DOI: 10.5281/zenodo.16989602
title: FoG-STAR: Freezing of Gait Severity, Tasks, Activities, and Ratings

description:  

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




LICENSE → Dataset license (CC-BY 4.0)




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



📑 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




Column(s)
Name
Description




1
timestamp
Float, timestamp in ms (60 Hz)


2–25
Sensor signals
Format: [position]_[sensor]_[axis]. Positions = ankleL, ankleR, back, wrist; Sensor = acc (g), gyro (°/s); Axis = x,y,z


26
activity
Motor activity code: 1=Walking, 2=Sit, 3=Stand, 4=Sit-to-Stand, 5=Stand-to-Sit, 6=Turn Right, 7=Turn Left


27
fog
Binary FoG label: 0=No FoG, 1=FoG


28
fog_severity
Severity during FoG: 1=Shuffling, 2=Trembling, 3=Akinesia


29
subjectID
Subject identifier (1–22), link to clinical_data.csv


30
sessionID
Recording session ID (usually 1, but >1 if multiple recordings were needed)


31
taskID
Task code: 1=Timed Up-and-Go, 2=Stand 1min, 3=Walk back/forth, 4=Walk+Doorway, 5=Walk+Water, 6=Walk+Count, 7=360° turn




📑 2. Clinical Data (clinical_data.csv)

Population





22 subjects with Parkinson’s disease




Each row corresponds to one subject (linked via subjectID)



Column definitions




Column
Variable
Description




1
subjectID
Subject ID (1–22), matches sensor_data.csv


2
age
Age in years


3
gender
Gender (M/F)


4
disease_duration
Years since PD diagnosis


5
h_y
Hoehn & Yahr stage (0–5, higher = more advanced PD)


6
updrs_iii
MDS-UPDRS Part III score (0–76, higher = worse motor impairment)


7
fog_q
Freezing of Gait Questionnaire (0–24, higher = more severe FoG)


8
moca
Montreal Cognitive Assessment (0–30, lower = worse cognition)


9
fes_i
Falls Efficacy Scale–International (16–64, higher = more fear of falling)


10
pdq_8
Parkinson’s Disease Questionnaire–8 (0–32, higher = poorer QoL)




👩‍⚕️ 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.