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Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer
Maolin Liu1, Huaiyu Li2,3, Yuan Wang4
1Institute of Remote Sensing and GIS, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China. maolin@pku.edu.cn.
Sensors (Basel, Switzerland)
|April 5, 2018
Summary
Smartphone barometers can now accurately recognize vertical motion, like using stairs or elevators, achieving 95.05% accuracy. This new method improves recognition by analyzing pressure signals and handling imbalanced data effectively.
Area of Science:
- Sensor fusion and signal processing for human motion recognition.
- Mobile sensing and ubiquitous computing applications.
- Machine learning for pattern recognition in time-series data.
Background:
- Traditional smartphone sensors (accelerometers, gyroscopes, magnetometers) have limited accuracy for vertical motion detection.
- Barometers offer improved vertical motion sensing but lack quantitative signal analysis and modeling.
- Imbalanced data presents a challenge for accurate motion recognition algorithms.
Purpose of the Study:
- To develop and validate a method for accurate vertical motion recognition using smartphone barometers.
- To address the limitations of existing sensors and data imbalance issues in motion recognition.
- To provide a robust strategy for enhancing vertical motion detection in multi-floor environments.
Main Methods:
- Modeling and feature extraction of barometer pressure signals using a novel double-windows approach.
- Implementation of a random forest classifier with a correlation rule to mitigate imbalanced data effects.
- Quantitative analysis of pressure signals for distinguishing vertical movements like stair climbing and elevator use.
Main Results:
- Achieved a recognition accuracy of 95.05% for vertical motion recognition using barometer data and an improved random forest classifier.
- Significantly enhanced accuracy and response time for recognizing stair and elevator movements.
- Demonstrated the effectiveness of the proposed double-windows feature extraction and classifier in improving vertical motion detection.
Conclusions:
- Smartphone barometer-based vertical motion recognition is highly accurate and effective, particularly for multi-floor navigation.
- The proposed methods successfully address challenges of sensor limitations and imbalanced datasets.
- This work provides a robust and accurate strategy for improving vertical motion recognition, enhancing mobile sensing capabilities.
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