Name
Abstract | ||
|---|---|---|
Interacting with smart devices is a common experience and is becoming an integral part of daily life for many people. Modern smart devices are equipped with a large variety of environmental and user input sensors. We hypothesize that a fusion of smart device sensor data can provide biometric data that allows for classification of user demographics such as age, gender, and native language. A smart device is instrumented with sensor data collection software and with user demographic classification software. An experiment is devised where data is collected for a sample group of users. The data is analyzed, and two classification algorithms are implemented based on the fusion of the different sensors. The classification methods are based upon decision tree and principle component analysis. The results of the experiment indicate that high accuracy is achieved for user demographic classification. Finally, we further discuss the applications and limitations of the study's approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICMLA.2015.16 | 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) |
Keywords | Field | DocType |
demographic classification,principle component analysis,decision tree | Data collection,Data mining,Decision tree,Smart device,Computer science,Software,Demographics,Biometric data,Statistical classification | Conference |
Citations | PageRank | References |
1 | 0.37 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adel R. Alharbi | 1 | 1 | 0.71 |
| Mitchell A. Thornton | 2 | 280 | 40.94 |