FitBit_Steps provides minute-level step counts recorded by wearable devices for multiple users. The dataset, authored by Mobius and sourced from OpenML, contains user IDs, timestamps, and step counts for precise activity tracking. It is released under a CC0-1.0 license.
Use Cases
- Analyzing health and fitness trends based on minute-level step count variations.
- Researching circadian rhythms of physical activity based on timestamped activity data.
- Developing gamified fitness challenges based on granular user step records.
- Building machine learning models to predict future physical activity levels based on historical minute-level data.
Strengths
- Data is detailed down to the minute, offering a microscopic view of activity patterns.
- Includes unique user identifiers, enabling analysis across multiple individuals.
- Timestamps follow a precise M/D/YYYY H:MM:SS AM/PM structure for exact activity pinpointing.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent for any fields beyond the three described; field semantics must be inferred after download.
- Last update date is unknown; freshness unverified.
Provenance
- Source
- Mobius via OpenML
- Collection Method
- Likely collected from wearable Fitbit devices.
- Time Range
- null
- Freshness
- null
- Geography
- null