SUMMARYPedestrian dead reckoning (PDR) based on human gait locomotion is a promising solution for indoor location services, which independently determine the relative position of the user using multiple sensors. Most existing PDR methods assume that all sensors are mounted in a fixed position on the user's body while walking. However, it is inconvenient for a user to mount his/her mobile phone or additional sensor modules in a specific position on his/her body such as the torso. In this paper, we propose a new PDR method and a prototype system suitable for indoor navigation systems on a mobile phone. Our method determines the user's relative position even if the sensors' orientation relative to the user is not given and changes from moment to moment. Therefore, the user does not have to mount the mobile phone containing sensors on the body and can carry it in a natural way while walking, e.g., while swinging the arms. Detailed algorithms, implementation and experimental evaluation results are presented.

Nowadays mobile phones are multifunctional devices that provide us with various useful applications and services anytime and anywhere. However, people are sometimes unable to access an appropriate application due to the complexity and depth of the menu structure. This paper focuses on a feasibility study of operation prediction using observable attributes to realize self-optimization functionality in the mobile phones that can automatically and adaptively change their user interface (UI) according to user characteristics and circumstances. Machine learning (ML) is a promising technology for enhancing UI. However, few studies have been conducted for the operation prediction using the ML framework. We analyzed the real usage data collected by practical mobile phones and found that ML-based prediction methods were feasible to estimate future operations, and to provide context-aware UI.

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SUMMARYThis paper proposes a method for using an accelerometer, microphone, and GPS in a mobile phone to recognize the movement of the user. Past attempts at identifying the movement associated with riding on a bicycle, train, bus or car and common human movements like standing still, walking or running have had problems with poor accuracy due to factors such as sudden changes in vibration or times when the vibrations resembled those for other types of movement. Moreover, previous methods have had problems with has the problem of high power consumption because of the sensor processing load. The proposed method aims to avoid these problems by estimating the reliability of the inference result, and by combining two inference modes to decrease the power consumption. Field trials demonstrate that our method achieves 90% or better average accuracy for the seven types of movement listed above. Shaka's power saving functionality enables us to extend the battery life of a mobile phone to over 100 hours while our estimation algorithm is running in the background. Furthermore, this paper uses experimental results to show the trade-off between accuracy and latency when estimating user activity.

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