• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1053-1059
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• 2021

In order to realize a smart home, a new type of service that converges the two technologies is required as a method to overcome the respective limitations of augmented reality and IoT technologies. Augmented reality recognizes objects and projects augmented content with the recognized objects on the screen. This technology mainly uses image processing methods such as markers as a method for recognizing objects. In this paper, an augmented reality-based IoT control platform using markers was developed. By defining a marker unique to the object, a unique identifier displayed on the camera was distinguished. A smart home system was implemented by calling a controller to control things. The proposed system receives state information of objects through symptom reality and transmits control commands. The proposed platform was verified by manipulating household lights.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.374-376
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• 2018

In this study, we developed a mobile home robot using a face recognition method using a camera attached to a raspberry pie. It receives the real time image through the camera attached to the raspberry pie, recognizes the face of the person, and distinguishes the operation of the smart cool air temperature device according to the result. It is expected that the robot will be able to increase the energy utilization efficiency by allowing the robot to operate in cold and hot winds only where there is no human being, instead of operating the hot and cold air conditioner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1010-1011
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• 2013

In this paper, we have developed a system for identifying pills and providing their information by image processing to assist the health care of the elderly. A wide variety of pills can lead to the difficulty of distinguishing for the elderly at home and results in unusage and abandonment of the pills. To resolve this problem, we obtain images from a camera of a smart phone, extract features such as shape, line and color by image processing, classify pills, and provide information including the name, effect and directions for the usage of the pills.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.677-686
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• 2023

In this paper, we study the design and implementation of a smart home outing care system that recognizes the user's purpose of going out and delivers useful information that can help when going out. RSS service data of the Korea Meteorological Administration can be transmitted in real time using ESP8266, and a system that can provide weather information to users after analyzing the data using Arduino MEGA is implemented. Using App Inventor, you can pack the necessary items without forgetting, and you can change the settings according to the desired weather and purpose. The position of the microphone was placed outside to increase awareness by 12%, and the sensitivity of the pressure sensor was set to a maximum of 210 kΩ. If there is an obstacle between the doors, the doors open automatically. An ultrasonic sensor was placed on the ceiling of the drawer to recognize an object within the range of 0.5cm to 10cm to check the existence of an object, and a camera was installed to research a security reinforcement system.

• Advances in Computational Design
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• v.4 no.2
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• pp.73-90
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• 2019

The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1059-1071
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• 2008

Users in ubiquitous computing environment can easily access and use a multitude of devices and services anywhere and anytime. The key technology to realize this scenario is the method to intuitively provide proper user interfaces for each device and service. Previous attempts simply provided a designated user interface for each device and service or provided an abstract user interface to control common functions of different services. To select a target appliance, either user directly specified the target device or depended on sensors such as RFID tags and readers limiting the applicable scenarios. In this paper, we present UbiController which uniquely uses camera on the mobile device to recognize markers of appliances to acquire user interface for controlling task. UbiController aims to provide automatic discovery of multiple services in the smart home environment, support traditional GUI and novel camera-based recognition method as well as intuitive interaction methods for users. In this paper, we show experiments on the performance of UbiController's discovery and recognition methods and user feedback on interaction methods from a user study.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.396-401
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• 2016

Owing to developments in the internet of things, many products have developed and various researches have processed. Smart home systems in Internet of things area are receiving attention from many people than the other areas. Autonomous mobile robots perform various parts in many industries. In this paper, a smart housekeeping robot was implemented using internet of things and an autonomous mobile robot. In order to make a smart housekeeping robot, Raspberry Pi, wireless USB camera, and uBrain robot of Huins Corp. is used. To control the robot, cell-phone connected with IP of Raspberry Pi, and then Raspberry Pi connected with uBrain robot using Bluetooth. a smart housekeeping robot was controlled using commands of a cell-phone application. If some user wants to move a robot automatically, we implemented that a robot can be chosen an autonomous driving mode from the user. In addition, we checked a realtime video using a cell-phone and computer. This smart housekeeping robot can help user check their own homes in real time.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.673-676
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• 2009

In this paper, we describe CAMAR Companoin, a context-aware mobile AR system that provides a user-adaptive assistance with an augmented picture according to the user's context in smart space. It recognizes physical objects and tracks the movement of those objects with a camera embodied to a mobile device. CAMAR Companion observes a mobile user's context, which is sensed by various kinds of sensors in environments, and infers user preference for the content in the situation. It recommends multimedia content relevant to the user's context. It overlays selected content over associated physical objects and enables the user to experience the content in a user-centric manner. Furthermore, we have developed the prototype to illustrate how our system could be used for a mobile user's well-being care applications in smart home environments. In this application, we found that our system could perceive a user preference even though a user's context is changed dynamically, and then adapt the multimedia content with respect to the user's context effectively. As such, the proposed user-adaptive system has the potential to play an important role in developing customized user interfaces in mobile devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1890-1897
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• 2021

People counting is an important technology to provide application services such as smart home, smart building, smart car, etc. Due to the social distancing of COVID-19, the people counting technology attracted public attention. People counting system can be implemented in various ways such as camera, sensor, wireless, etc. according to service requirements. People counting system using WiFi AP uses WiFi CSI data that reflects multipath information. This technology is an effective solution implementing indoor with low cost. The conventional WiFi CSI-based people counting technologies have low accuracy that obstructs the high quality service. This paper proposes a deep learning people counting system based on WiFi CSI data. Data preprocessing using auto-encoder, data augmentation that transform WiFi CSI data, and a proposed deep learning model improve the accuracy of people counting. In the experimental result, the proposed approach shows 89.29% accuracy in 6 subjects.