Abstract
We demonstrate the feasibility of constructing a novel and practical real-world mobile cloud system, called myBlackBox, that efficiently fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users’ daily lives. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve high accuracy, customization, convenience and scalability. We show the feasibility of myBlackBox by implementing and evaluating this end-to-end system that combines Android smartphones with cloud servers, deployed for 15 users over a one-month period.
| Original language | English |
|---|---|
| Article number | 753 |
| Pages (from-to) | 1-20 |
| Number of pages | 20 |
| Journal | Sensors (Switzerland) |
| Volume | 16 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 2016 |
| Externally published | Yes |
Bibliographical note
Copyright the Author(s) 2016. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Behavior pattern
- Blackbox
- Fusion
- Mobile user
- Unusual event