 Provided Scripts

• Data exploration: distributions of FoG vs non-FoG, FoG severity, time per task/activity/subject

• FoG event analysis: duration distributions, severity-based comparisons

• Signal visualization: example raw traces with shaded FoG episodes

• Clinical correlation: merge sensor_data with clinical_data

🔖 Citation

If you use this dataset, please cite:

Borzi, L. et al. Freezing of Gait Wearable Sensor and Clinical Dataset. Zenodo, 2025. DOI: 10.5281/zenodo.16989602

Borzi, L. et al.. Freezing of gait detection: The effect of sensor type, position, activities, datasets, and machine learning model. Journal of Parkinson’s Disease, 15(1), 163-181.

Demrozi, F. et al. "A low-cost wireless body area network for human activity recognition in healthy life and medical applications." IEEE Transactions on Emerging Topics in Computing 11, no. 4 (2023): 839-850.

⚖️ License

This dataset is licensed under CC-BY 4.0. You are free to share and adapt the data with proper attribution.