FoG-STAR: Freezing of Gait Severity, Tasks, Activities, and Ratings
Authors/Creators
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BORZI', LUIGI1
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DEMROZI, FLORENC2
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Bacchin, Ruggero3
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Turetta, Cristian4
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Tebaldi, Michele4
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Sigcha, Luis5
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Zolfaghari, Samaneh6
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Rinaldi, Domiziana7
- Fazzina, Giuliana8
- Balestro, Giulio9
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Picelli, Alessandro9
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PRAVADELLI, Graziano4, 10
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OLMO, Gabriella11
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Tamburin, Stefano9
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Lopiano, Leonardo12
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Artusi, Carlo Alberto13
- 1. Politecnico di Torino Dipartimento di Automatica e Informatica
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2.
University of Stavanger
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3.
Ospedale Santa Chiara
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4.
University of Verona
- 5. University of Limerick Faculty of Education and Health Sciences
- 6. Mälardalen University: Västerås, Västmanland, SE
- 7. Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome
- 8. Department of Neuroscience, University of Turin,
- 9. Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona,
- 10. EDALab s.r.l.
- 11. Department of Control and Computer Engineering, Politecnico di Torino,
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12.
University of Turin
- 13. Department of Neuroscience, University of Turin
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:
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sensor_data.csv→ synchronized inertial sensor signals with FoG labels and task annotations -
clinical_data.csv→ subject-level demographic and clinical assessments
📂 Files
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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
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Sensors: Accelerometer (g) + Gyroscope (°/s)
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Positions: Left ankle, Right ankle, Back, Wrist
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Sampling frequency: 60 Hz
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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
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22 subjects with Parkinson’s disease
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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
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Participants: 22 people with PD
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Tasks performed: 7 mobility tasks (see
taskID) designed to elicit FoG -
Annotations: FoG presence and severity labeled by experts via video analysis
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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
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Data exploration: distributions of FoG vs non-FoG, FoG severity, time per task/activity/subject
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FoG event analysis: duration distributions, severity-based comparisons
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Signal visualization: example raw traces with shaded FoG episodes
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Clinical correlation: merge
sensor_datawithclinical_data
🔖 Citation
If you use this dataset, please cite:
⚖️ License
This dataset is licensed under CC-BY 4.0. You are free to share and adapt the data with proper attribution.
Files
clinical_data.csv
Additional details
References
- 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.