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Residential wearable RSSI and accelerometer measurements with detailed location annotations
Dallan Byrne1, Michal Kozlowski1, Raul Santos-Rodriguez1
1SPHERE, Electronic Engineering Digital Health, University of Bristol, Office 1.31, Merchants Venturer Building, Bristol BS81UB, UK.
Scientific Data
|August 22, 2018
Summary
This study provides wearable accelerometer and Radio Signal Strength Indicator (RSSI) data from four UK homes to test indoor localization methods. The dataset enables evaluation of activity recognition and precise location tracking using low-cost hardware.
Area of Science:
- Computer Science
- Ubiquitous Computing
- Signal Processing
Background:
- Accurate indoor localization remains a challenge, particularly in smart-home environments.
- Wearable sensors offer a promising avenue for collecting activity and location data.
- Existing datasets may lack the resolution or naturalistic context required for robust evaluation.
Purpose of the Study:
- To present a novel dataset for evaluating Radio Signal Strength Indicator (RSSI)-based indoor localization.
- To facilitate the assessment of activity recognition using wearable accelerometer data.
- To provide high-resolution, annotated data from real-world smart-home settings.
Main Methods:
- Collected data using low-cost hardware in four UK homes.
- Utilized wrist-worn accelerometers for activity signature capture.
- Employed a camera-based system with fiducial floor tags for automatic, high-resolution location annotation.
- Recorded both scripted and unscripted daily living activities.
Main Results:
- Acquired approximately 14 hours of annotated wearable measurements.
- Data includes both fingerprinting measurements and naturalistic activity recordings.
- Provided access codes for data retrieval and ground-truthing procedure replication.
Conclusions:
- The dataset enables robust evaluation of RSSI-based indoor localization techniques.
- It supports research into integrating activity recognition with location tracking.
- The naturalistic data collection method enhances the ecological validity of smart-home research.
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