We propose an accelerometer-based gesture recognition method for smartphone users. In our method, similarities between a new time series accelerometer data and each gesture exemplar are computed with DTW algorithm, and then the best matching gesture is determined based on k-NN algorithm. In order to investigate the performance of our method, we implemented a gesture recognition program working on an Android smartphone and a gesture-based teleoperating robot system. Through a set of user-mixed and user-independent experiments, we showed that the proposed method and implementation have high performance and scalability.

A wide range of application domains from cognitive robotics to intelligent systems encompassing diverse paradigms such as ambient intelligence and ubiquitous computing environments require the ability to represent and reason about the spatial aspects of the environment within which an agent or a system is functional. Many existing spatial reasoners share a common limitation that they do not provide any checking functions for cross-consistency between the directional and the topological relation set. They provide only the checking function for path-consistency within a directional or topological relation set. This paper presents an efficient spatial reasoning algorithm working on a mixture of directional and topological relations between spatial entities and then explains the implementation of a spatial reasoner based on the proposed algorithm. Our algorithm not only has the checking function for path-consistency within each directional or topological relation set, but also provides the checking function for cross-consistency between them. This paper also presents an application system developed to demonstrate the applicability of the spatial reasoner and then introduces the results of the experiment carried out to evaluate the performance of our spatial reasoner.

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Activity recognition using smartphone accelerometer suffers from the user dependency problem that acceleration patterns of one user differ from those of others for the same activity. Moreover, it also suffers from the position dependency problem since a smartphone may be placed in any pockets or hands. In order to overcome these problems, this paper proposes an effective activity recognition method which is less dependent with both specific users and specific positions of the smartphone. Based on the proposed method, we implement a real-time activity recognition system working on an Android smartphone. Throughout some experiments with 6642 examples collected from different users and different positions, we investigate the performance of our activity recognition system.

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