• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• Physical Therapy Korea
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• v.22 no.4
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• pp.27-33
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• 2015

The purpose of this study was to find which spatiotemporal gait parameters gained from stroke patients could be predictive factors for the gait part of Tinetti Performance-Oriented Mobility Assessment (POMA-G). Two hundred forty-six stroke patients were recruited for this study. They participated in two assessments, the POMA-G and computerized spatiotemporal gait analysis. To analyze the relationship between the POMA-G and spatiotemporal parameters, we used Pearson's correlation coefficients. In addition, multiple linear regression analyses (stepwise method) were used to predict the spatiotemporal gait parameters that correlated most with the POMA-G. The results show that the gait velocity (r=.67, p<.01), cadence (r=.66, p<.01), step length of the affected side (r=.49, p<.01), step length of the non-affected side (r=.53, p<.01), swing percentage of the non-affected side (r=.47, p<.01), and single support percentage of the affected side (r=.53, p<.01) as well as the double support percentage of the non-affected side (r=-.42, p<.01) and the step-length asymmetry (r=-.64, p<.01) correlated with POMA-G. The gait velocity, step-length asymmetry, cadence, and single support percentage of the affected side explained 67%, 2%, 2%, and 1% of the variance in the POMA-G, respectively. In conclusion, gait velocity would be the most predictive factor for the POMA-G.

• Journal of International Academy of Physical Therapy Research
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• v.10 no.4
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• pp.1873-1878
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• 2019

Background: Walking is a complex activity. The main components of walking include balance, coordination, and symmetrical posture. The characteristics of walking patterns of stroke patients include slow walking, measured by gait cycle and walking speed. This is an important factor that reflects post-stroke quality of life and walking ability. Objective: This study aimed to examine the effect of deep lumbar muscle stabilization exercise on the spatiotemporal walking ability of stroke patients. Design: Quasi-experial study Methods: The experiment was conducted 5 times per week for 4 weeks, with 30 minutes per session, on 10 subjects in the experimental group who performed the deep lumbar muscle stabilization exercise and 10 subjects in the control group who performed a regular exercise. Variables that represent the spatiotemporal walking ability (step length, stride length, step rate, and walking speed) were measured using GAITRrite before and after the experiment and were analyzed. Results: There was a significant difference in the pre- and post-exercise spatiotemporal walking ability between the two groups (p<.05). Furthermore, there was a significant difference in the step rate and walking speed between the two groups (p<.05). Conclusions: Deep lumbar muscle stabilization exercise is effective in improving the walking ability of stroke patients. Therefore, its application will help improve the spatiotemporal walking ability of stroke patients.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.127-133
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• 2003

The data models which express all types of video information physically and logically. and the definition of spatiotemporal relationship of video data objects In This paper, we classifies meta-model for efficient management on spatiotemporal relationship between two objects in video image data, suggests meta-models based on Rambaugh's OMT technique, and expanded user model to apply the adaptive model, established from hyper-media or web agent to VOD. The proposed meta-model uses data's special physical feature: the effects of camera's and editing effects of shot, and 17 spatial relations on Allen's 13 temporal relations, topology and direction to include logical presentation of spatiotemporal relation for possible spatiotemporal reference and having unspecified applied mediocrity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.29-34
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• 1999

We present a practical method for removing biotches and restoring their mission data. To detect blotches, we employ a robust approach of local analysis of spatiotemporal anisotropic brightness continuity Our approach uses first-order spatiotemporal directional derivatives to select the smoothest direction for each examined pixel, and puts out the incorruption probability that he examined pixel may not be corrupted by blotches. As the restoration filter, were employ a spatiotemporal fuzzy filter whose response is adaptively controlled according to a fuzzy rule defined by the incorruption probability. The fuzzy filter is composed of the two different filter of the identity filter and the spatiotemporal directional-weighted-mean filter, and will put out an intermediate value between the original input brightness and the directional-weighted-mean brightness. We design the fuzzy rule in advance by a standard supervised learning fuzzy rule in advance by a standard supervised learning method. The computer simulations are presented.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.294-298
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• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1103-1114
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• 2003

Currently, one of the most critical issues in developing the service support system for various spatio-temporal applications is the discoverying of meaningful knowledge from the large volume of moving object data. This sort of knowledge refers to the spatiotemporal moving pattern. To discovery such knowledge, various relationships between moving objects such as temporal, spatial and spatiotemporal topological relationships needs to be considered in knowledge discovery. In this paper, we proposed an efficient method, MPMine, for discoverying spatiotemporal moving patterns. The method not only has considered both temporal constraint and spatial constrain but also performs the spatial generalization using a spatial topological operation, contain(). Different from the previous temporal pattern methods, the proposed method is able to save the search space by using the location summarization and generalization of the moving object data. Therefore, Efficient discoverying of the useful moving patterns is possible.

• The Journal of the Society of Stroke on Korean Medicine
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• v.14 no.1
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• pp.1-7
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• 2013

The following study reviewed the walking patterns of stroke patients with hemiplegia, which is called hemiplegic gait of stroke patients. Focusing is given to the changes in the distance and temporal factors of walking, which is called spatiotemporal characteristics, throughout the walking cycle. First, we introduced the definitions of essential terms related to gait and its measure. Second, we reviewed the spatiotemporal characteristics of hemiplegic gait. A main issue was that hemiplegic gait showed significant deviations from normal healthy gait. Although hemiplegia is primarily associated with unilateral motor disorder, changes in almost all spatiotemporal parameters used to assess walking were evident on both the involved and uninvolved sides of the body. Last, we reviewed the changes of spatiotemporal parameters of hemiplegic gait according to the prognosis or status of stroke patients, which may help to give a specific intervention for rehabilitation of stroke.

• Physical Therapy Korea
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• v.21 no.1
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• pp.29-36
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• 2014

The purpose of this study was to investigate the immediate effect of fabric ankle-foot orthosis on spatiotemporal gait parameters, compared to a barefoot condition in children with spastic cerebral palsy. Eleven children with spastic cerebral palsy participated in this study. Spatiotemporal gait parameters were measured with the GAITRite system. Fabric ankle-foot orthosis significantly improved Timed Up and Go test time and gait velocity. There was no significant difference in cadence. The step time significantly improved in both the more and less affected foot compared to the barefoot condition. The step length of the affected foot also significantly improved, but there was no significant difference in the step length of the less affected foot. There was significant improvement in the stride length of both the affected and less affected foot, but no significant difference in single stance or double stance. The fabric ankle-foot orthosis could improve stability, and selective control of the joint and promote better walking in children with cerebral palsy. Consequently, the fabric ankle-foot orthosis might be an alternative assistive device for neurological populations as a primary role instead of the typical ankle-foot orthosis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.517-523
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• 2019

PM₁₀ concentration is a spatiotemporal phenomenta and capturing data for such continuous phenomena is a difficult task. This study designed a model that enhances spatiotemporal resolution of PM₁₀ concentration levels using satellite imagery, atmospheric and meteorological sensor data, and multiple deep learning models. The designed deep learning model was trained using input data whose factors may affect concentration of PM₁₀ such as meteorological conditions and land-use. Using this model, PM₁₀ images having 15 minute temporal resolution and 30m×30m spatial resolution were produced with only atmospheric and meteorological data.