Realizing the potential of mobile devices as experimental devices: Human computer interface and performance considerations Chiung Ching Ho & C. Eswaran Faculty of Computing and Informatics 23/09/13

Presentation outline Introduction Research question Related works Methodology and description of experiments Experimental work done Analysis Conclusion

1.0 Introduction 6 billion devices and rising as of 2012 6 billion mobile devices and rising, in use in all spheres of life

Presentation outline Introduction Research questions Related works Methodology and description of experiments Analysis Conclusion

2.0 Research questions To what extent can mobile devices be used as experimental devices? How does screen size, processing power and battery life impact the usage of mobile devices as experimental devices? 1 2

Presentation outline Introduction Research questions Related Works Methodology and description of experiments Analysis Conclusion

3.0 Related Works “Don’t key in data while moving” Problems When Moving > Errors > Time Solution Small form factor, pen , robustness Non visual feedback Gesture and speech controls Adaptive widgets “Don’t key in data while moving”

Increased screen size = More space for data and screen controls vs higher power consumption Smaller screen can be mitigated using one handed navigation, horizontal display, tabbed and pivot controls, zooming, new ways of navigation Trade-off between large screens and small screens and methods for optimizing small screen space

Increase of processor speed and memory size from 1996 to 2012 on log scale ( from 24 MHz and 16 Mb RAM to 1.5 GHz and 2 Gb Ram )

Presentation outline Introduction Research questions Related Works Methodology and description of experiments Analysis Conclusion

Experiment No.1 : HTK based smart device user verification Processing power and memory 206 MHz and 32Mb RAM Limited processing power prevented the use of computationally expensive algorithm Screen size 240 by 320 pixels Limited space for widgets and data Battery life Limited Needed to be connected to a power source Lack of suitable mobile machine learning library Computation was done on the server machine

Processing power and memory 1 GHz and 768 Mb RAM Increased processing power allowed the use of computationally expensive algorithm Screen size 480 by 800 pixels, double the screen size as compared to the device used in Experiment 1 Limited space for widgets and data Battery life Limited Needed to be connected to a power source frequently owing to the use of accelerometers, and larger screen size. This slowed down the data capturing process Lack of suitable mobile machine learning library Computation was done on the server machine, as process involved signal processing and classification Graphing was done on the mobile device Experiment no. 2: Accelerometer based gait smart device user verification

Presentation outline Introduction Research questions Related Works Methodology and description of experiments Analysis Conclusion

5.0 Analysis Increased drain on battery due to large screen size and connectivity needs Increased computational resources and useable screen area for controls and visualization

Dedicated mobile machine learning libraries Adaptive connectivity that is enabled on demand Longer lasting battery

6.0 Conclusion With the easy availability of mobile devices, and the array of sensors and connectivity options , mobile devices have a promising future as experimental devices To realize this opportunity, advancement in the following areas needs to be made Battery life Screen independent controls (voice or gesture) Dedicated mobile libraries for machine learning, signal processing Interoperability with other ‘plug and play’ sensors

Thank you! Q and A