• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.219-234
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• 2009

This paper presents a novel approach for extracting simultaneously human daily activity patterns and discovering the temporal relations of these activity patterns. It is necessary to resolve the services conflict and to satisfy a user who wants to use multiple services. To extract the simultaneous activity patterns, context has been collected from physical sensors and electronic devices. In addition, a context model is organized by the proposed incremental statistical method to determine conflicts and to infer user intentions through analyzing the daily human activity patterns. The context model is represented by the sets of the simultaneous activity patterns and the temporal relations between the sets. To evaluate the method, experiments are carried out on a test-bed called the Ubiquitous Smart Space. Furthermore, the user-intention simulator based on the simultaneous activity patterns and the temporal relations from the results of the inferred intention is demonstrated.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2005.10a
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• pp.143-146
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• 2005

Human activity pattern has been changed as the contemporary urban society changes. Diverse activities repeat regular patterns as time passes. Diagram is a simple drawing which aims to organize and unify various information. The elements of the social behaviour could be spatialized by means of diagram applications. By using diagrams, architects understand contemporary urban society and form new space conditions. Time-based activity patterns consists of activity pattern in a restricted space and in urban structure for space use. Activity patterns for different time zones are explained by two types of diagrams, space occupation and flexibility of space, By the characteristic of space system structred by these diagrams, activities and programs are rearranged and variety of space is allowed through flexibility. Also, programs are mixed to apply to simultaneous occurrence of ever-changing human activities.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1087-1091
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• 2006

This paper describes the current spatial patterns of the net primary productivity (NPP) of the terrestrial vegetation and carbon emission (C) in the world due to the burning of fossil fuels in order to clarify the amount of expansion of human activity. The C/NPP value varies spatially from almost zero to several tens of thousand times the local NPP. C/NPP is higher under the condition of extensive human activities due to a high human population density or when the local NPP is extremely low in severe climatic zones. In contrast, the low C/NPP areas are distributed mainly in sparsely populated districts, loading to a low impact of human activity. Although the area where C/NPP is less than 10% accounts for about 70% of the entire land area, one-third of these areas cannot contribute to carbon absorption because of low NPP with a shortage of climatic resources. Since more than half of the areas of the remaining areas are agricultural land and forest ecosystems with high NPP, the possible afforestation area was evaluated to be maximum of $30{\times}10^{6}\;km^{2}$; here only sequestrate carbons that correspond to 2% of the global total NPP are present. These analyses revealed that presently most of the areas where the NPP is high are those exclusively used by humans and that it is difficult for large-scale forest plantations to absorb a substantial amount of the carbon emitted annually by humans.

• Journal of the Korean Institute of Landscape Architecture
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• v.14 no.3
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• pp.47-57
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• 1987

본 연구의 목적은 인간 생태학적 조경계획의 이론적 배경과 계획과정을 살펴보고, 인간 생태학이라는 사회과학적 이론의 환경계획에의 적용 가능성및 필요성을 밝히고자 함에 있다. 인간의 건강과 복지가 인간 생태학적 조경계획의 지침으로 제시되고 있다. 따라서 본 연구는 Philadelphia와 New York의 교외에 위치한 Upper Makefield Township 주민들의 정주유형 (Settlement Patterns), 활동유형(Activity Patterns), 이용자유형(User Patterns), 인간생태학적 소구역(Human Ecological Subregion)의 구분 및 정주기준(Siting Criteria)등을 조사 연구한 후 이들의 상호관계를 밝혀 계획가들에게 인간생태계의 이해를 도모하고자 하였다.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.150-161
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• 2018

We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.923-926
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• 2000

This paper concerned with the patterns of foot-floor contact and electromyography activities of the lower extremity of the body during the termination of human gait. The termination of human gait is defined as the transition from a steady-state gait to a quiet standing posture. The transition between these two states has not been extensively studied and defined. There appears to be a critical period in the gait cycle that the decision to terminate gait or continue to take an additional step must be made.

• Advances in Computational Design
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• v.4 no.2
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• pp.73-90
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• 2019

The growth in the number of single-member households is a critical issue worldwide, especially among the elderly. For those living alone, who may be unaware of their health status or routines that could improve their health, a continuous healthcare monitoring system could provide valuable feedback. Assessing the performance adequacy of activities of daily living (ADL) can serve as a measure of an individual's health status; previous research has focused on determining a person's daily activities and extracting the most frequently performed behavioral patterns using camera recordings or wearable sensing techniques. However, existing methods used to extract common patterns of an occupant's activities in the home fail to address the spatio-temporal dimensions of human activities simultaneously. Though multiple sequence alignment (MSA) offers some advantages - such as inherent containment of the spatio-temporal data in sequence format, and rapid identification of hidden patterns - MSA has rarely been used to extract in-home ADL routines. This research proposes a method to extract a household occupant's ADL routines from a cumulative spatio-temporal data log of occupancy collected using a non-intrusive method (i.e., a tomographic motion detection system). The findings from an occupant's 28-day spatio-temporal activity log demonstrate the capacity of the proposed approach to identify routine patterns of an occupant's daily activities and to reveal the order, duration, and frequency of routine activities. Routine ADL patterns identified from the proposed approach are expected to provide a basis for detecting/evaluating abrupt or gradual changes of an occupant's ADL patterns that result from a physical or mental disorder, and can offer valuable information for home automation applications by enabling the prediction of ADL patterns.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.182-186
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• 2017

Maintaining a proper lookout activity routine is integral to preventing ship collision accidents caused by human errors. Various subjective measures such as interviewing, self-report diaries, and questionnaires have been widely used to monitor the lookout activity patterns of navigators. An objective measurement of a lookout activity pattern classification system is required to improve lookout performance evaluation in a real navigation setting. The purpose of this study was to develop an objective navigator lookout activity classification system using wearable accelerometers. In the training session, 90.4% accuracy was achieved in classifying five fundamental lookout activities. The developed model was then applied to predict real-lookout activity in the second session during an actual ship voyage. 86.9% agreement was attained between the directly observed activity and predicted activity. Based on these promising results, the proposed unobstructed wearable system is expected to objectively evaluate navigator lookout patterns to provide a better understanding of lookout performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.395-397
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• 2019

Despite technical advance, human error is the main reason for maritime accidents. To ensure a safety of maritime transporting environment, technical and methodological improvement to react to various types of maritime accidents should be developed instead of ambiguously anticipating maritime accidents due to human errors. Survey, questionnaires, and interview have been routinely applied to understand objective human lookout pattern differences in various navigational situations. Although the descriptive methodology helps systematically categorizing different patterns of human behavior to avoid accidents, the subjective methods limit to objectively recognize physical behavior patterns during navigation. The purpose of the study is to develop an objective lookout pattern detection system using wearable sensors in the simulated navigation environment. In the simulated maritime navigation environment, each participant performed a given navigational situation by wearing the wearable sensors on the wrist, trunk, and head. Activity classification algorithm that was developed in the previous navigation activity classification research was applied. The physical lookout behavior patterns before and after situation-aware showed distinctive patterns, and the results are expected to reduce human errors of navigators.