Abstract. We describe our data collection and results on activity recognition with wearable, coin-sized sensor devices. The devices were attached to four different parts of the body: right thigh and wrist, left wrist and to a necklace on 13 different testees. In this experiment, data was from 17 daily life examples from male and female subjects. Features were calculated from triaxial accelerometer and heart rate data within different sized time windows. The best features were selected with forward-backward sequential search algorithm. Interestingly, acceleration mean values from the necklace were selected as important features. Two classifiers (multilayer perceptrons and kNN classifiers) were tested for activity recognition, and the best result (90.61 % aggregate recognition rate for 4-fold cross validation) was achieved with a kNN classifier.

Abstract. In this paper, we propose a personalized display, "AwareMirror : an augmented mirror". AwareMirror presents information relevant to a person in front of it by super-imposing his/her image. A toothbrush has been chosen as an identification tool while proximity sensors have been utilized to detect a person's position (in front of the mirror). Also, three types of information that can affect a user's decision have been selected. The mirror has been constructed using an acrylic magic mirror board and ordinal computer monitor. The acrylic board has been attached in front of the monitor, and only bright color from the display can penetrate the board. As a result of preliminary evaluation, we found that the mirror is useful to offer information in an unobtrusive manner while preserving its metaphor.

In this paper, we study a method of associating a person and an object utilized by him/her in real-time and an adhoc manner. The correlation coefficient is utilized to represent the proximity of the acceleration signal patterns from wearable sensors on different parts of the body and sensor augmented daily objects. The coefficients are evaluated in terms of the accuracy of the association and different varieties of calculating the correlation along 3D acceleration axes are studied. Also, the effects of the size of the time window and the sampling interval are examined.

A user of a smartphone may feel convenient, happy, safe, etc., if his/her smartphone works smartly based on his/her context or the context of the device. In this article, we deal with the position of a smartphone on the body and carrying items like bags as the context of a device. The storing position of a smartphone impacts the performance of the notification to a user, as well as the measurement of embedded sensors, which plays an important role in a device's functionality control, accurate activity recognition and reliable environmental sensing. In this article, nine storing positions, including four types of bags, are subject to recognition using an accelerometer on a smartphone. In total, 63 features are selected as a set of features among 182 systematically-defined features, which can characterize and discriminate the motion of a smartphone terminal during walking. As a result of leave-one-subject-out cross-validation, an accuracy of 0.801 for the nine-class classification is shown, while an accuracy of 0.859 is obtained against five classes, which merges the subclasses of trouser pockets and bags. We also show the basic performance evaluation to select the proper window size and classifier. Furthermore, the analysis of the contributive features is presented.

No abstract