Activity Recognition of Workers and Passengers onboard Ships Using Multimodal Sensors in a Smartphone 


Vol. 39,  No. 9, pp. 811-819, Sep.  2014


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  Abstract

Activity recognition is a key component in identifying the context of a user for providing services based on the application such as medical, entertainment and tactical scenarios. Instead of applying numerous sensor devices, as observed in many previous investigations, we are proposing the use of smartphone with its built-in multimodal sensors as an unobtrusive sensor device for recognition of six physical daily activities. As an improvement to previous works, accelerometer, gyroscope and magnetometer data are fused to recognize activities more reliably. The evaluation indicates that the IBK classifier using window size of 2s with 50% overlapping yields the highest accuracy (i.e., up to 99.33%). To achieve this peak accuracy, simple time-domain and frequency-domain features were extracted from raw sensor data of the smartphone

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  Cite this article

[IEEE Style]

R. K. Piyare and S. R. Lee, "Activity Recognition of Workers and Passengers onboard Ships Using Multimodal Sensors in a Smartphone," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 9, pp. 811-819, 2014. DOI: .

[ACM Style]

Rajeev Kumar Piyare and Seong Ro Lee. 2014. Activity Recognition of Workers and Passengers onboard Ships Using Multimodal Sensors in a Smartphone. The Journal of Korean Institute of Communications and Information Sciences, 39, 9, (2014), 811-819. DOI: .

[KICS Style]

Rajeev Kumar Piyare and Seong Ro Lee, "Activity Recognition of Workers and Passengers onboard Ships Using Multimodal Sensors in a Smartphone," The Journal of Korean Institute of Communications and Information Sciences, vol. 39, no. 9, pp. 811-819, 9. 2014.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880