Jonghwa Choi

Research and implementation of the context-aware middleware for controlling home appliances

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. The paper describes a context-aware middleware providing an automatic home appliance control based on a users preference. The context-aware middleware utilizes 6 basic data for learning and predicting the users preferences in multimedia content: the pulse; body temperature; facial expression; room temperature; the time; the location. The test results show that the pattern of an individuals preferences can be effectively evaluated and predicted by adopting the proposed context model.

Real-Time Human Tracker Based on Location and Motion Recognition of User for Smart Home

ABSTRACT The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human trackers architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the humans location, which uses three images, took an average of 0.037 seconds. The SVMs feature of humans motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.Keywords:Real-Time Human Tracker, Smart Home, Ubiquitous Computing, Pattern Recognition

Research on design and implementation of the artificial intelligence agent for smart home based on support vector machine

In this paper, we provide information an artificial intelligence agent for a smart home and discuss a context model for implementation in an efficient smart home. An artificial intelligence agent in a smart home learns about the occupants and the smart environment, and predicts the appliance service that they will want. We propose the SVM (Support Vector Machine) for the learning and prediction aspects of the artificial intelligence agent. The experiment was done using three methods. Each of these three methods applies a higher importance to a different set of context data, out of the data related to the occupant, home environment, and the characteristics of the home appliances. Excellent results were seen when the experiment applied a higher importance to the data related to the characteristics of the home appliances.

Fundamentals and Design of Smart Home Middleware

A smart home aims to provide home automation services for its inhabitants. One of the essential components of the smart home environment is middleware, which analyzes inhabitants’ behaviors and predicts their next activity based on physiological and environmental cues. This paper focuses on the architecture and the flowchart of smart home middleware and presents initial experimental results.

Ubiquitous intelligent sensing system for a smart home

We present the ubiquitous intelligent sensing system for a smart home in this paper. A smart home is intelligent space that studies patterns of home contexts that is acquired in a home, and provides automatic home services for the human. The ubiquitous intelligent sensing system acquires seven sensing contexts from four sensor devices. We utilize association rules of data mining and linear support machine to analyze context patterns of seven contexts. Also, we analyze stress rates of the human through the HRV pattern of the ECG. If the human is suffering from stress, the ubiquitous intelligent sensing system provides home service to reduce one’s stress. In this paper, we present the architecture and algorithms of the ubiquitous intelligent sensing system. We present the management toolkit to control the ubiquitous intelligent sensing system, and show implementation results of the smart home using the ubiquitous intelligent sensing system.

Performance evaluation of numerical integration methods in the physics engine

A physics engine in computer games takes charge of the calculations simulating the physical world. In this paper, we evaluate the performance of three numerical integral methods: Euler method, Improved Euler method, and Runge-Kutta method. We utilized a car moving game for the simulation experiments logging fps (frame per second). Each numerical integral was evaluated under two different settings, one with collision detection and the other without it. The simulation environment without collision detection was divided into two sections, a uniform velocity section and a variable velocity section. The Euler method was shown to have the best fps in the simulation environment with collision detection. Simulation with collision detection shows similar fps for all three methods and the Runge-Kutta method showed the greatest accuracy. Since we tested with rigid bodies only, we are currently studying efficient numerical integral methods for soft body objects.

Research and implementation of the context-aware middleware based on neural network

Smart homes integrated with sensors, actuators, wireless networks and context-aware middleware will soon become part of our daily life. This paper describes a context-aware middleware providing an automatic home service based on a users preference inside a smart home. The context-aware middleware utilizes 6 basic data for learning and predicting the users preference on the home appliances: the pulse, the body temperature, the facial expression, the room temperature, the time, and the location. The six data sets construct the context model and are used by the context manager module. The user profile manager maintains history information for home appliances chosen by the user. The user-pattern learning and predicting module based on a neural network predicts the proper home service for the user. The testing results show that the pattern of an individuals preferences can be effectively evaluated and predicted by adopting the proposed context model.

Intelligent pervasive middleware based on biometrics

This paper presents IPD (intelligent pervasive middleware) that provides automatic home services (consumer electronics: TV, DVD, audio, light, and air-conditioner) for human through analysis of the biometrics and environment contexts. The IPD receives the biometrics context (pulse, facial expression and body temperature, human location in smart home and human motion) from sensor devices. We handled the context’s pattern analysis in two steps. The first step selects consumer electronics (TV, DVD, audio, air-conditioner, light, project) from IPD’s rules. In the second step, IPD predicts detailed home service (for example, a detailed home service of the TV includes news, sports, and drama), using the supervised algorithm-based pattern analyzer. We used the SVM (support vector machine) for detailed service pattern analysis. We experimented on the intelligent pervasive middleware in two directions, and it was shown to have an effective performance in practical application. We are currently studying the association technique of home service (by using data mining) that can happen when IPD predicts home service by the home service predictor.

Multi-user human tracking agent for the smart home

This paper presents a human tracking agent that recognizes the location and motion of the human in the home. We describe the architecture of the human tracking agent, and present an image recognition algorithm to track location and motion of the human. The human tracking agent decides the human’s location, which changes in real-time, through the reletive distance of home furniture (or appliance) and human. Unlike the human’s location, because a person’s appearance (height, weight) is different for each person, a human’s motion should be recognized to be different from each other person. We converted the image (that is acquired from the network camera) into a standard image (that is defined in the human tracking agent) for recognition of multi-user’s motion. We used a LSVM(linear support vector machine) to recognize the feature patterns for human motion. In our experiment, results of motion recognition showed excellent performance accuracy of over 80%.

Real-Time human tracker based location and motion recognition for the ubiquitous smart home

The ubiquitous smart home is the home of the future that takes advantage of context information from the human and the home environment and provides an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. We present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. We used four network cameras for real-time human tracking. This paper explains the real-time human tracker’s architecture, and presents an algorithm with the details of two functions (prediction of human location and motion) in the real-time human tracker. The human location uses three kinds of background images (IMAGE1: empty room image, IMAGE2: image with furniture and home appliances in the home, IMAGE3: image with IMAGE2 and the human). The real-time human tracker decides whether the human is included with which furniture (or home appliance) through an analysis of three images, and predicts human motion using a support vector machine. A performance experiment of the human’s location, which uses three images, took an average of 0.037 seconds. The SVM’s feature of human’s motion recognition is decided from pixel number by array line of the moving object. We evaluated each motion 1000 times. The average accuracy of all the motions was found to be 86.5%.