Smart phone has become an important part of people's daily life. Most of current smart phone are equipped with a rich set of built-in sensors. The mobile applications such as geo-location based video annotation and indoor positioning require precise measurements from sensors. In addition, understanding the sensing performance of a smart phone device is helpful for implementing a mobile application that needs sensor data. This paper presents an experimental evaluation of key sensors in a state of the art smart phone-Google Nexus 4. The sensors chosen in the paper are accelerometer, gyroscope, magnetometer and GPS. Substantial tests have been executed to evaluate the sensors' accuracy, precision, maximum sampling frequency, sampling period jitter, energy consumption.

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Background and Objective: In smoking cessation clinical research and practice, objective validation of self-reported smoking status is crucial for ensuring the reliability of the primary outcome, that is, smoking abstinence. Speech signals convey important information about a speaker, such as age, gender, body size, emotional state, and health state. We investigated (1) if smoking could measurably alter voice features, (2) if smoking cessation could lead to changes in voice, and therefore (3) if the voice-based smoking status assessment has the potential to be used as an objective smoking cessation validation method.Methods: A systematic review of the scientific literature was conducted to compile studies on smoking status assessment based on voice features. We searched nine scientific databases for original studies involving the effects of smoking on voice features, the effects of smoking cessation on voice features.Results: A total of 34 studies were identified for review. We found that fundamental frequency, jitter, shimmer, harmonics to noise ratio, and other voice features are affected by smoking and could be used to assess smoking status. Conclusion:Speech assessment of smoking status based on voice features has potential as a smoking status validation method, as it is simple, reliable, and less time-consuming. Furthermore, this study provides recommendations for future research on the objective speech assessment of smoking status based on voice features.

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