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In this paper, we studied Stacked Denoising Autoencoder(SDA) model for Human pose-based action recognition. We used public dataset Chalearn 2013 which contains Italian body language actions from 27 persons. We studied two model of SDA for pose clustering: 1) Traditional SDA with epoch and Neural Network supervised classifier and 2) Marginalized SDA which faster and ELM supervised classifier. We used...
The purpose of this study is to develop a device that combines exercise and entertainment. Therefore, we developed an application and the driving device to visualize the energy consumption during exercise. The first experiment utilizes the smart phone; the second experiment utilizes the action of jumping rope, handgrip, or playing a Kendama. The result of the test is that the driving devices responded...
GPS performs considerably well in the outdoor LoS conditions. However, its performance rapidly deteriorates when it is used in non-LoS indoor environments. This paper analyzes the performance of Wi-Fi based positioning in the outdoor environments. Our tests reveal that scene analysis can achieve high accuracies even in the outdoor setups. All observations reported in this work are based on experimental...
This paper presents a unified framework for recognizing and scoring dance motion using 2-layer classifier so that computation complexity is distributed into two layers. This research examines the performance of sliding window, hidden Markov Model (HMM) and conditional random field (CRF) as the first layer classifier to segment the input video into a sequence of motion primitive label. The second layer...
In this paper we describe the design and implementation of the Minimization of Drive Tests (MDT) system in which user equipments (UEs) upload the measurement reports periodically or upon requests. Based on the collected measurement reports, the MDT system learns the knowledge about the communication environment. We also propose a signal strength forecast method, which is an interesting and promising...
This work proposes a gesture-based left and right hand recognition algorithm for dynamic interface adjustment by using the information of the accelerometer and the touch screen on a mobile device. We design a warping method that aligns accelerometer signals with their corresponding positions of unlock-screen gestures. By projecting samples onto feature space and using a support vector machine classifier,...
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