• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.6
• /
• pp.603-610
• /
• 2015

This paper presents a multimodal sensor system that can determine the location of house space by analyzing the postures and heights of the residents. It consists of two sensors: a tilt sensor and an altimeter sensor. The tilt sensor measures the static and dynamic postures of the residents, and the altimeter sensor measures their heights. The sensor system includes a Bluetooth transmitter, and the server receives the measured data and determines the location in the house. We describe the process determining the locations of the residents after analyzing their postures and behaviors from the measured data. We also demonstrate the usefulness of the proposed system by applying it to a real environment.

• Journal of Korea Multimedia Society
• /
• v.7 no.8
• /
• pp.1035-1045
• /
• 2004

In this paper, we describe a method that can recognize gestures and evaluate the degree of the gestures from sequential gesture images by using Gesture Feature Space. The previous popular methods based on HMM and neural network have difficulties in recognizing the degree of gesture even though it can classify gesture into some kinds. However, our proposed method can recognize not only posture but also the degree information of the gestures, such as speed and magnitude by calculating distance among the position vectors substituting input and model images in parametric eigenspace. This method which can be applied in various applications such as intelligent interface systems and surveillance systems is a simple and robust recognition algorithm.

• ETRI Journal
• /
• v.44 no.2
• /
• pp.286-299
• /
• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• Journal of Korea Multimedia Society
• /
• v.15 no.10
• /
• pp.1196-1204
• /
• 2012

In this paper, a series of algorithms are proposed for controlling different kinds of multimedia contents and realizing interact between human and computer by using single input device. Human gesture recognition based on NUI is presented firstly in my paper. Since the image information we get it from camera is not sensitive for further processing, we transform it to YCbCr color space, and then morphological processing algorithm is used to delete unuseful noise. Boundary Energy and depth information is extracted for hand detection. After we receive the image of hand detection, PCA algorithm is used to recognize hand posture, difference image and moment method are used to detect hand centroid and extract trajectory of hand movement. 8 direction codes are defined for quantifying gesture trajectory, so the symbol value will be affirmed. Furthermore, HMM algorithm is used for hand gesture recognition based on the symbol value. According to series of methods we presented, we can control multimedia contents by using human gesture recognition. Through large numbers of experiments, the algorithms we presented have satisfying performance, hand detection rate is up to 94.25%, gesture recognition rate exceed 92.6%, hand posture recognition rate can achieve 85.86%, and face detection rate is up to 89.58%. According to these experiment results, we can control many kinds of multimedia contents on computer effectively, such as video player, MP3, e-book and so on.

• Journal of Korean Academy of Nursing
• /
• v.30 no.1
• /
• pp.225-239
• /
• 2000

This study is a research of conceptual development to find the factors of presence. The concept and the definition of presence received from literary review. On the basis of these findings, we formulate the contents of presence through structured interview guide composed of open-ended questionnaire which included the frequency, attitude, and posture. We selected 104 samples who are the patients, doctors, nurses, and other health providers. And then the contents of presence were established after integrating the formulated contents and putting them in order. The categorizing of the presence was made after discussing with specialist in this field. By using the selected contents, we made 25 statements of presence which were categorized into three factors. The results were as follows: 1. The definition of presence is being with at the same time and space, making attention with openness, and the therapeutic interaction with empathy. 2. The contents of presence through personal interviews are The time required is 5 minutes(46.15%), 2-3 minutes(34.61%), and 10 minutes (15.38%) respectively. The frequency of visiting is 3 times(39.20%), every time(23.07%), and more than 5 times(20.19%) respectively. \circled2 In case of being with nurse is having pain(39.42%), suffering trouble or severe fear(9.61%), feeling discomfort(8.65%), taking care of wound(7.69%), and other unfavorable symptoms(6.73%) respectively. \circled3 The posture being with nurse is depends on the situations(63.46%), sitting(26.92%), and standing(9.61%) respectively. Eye contact with nurse is face to face(78.84%), depends on the situations(20.19%), and not face to face(0.96%) respectively. \circled4 The attitudes of comfort are explaining about disease(23.07%), holding on hands (14.42%), touching on the suffering parts (11.53%), and unconditionally being with(7.69%) respectively. \circled5 Nurses' caring actions are kindness (27.88%), replying to the question (12.50%), smiling(10.57%), bright appearances (8.65%), and right and quick treatment(8.65%) respectively. \circled6 The effects of being with are peaceful mind(58.65%), quick recovery(13.46%), and decrease in fear(12.50%) respectively. \circled7 The attitudes of being with are listening (11.53%), recognition(8.65%), talking about discomfort(8.65%), and answering kindly (7.69%) respectively. 3. From the analysis of presence factors, 25 statements and 3 categorized factors are presented.