• Electronics and Telecommunications Trends
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• v.33 no.3
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• pp.89-99
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• 2018

Human activity recognition (HAR) is a technology that aims to offer an automatic recognition of what a person is doing with respect to their body motion and gestures. HAR is essential in many applications such as human-computer interaction, health care, rehabilitation engineering, video surveillance, and artificial intelligence. Smartphones are becoming the most popular platform for activity recognition owing to their convenience, portability, and ease of use. The noticeable change in smartphone-based activity recognition is the adoption of a deep learning algorithm leading to successful learning outcomes. In this article, we analyze the technology trend of activity recognition using smartphone sensors, challenging issues for future development, and a strategy change in terms of the generation of a activity recognition dataset.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.90-96
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• 2023

Securing large amounts of training data in deep learning neural networks, including convolutional neural networks, is of importance for avoiding overfitting phenomenon or for the excellent performance. However, securing labeled training data in deep learning neural networks is very limited in reality. To overcome this, several augmentation methods have been proposed in the literature to generate an additional large amount of training data through transformation or manipulation of the already acquired traing data. However, unlike training data such as images and texts, it is barely to find an augmentation method in the literature that additionally generates bio-signal training data for convolutional neural network based human activity recognition. Thus, this study proposes a simple but effective augmentation method of bio-signal training data for convolutional neural network based human activity recognition. The usefulness of the proposed augmentation method is validated by showing that human activity is recognized with high accuracy by convolutional neural network trained with its augmented bio-signal training data.

• Educational Technology International
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• v.10 no.1
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• pp.79-105
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• 2009

The notion of contradiction from Cultural-Historical Activity Theory (CHAT) perspectives is known as an "engine" for the development of human practices because participants attempt to adjust their practices to resolve contractions. This study examines two middle school teachers' classroom practices from CHAT, focusing on the role of contradictions that emerged between their existing teaching practices and constructivist activities in the development of a student-centered technology-integrated learning environment. Findings indicated that teachers' awareness and resolution of contradictions played a large role in the development of a technology-supported student-centered learning environment, a culturally more advanced activity system: students displayed greater responsibilities for their learning and were guided to make effective decisions for their learning activity.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.221-228
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• 2021

Recent improvements on the quality, fidelity and availability of biometric data have led to effective human physical activity detection (HPAD) in real time which adds significant value to applications such as human behavior identification, healthcare monitoring, and user authentication. Current approaches usually use machine-learning techniques for human physical activity recognition based on the data collected from wearable accelerometer sensor from a single wearable smart device on the user. However, collecting data from a single wearable smart device may not provide the complete user activity data as it is usually attached to only single part of the user's body. In addition, in case of the absence of the single sensor, then no data can be collected. Hence, in this paper, a continuous HPAD will be presented to effectively perform user activity detection with mobile service infrastructure using multiple wearable smart devices, namely smartphone and smartwatch placed in various locations on user's body for more accurate HPAD. A case study on a comprehensive dataset of classified human physical activities with our HAPD approach shows substantial improvement in HPAD accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.100-108
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• 2016

The purpose of this study is to present a business strategy plan to increase organizational effectiveness of small and medium-sized enterprises. The research investigated in the level of human resource activity, such as recruitment, education, evaluation, compensation and development for the employees and executives who are working at small and medium-sized enterprises where located in Busan and Gyongnam province. With this, the research carried out actual proof analysis on the level of human resource activity effects on organization effectiveness like job satisfaction and organizational commitment. The following implications can be acquired from the result of multiple regression analysis on the 201 employees of small and medium enterprises. First, small and medium-sized enterprises should carry out human resource management activities and improve research and development capacity to enhance organization effectiveness. Second, in order to improve job satisfaction of the members of small and medium-sized enterprises, the management should concentrate on recruitment activity and reward maintenance management activity and come up with strategies to enhance learning ability and external network ability. Third, in order to enhance organizational commitment of the members of small and medium-sized enterprises, recruitment activity, training activity, and reward maintenance management activity should be carried out and the management should come up with strategies to enhance learning ability and external network ability. In this research, the objective was only to find out antecedents of organization effectiveness, but considering that causality might arise among the antecedents, in the studies hereafter, the verification on the structural relationship of various factors will be needed.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.87-93
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• 2019

In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 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
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• v.23 no.12
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• pp.1540-1551
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• 2020

Aiming at the problem that the existing human behavior recognition algorithm cannot fully utilize the multi-level spatio-temporal information of the network, a human behavior recognition algorithm based on a dense three-dimensional residual network is proposed. First, the proposed algorithm uses a dense block of three-dimensional residuals as the basic module of the network. The module extracts the hierarchical features of human behavior through densely connected convolutional layers; Secondly, the local feature aggregation adaptive method is used to learn the local dense features of human behavior; Then, the residual connection module is applied to promote the flow of feature information and reduced the difficulty of training; Finally, the multi-layer local feature extraction of the network is realized by cascading multiple three-dimensional residual dense blocks, and use the global feature aggregation adaptive method to learn the features of all network layers to realize human behavior recognition. A large number of experimental results on benchmark datasets KTH show that the recognition rate (top-l accuracy) of the proposed algorithm reaches 93.52%. Compared with the three-dimensional convolutional neural network (C3D) algorithm, it has improved by 3.93 percentage points. The proposed algorithm framework has good robustness and transfer learning ability, and can effectively handle a variety of video behavior recognition tasks.

• Journal of the Korea Safety Management & Science
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• v.12 no.1
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• pp.83-96
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, not completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.