• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• Smart Media Journal
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• v.3 no.2
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• pp.14-19
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• 2014

This paper presents a novel approach to recognize human gestures using k-NN and DTW based on Con tactless Electronic Potential Sensor(CEPS) in the smart devices such as smart TV and smart-phone in the proposed method, we used a Kalman filter to remove noise on gesture signal from CEPS and a PCA algorithm is utilized for reducing the dimensionality of gesture signal without data losses. And then in order to categorize gesture signals, k-NN classifier with DTW distance measure is considered. In the experimental result, we evaluate recognition performance with CEPS gesutres signal form the above two types of smart devices, and we can successfully identify five different gestures with more than 90% of recognition accuracy.

• Journal of the Korea Society of Computer and Information
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• v.16 no.11
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• pp.9-15
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• 2011

In this paper, we design and implement of gate management system by face recognition using smart phone. We investigate various algorithms for face recognition on smart phones. First step in any face recognition system is face detection. We investigated algorithms like color segmentation, template matching etc. for face detection, and Eigen & Fisher face for face recognition. The algorithms have been first profiled in MATLAB and then implemented on the Android phone. While implementing the algorithms, we made a tradeoff between accuracy and computational complexity of the algorithm mainly because we are implementing the face recognition system on a smart phone with limited hardware capabilities.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.19-26
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• 2016

Among the various services for mobile banking, user authentication method using bank security card is still very useful. We can use mobile banking easily and safely in case of saving encoded security codes in smart phone and entering codes automatically whenever user authentication is required without bank security card. In this paper automatic recognition algorithm of security codes of bank security card is proposed in oder to enroll the encoded security codes into smart phone using smart phone camera. Advanced adaptive binarization is used for extracting digit segments from various background image pattern and adaptive 2-dimensional layout analysis method is developed for segmentation and recognition of damaged or touched digits. Experimental results of proposed algorithm using Android and iPhone, show excellent security code recognition results.

• Smart Media Journal
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• v.2 no.4
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• pp.20-26
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• 2013

This paper presents an approach to Mongolian car plate recognition using artificial neural network. Our proposed method consists of two steps: detection and recognition. In detection step, we implement Flood fill algorithm. In recognition step we proceed to segment the plate for each Cyrillic character, and use an Artificial Neural Network (ANN) machine - learning algorithm to recognize the character. We have learned the theory of ANN and implemented it without using any library. A total of 150 vehicles images obtained from community entrance gates have been tested. The recognition algorithm shows an accuracy rate of 89.75%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2853-2873
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• 2013

Over the last few years, one of the most common purposes of smart homes is to provide human centric services in the domain of u-healthcare by analyzing inhabitants' daily living. Currently, the major challenges in activity recognition include the reliability of prediction of each classifier as they differ according to smart homes characteristics. Smart homes indicate variation in terms of performed activities, deployed sensors, environment settings, and inhabitants' characteristics. It is not possible that one classifier always performs better than all the other classifiers for every possible situation. This observation has motivated towards combining multiple classifiers to take advantage of their complementary performance for high accuracy. Therefore, in this paper, a method for activity recognition is proposed by optimizing the output of multiple classifiers with Genetic Algorithm (GA). Our proposed method combines the measurement level output of different classifiers for each activity class to make up the ensemble. For the evaluation of the proposed method, experiments are performed on three real datasets from CASAS smart home. The results show that our method systematically outperforms single classifier and traditional multiclass models. The significant improvement is achieved from 0.82 to 0.90 in the F-measures of recognized activities as compare to existing methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.199-204
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• 2019

This paper introduces smart mirror that provides the contents needed for an individual's daily life. When a command that is designated as voice recognition is entered, Smart Mirror is produced that outputs desired contents from a display. The contents of the current smart mirror include time, weather, subway information, schedule and photography. Smart mirror sold for commercial private households is difficult to distribute due to high prices, but the smart mirror production presented in this paper can lower the manufacturing cost and can be more easily used by voice recognition.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.103-110
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• 2011

In this paper, we propose a gaze recognition interface for smart wheelchair. The gaze recognition interface is a user interface which recognize the commands using the gaze recognition and avoid the detected obstacles by sensing the distance through range sensors on the way to driving. Smart wheelchair is composed of gaze recognition and tracking module, user interface module, obstacle detector, motor control module, and range sensor module. The interface in this paper uses a camera with built-in infra red filter and 2 LED light sources to see what direction the pupils turn to and can send command codes to control the system, thus it doesn't need any correction process per each person. The results of the experiment showed that the proposed interface can control the system exactly by recognizing user's gaze direction.

• Journal of Contemporary Eastern Asia
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• v.18 no.1
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• pp.124-152
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• 2019

Smart city, which employs information and communication technology (ICT) to resolve urban problems, is gaining more research attention in the innovation research. However, most previous studies regard citizens as merely passive accepters of the smart city services, focusing on individual private values. The present study aims to expand existing limited perspectives by applying public value management theory. Drawing from the literature review, we developed a dual perspective that a smart city service should encompass: private and public value. Then we set up a causal relationship between the value recognitions and intention to adopt smart city services. We further related antecedent variables to the dual value recognition in terms of citizens' characteristics: prior knowledge, personal innovativeness, and citizenship. Two case subjects among currently operating smart city services in South Korea were selected to empirically investigate our hypothesis. Results confirm the recognition of both public and private value is significantly related to the citizens' personal characteristics and resultant attitude towards acceptance and support for diffusion of the smart city services. This study is expected to provide useful implications for a new angle for the recipient of the smart city services, value orientation of the services, citizen's participation, and method selection for promotion.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.79-85
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• 2021

Speech recognition technology refers to a technology in which a computer interprets the speech language spoken by a person and converts the contents into text data. This technology has recently been combined with artificial intelligence and has been used in various fields such as smartphones, set-top boxes, and smart TVs. Examples include Google Assistant, Google Home, Samsung's Bixby, Apple's Siri and SK's NUGU. Google and Daum Kakao offer free open APIs for speech recognition technologies. This paper selects three APIs that are free to use by ordinary users, and compares each recognition rate according to the three types. First, the recognition rate of "numbers" and secondly, the recognition rate of "Ga Na Da Hangul" are conducted, and finally, the experiment is conducted with the complete sentence that the author uses the most. All experiments use real voice as input through a computer microphone. Through the three experiments and results, we hope that the general public will be able to identify differences in recognition rates according to the applications currently available, helping to select APIs suitable for specific application purposes.