• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.77-83
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• 2019

With the increase in population, the number of such senior citizens is increasing day by day. These senior citizens have a variety of care needs, but there are not enough health workers to look after them. Ambient Assisted Living (AAL) aims at ensuring the safety and health quality of the older adults and extending the number of years the senior citizens can live independently in an environment of their own preference. AAL provides a system comprising of smart devices, medical sensors, wireless networks, computer and software applications for healthcare monitoring. AAL can be used for various purposes like preventing, curing, and improving wellness and health conditions of older adults. While information security and privacy are critical to providing assurance that users of AAL systems are protected, few studies take into account this feature. In this paper, we propose a secure and lightweight authentication scheme for the AAL systems. The proposed authentication scheme not only supports several important security requirements needed by the AAL systems, but can also withstand various types of attacks. Also, the security analysis results are presented to show the proposed authentication scheme is more secure and efficient rather than existing authentication schemes.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.81-89
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• 2023

In modern society, blind people face difficulties in navigating common environments such as sidewalks, elevators, and crosswalks. Research has been conducted to alleviate these inconveniences for the visually impaired through the use of visual and audio aids. However, such research often encounters limitations when it comes to practical implementation due to the high cost of wearable devices, high-performance CCTV systems, and voice sensors. In this paper, we propose an artificial intelligence fusion algorithm that utilizes low-cost video sensors integrated into smartphones to help blind people safely navigate their surroundings during walking. The proposed algorithm combines motion capture and object detection algorithms to detect moving people and various obstacles encountered during walking. We employed the MediaPipe library for motion capture to model and detect surrounding pedestrians during motion. Additionally, we used object detection algorithms to model and detect various obstacles that can occur during walking on sidewalks. Through experimentation, we validated the performance of the artificial intelligence fusion algorithm, achieving accuracy of 0.92, precision of 0.91, recall of 0.99, and an F1 score of 0.95. This research can assist blind people in navigating through obstacles such as bollards, shared scooters, and vehicles encountered during walking, thereby enhancing their mobility and safety.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.197-204
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• 2015

Recent changes in health care environment including aging population and prevalence of chronic disease encourage the adoption of new innovative technological solutions including wearable vital sensors, wireless networks, and smart phone. In this paper, we present an effective at-home lifestyle monitoring system that can be used for self-management and health intervention of patient himself in the Management-by-Exception perspectives. We implemented the filtering and queuing algorithms as a preprocessor of monitoring system to enhance efficiency of proposed system, and the effective UX design for self-management of patients themselves. The 94,467 actual clinic data was used to test the efficiency of the proposed system. As as a result, 64.8% of the incoming vital data was identified to be filtered out.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1148-1153
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• 2016

The number of cyclists is on the steady growing for leisure and transportation with the increasing interest in health and environment. However, the number of cycling accidents is also increasing steadily due to the lack of safety awareness and regulations. Focusing on this issue, we propose and develop a smart LED bicycle helmet in order to reduce a risk of cycling accident. The main idea is to change status of the LED on the helmet based on the bicycle's movement and provide motion information of the bicycle for others. To control the LED lights on the helmet, we use the Arduino board which communicates with the LED module through serial connection. We decide motion information by using the values from acceleration and GPS sensors of the smartphone. To receive this information from the smartphone, the control board and the smartphone are connected by Bluetooth.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.11
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.345-350
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• 2014

According to the necessity of smart working with various mobile devices, and the increasing services based on the converged infrastructures such as Cloud, Wearable Computing, Next Generation Wired/Wireless Mobile Networks, the network reliability has been one of the most important things. However, the research related to the network reliability is still insufficient. To solve these problems, we propose the ISNTC (Intelligent Safe Network Technology based on Cloud), which uses the safe network technique based on SDN, to be adopted to the smart working environments. The proposed ISNTC guarantees secure data forwarding through the synchronized transmission path and timing. We have verified the throughput which outperformed the existing techniques through the computer simulations using OPnet.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.167-173
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• 2015

Extraction of Finger-end points is one of the most process for user multi-commands in the Hand-Gesture interface technology. However, most of previous works use the geometric and morphological method for extracting a finger-end points. Therefore, this paper proposes the method of user finger-end points extraction that is motivated a ultrared diffused illumination, which is used for the user commands in the multi-touch display device. Proposed air-mouse is worked by the quantity state and moving direction of extracted finger-end points. Also, our system includes a basic mouse event, as well as the continuous command function for expending a user multi-gesture. In order to evaluate the performance of the our proposed method, after applying to the web browser application as a command device. As a result, the proposed method showed the average 90% success-rate for the various user-commands.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.89-97
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• 2017

Recent Internet of Things(IoT), and in addition to wearable PC, such as software development methodologies based on a variety of object-oriented design and design patterns of GoF(Gang of Four) with OOP(Object-Oriented Programming) intended for portable devices. However, incorrect application design specification is that the higher the importance of the optimization of the program on the device because it can cause problems such as decreased operating speed, increase the memory occupancy and battery usage. In this paper, we propose an optimized design pattern based on the method of application, such as Android (Android) OS Strategy Pattern, State Pattern, Observer pattern. Test results show that the proposed scheme selection patterns can be selected to optimize the design pattern in the device that specification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.265-269
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• 2008

Home health care with compact wearable units sounds to be a convenient solution for the elderly people living independently. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring enables them to get an emergency help in the case of the fatal fall event and can provide their general health status by observing the activities being performed in daily life. A tri-axial accelerometer sensor is used to get the acceleration anomalies associated with the user's movements. The three axis acceleration data are transferred to the base station sensor node via an IEEE 802.15.4 compliant zigbee module. The base station sensor node sends the data to base station PC for an offline processing. This work shows the feature set preparation using the principal component analysis (PCA) for the designing of neural network. The work includes the most common activities of daily living (ADL) like Rest, Walk and Run along with the detection of fall events from ADL. The angle from the vertical is found to be the most significant feature parameter for classification of fall while mean, standard deviation and FFT coefficients were used as the feature parameter for classifying the other activities under consideration. The accuracy for detection of fall events is 86%. The overall accuracy for ADL and fall is 94%.