• KIPS Transactions on Software and Data Engineering
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• v.4 no.1
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• pp.57-64
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• 2015

Activity recognition technology is gaining attention because it can provide useful information follow user's situation. In research of activity recognition before smartphone's dissemination, we had to infer user's activity by using independent sensor. But now, with development of IT industry, we can infer user's activity by using inner sensor of smartphone. So, more animated research of activity recognition is being implemented now. By applying activity recognition system, we can develop service like recommending application according to user's preference or providing information of route. Some previous activity recognition systems have a defect using up too much energy, because they use GPS sensor. On the other hand, activity recognition system which Google released recently (Google Activity Recognition) needs only a few power because it use 'Network Provider' instead of GPS. Thus it is suitable to smartphone application system. But through a result from testing performance of Google Activity Recognition, we found that is difficult to getting user's exact activity because of unnecessary activity element and some wrong recognition. So, in this paper, we describe problems of Google Activity Recognition and propose AGAR(Advanced Google Activity Recognition) applied method to improve accuracy level because we need more exact activity recognition for new service based on activity recognition. Also to appraise value of AGAR, we compare performance of other activity recognition systems and ours and explain an applied possibility of AGAR by developing exemplary program.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1421-1429
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• 2017

Human activity recognition has been studied using various sensors and algorithms. Human activity recognition can be divided into sensor based and vision based on the method. In this paper, we proposed an activity recognition system using acceleration sensor and gyroscope sensor in smartphone among sensor based methods. We used Deep Belief Network (DBN), which is one of the most popular deep learning methods, to improve an accuracy of human activity recognition. DBN uses the entire input set as a common input. However, because of the characteristics of different time window depending on the type of human activity, the RBMs, which is a component of DBN, are configured hierarchically by combining them from different time windows. As a result of applying to real data, The proposed human activity recognition system showed stable precision.

• 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.

• KIISE Transactions on Computing Practices
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• v.21 no.5
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• pp.351-358
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• 2015

As smart phones with built-in sensors are developed, research on recognition using wearable devices is increasing. Existing papers are mostly limited on research to personal activity recognition. In this paper, we propose a method to recognize conceptual group activity. Before doing recognition, we generate new data based on the analysis of the conceptual group activity in a classroom. The study focuses on three activities in the classroom environment: Taking Lesson, Doing Presentation and Discussing. With the proposed algorithm, the recognition rate is over 96%. Using this method in real time will make it easy to automatically analyze the activity and the purpose of the classrooms. Moreover, it can increase the utilization of the classroom through the data analysis. Further research will focus on group activity recognition in other environments and the design of an group activity recognition system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.25-32
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• 2022

In this paper, we propose a pet monitoring system based on deep learning using an activity recognition device. The system consists of a pet's activity recognition device, a pet owner's smart device, and a server. Accelerometer and gyroscope data were collected from an Arduino-based activity recognition device, and the number of steps was calculated. The collected data is pre-processed and the amount of activity is measured by recognizing the activity in five types (sitting, standing, lying, walking, running) through a deep learning model that hybridizes CNN and LSTM. Finally, monitoring of changes in the activity, such as daily and weekly briefing charts, is provided on the pet owner's smart device. As a result of the performance evaluation, it was confirmed that specific activity recognition and activity measurement of pets were possible. Abnormal behavior detection of pets and expansion of health care services can be expected through data accumulation in the future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.197-201
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• 2006

In the implementation of a smart home, activity recognition technology using simple sensors is very important. In this paper, we propose a new activity recognition method based on Bayesian network (BN). The structure of the BN is learned by K2 algorithm and is composed of sensor nodes, activity nodes and time node whose state is quantized with reasonable interval. In the proposed method, the BN has less complexity and provides better activity recognition rate than the previous method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.67-72
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• 2015

Recently, studies that interact with human and things through motion recognition are increasing due to the expansion of IoT(Internet of Things). This paper proposed the system that recognizes the user's logical activity in home environment by attaching some sensors to various objects. We employ Arduino sensors and appreciate the logical activity by using the physical activitymodel that we processed in the previous researches. In this System, we can cognize the activities such as watching TV, listening music, talking, eating, cooking, sleeping and using computer. After we produce experimental data through setting virtual scenario, then the average result of recognition rate was 95% but depending on experiment sensor situation and physical activity errors the consequence could be changed. To provide the recognized results to user, we visualized diverse graphs.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.335-354
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• 2014

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

• The Journal of Korean Physical Therapy
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• v.33 no.6
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• pp.307-313
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• 2021

Purpose: This study was to the investigate recognition of physical activity between physical therapy students (PTS) and non-physical therapy students (NPTS) by measuring the level of physical activity using International Physical Activity Questionnaires (IPAQ). Methods: A cross-sectional survey was completed by 191 university students. The IPAQ with an additional question (Is physical activity necessary for your future job?) was used to evaluate the recognition and the amount of physical activity. The collected data were calculated as MET-minutes scores and were classified as walking, moderate, and vigorous level of physical activity. The students were analyzed by dividing them into those who had a part-time employment (16 PTS and 12 NPTS) and those who did not have a part-time employment (80 PTS and 83 NPTS). Results: In students with a part-time employment, no significances were observed between the PTS and NPTS, in terms of MET, frequency and time of physical activity, and sitting time (p>0.05). In students without a part-time employment, the NPTS was significantly higher than the PTS for the MET and frequency of physical activity in a vigorous level (p<0.05), and there were no significant differences in other levels of physical activity (p>0.05). In the additional question, the PTS showed a slightly higher than the NPTS (p<0.05). Conclusion: The physical therapy students did not remarkable barrier to recognition of physical activity, but there was a difference in their recognition of the vigorous level of physical activity. Therefore, the understanding of physical activity for PTS would play an important role in the recognition of how physical activity can be promoted.