• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.19-26
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• 2020

This paper analyses the feasibility of using implantable antennas to detect and monitor tumors. We analyze this setting according to the wireless propagation loss and signal fading produced by human bodies and their environment in an indoor scenario. The study is based on the ITU-R propagation recommendations and prediction models for the planning of indoor radio communication systems and radio local area networks in the frequency range of 300 MHz to 100 GHz. We conduct primary estimations on 915 MHz and 2.4 GHz operating frequencies. The path loss presented in most short-range wireless implant devices does not take into account the human body as a channel itself, which causes additional losses to wireless designs. In this paper, we examine the propagation through the human body, including losses taken from bones, muscles, fat, and clothes, which results in a more accurate characterization and estimation of the channel. The results obtained from our simulation indicates a variation of the return loss of the spiral antenna when a tumor is located near the implant. This knowledge can be applied in medical detection, and monitoring of early tumors, by analyzing the electromagnetic field behavior of the implant. The tumor was modeled under CST Microwave Studio, using Wisconsin Diagnosis Breast Cancer Dataset. Features like the radius, texture, perimeter, area, and smoothness of the tumor are included along with their label data to determine whether the external shape has malignant or benign physiognomies. An explanation of the feasibility of the system deployment and technical recommendations to avoid interference is also described.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.637-640
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• 2023

This paper presents multimodal image fusion with human pose for detecting abnormal human behaviors in low illumination conditions. Detecting human behaviors in low illumination conditions is challenging due to its limited visibility of the objects of interest in the scene. Multimodal image fusion simultaneously combines visual information in the visible spectrum and thermal radiation information in the long-wave infrared spectrum. We propose an abnormal event detection scheme based on the multimodal fused image and the human poses using the keypoints to characterize the action of the human body. Our method assumes that human behaviors are well correlated to body keypoints such as shoulders, elbows, wrists, hips. In detail, we extracted the human keypoint coordinates from human targets in multimodal fused videos. The coordinate values are used as inputs to train a multilayer perceptron network to classify human behaviors as normal or abnormal. Our experiment demonstrates a significant result on multimodal imaging dataset. The proposed model can capture the complex distribution pattern for both normal and abnormal behaviors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.851-859
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• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.269-272
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• 2015

this is the study of the human body antenna device which can detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal using superhigh frequency RF beam equipped with the body. and it is able to transmit the data of the detection of the powder, battle combats can share that among them. with its flexible roof radial antenna structure, it emits the superhigh frequency RF beam to the front and flank multiply, preprocesses through the powder preprocessing part. and with the non-linear regression model algorism engine part, reflecting the attenuation characteristics depend on the delayed time of degree of the signal power which is received to the superhigh frequency RF beam. so it is able to detect the signal of the most likely mine or powder based on the degree of the answer signal power according to the delayed time of the superhigh frequency RF beam. also, it can detect the powder whether it is metal or nonmetal, mine, dud, VBIED. it can increase the chance of detection about 90% more than existing mine detector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.56-60
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• 2017

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.226-228
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• 2021

We propose a fall recognition system using the Pose Detection of Google ML kit using video data. Using the Pose detection algorithm, 33 three-dimensional feature points extracted from the body are used to recognize the fall. The algorithm that recognizes the fall by analyzing the extracted feature points uses k-NN. While passing through the normalization process in order not to be influenced in the size of the human body within the size of image and image, analyzing the relative movement of the feature points and the fall recognizes, thirteen of the thriteen test videos recognized the fall, showing an 100% success rate.

• Journal of Radiation Protection and Research
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• v.38 no.4
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• pp.242-245
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• 2013

Monte Carlo method was applied to discriminate the external contamination on radiation workers in nuclear power plants for internal dose assessment generally used with a bed type scanning detector whole body counter. Korean voxel model with internal contamination was used to estimate the detection patterns of whole body scanning. Also, the BOMAB model with various external contamination was assumed to compare with detection of radionuclides inside the human body. From the comparison of detection efficiency between front and back side up, external contamination was easily distinguished.

• Journal of IKEEE
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• v.26 no.1
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• pp.36-42
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• 2022

This study presents an analysis of the human body channel as an electric signal path using body impulse response (BIR). The human body communications (HBC) has recently emerged as an effective signal transmission method to create wireless body area networks (WBAN). We provide body channel characteristics based on measured BIR in a proper experimental environment for the HBC using capacitive coupling with a customized channel sounding device, which can be applied as a guideline for the HBC system design. The frequency response of the BIR, extracted by a customized signal processing for the measure signals, shows the channel path loss (CPS) between 0 MHz and 100 MHz with an average CPS of approximately 46.8 dB. In addition, the relative noise power distributions can provide estimations on the signal to noise ratio at the HBC receiver in terms of capacitor and resistor values in the measured frequency band and the frequency band lower than 3 MHz considering the baseband signal detection.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.