• International conference on construction engineering and project management
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• 2015.10a
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• pp.179-182
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• 2015

Detecting and assessing human-related risks is critical to improve the on-site safety condition and reduce the loss in lives, time and budget for construction industry. Recent research in neural science and psychology suggest inattentional blindness that caused by overload in working memory is the major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated and dangerous construction tasks. Therefore, detecting the risk perception abilities of workers could help to identify vulnerable individuals and reduce unexpected injuries. However, there are no available measurement approaches or devices capable of monitoring construction workers' mental conditions. The research proposed in this paper aims to develop such a measurement framework to evaluate hazards through monitoring electroencephalogram of labors. The research team developed a wearable safety monitoring helmet, which can collect the brain waves of users for analysis. A bispectrum approach has been developed in this paper to enrich the data source and improve accuracy.

• 한국신재생에너지학회:학술대회논문집
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• 2007.11a
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• pp.174-176
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• 2007

Because of accident or leak of electricity, high voltage electricity can be conducted to vehicle chassis and damage human. Therefore the unit for detecting ground fault is necessary to minimize loss of life or equipment damage. Isolation resistance must be monitored for detecting ground fault. GFD(Ground Fault Detection) unit continually generate the pulse voltage between high voltage network and chassis. This will be sensing the returned current, calculate the isolation resistance and make decision the ground fault. This paper describes the method detecting ground fault.

• Journal of IKEEE
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• v.24 no.3
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• pp.783-790
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• 2020

In this paper, the human detecting radar system for indoor security applications is proposed, and its FPGA-based implementation results are presented. In order to minimize the complexity and memory requirements of the computation, the top half of the spectrogram was used to extract features, excluding the feature extraction techniques that require complex computation, feature extraction techniques were proposed considering classification performance and complexity. In addition, memory requirements were minimized by designing a pipeline structure without storing the entire spectrogram. Experiments on human, dog and robot cleaners were conducted for classification, and 96.2% accuracy performance was confirmed. The proposed system was implemented using Verilog-HDL, and we confirmed that a low-area design using 1140 logics and 6.5 Kb of memory was possible.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.

• Smart Media Journal
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• v.5 no.3
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• pp.49-53
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• 2016

Related researches are progressing that method of non-contact method using the electromagnetic field on the human body by detecting the motion recognition signal is the limitations of time and space, so less than the existing systems. In this paper, we designed the circuit system that can implement the hardware that can detect the electric field signal of the human body non-contact method to increase the recognition rate to screen this digital waveform. The PCB design Used to automatically increase of composition of the circuit and the linkage of the comparator digital waveform with circuit simulation of the system. At same time for evaluate the characteristics of the whole circuit system.

• Journal of Sensor Science and Technology
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• v.7 no.3
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• pp.197-202
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• 1998

The human pulse wave is a vital biosignal that includes the diagnostic data related with the heart and the cardiovascular system of human body. Based on the mechanical transducing method, a pulse detection transducer using optical fiber was developed to acquire the pulses non-invasively. To improve the detection efficiency, we proposed a new design that consists of two combinational parts; detecting part, which is in contact with the pulsating skin and transmits the displacement motion of the pulsating skin to the sensing part, and sensing part, which converts the physical quantity transmitted from the detecting part to electronic signal. By using the new method, we confirmed that the proposed transducer can detect the C point(incisura) and the T wave(tidal wave) which is not easily detected by existing transducers.

• BMB Reports
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• v.29 no.3
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• pp.192-199
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• 1996

A rapid and sensitive method has been developed to detect a-fetoprotein (AFP) in human serum by a two-site sandwich enzyme-linked immunosorbent assay (ELISA) with monoclonal antibodies (MAbs) for human AFP within 1 h. To obtain the most sensitive and reliable MAbs. 12 kinds of MAbs (HPJ1 to HPJ12) as a capture antibody and 4 kinds of horseradish peroxidase (HRP) conjugated MAbs as a tracer antibody were investigated. Among these, only HPJ 10-HRP conjugated HPJ 1 (HPJ 10-HPJ $1^*$) and HPJ 11-HRP conjugated HPJ 10 (HPJ 11-HPJ $10^*$) were chosen as candidates based on the linearity of the standard curve and the sensitivity of the assay. To further characterize these two pairs. MAbs against human AFP were purified from hybridoma cells. conjugated with HRP. and then characterized to optimize the two-site sandwich ELISA The HPJ 10-HPJ $1^*$ pair showed a sensitivity of 1 ng/ml and a better reproducibility than the HPJ 11-HPJ $10^*$ pair when the human sera were incubated at $37^{\circ}C$ for 30 min. The results obtained for 480 randomly selected human sera showed 0~20 ng/ml of AFP values for the normal human sera. To test the utility of our kit, AFP concentrations were determined for 951 human sera (including 85 normal sera, 480 random blood sera, 213 HBsAg-positives. 50 anti-HCV antibody positives. and 47 malignant diseases) and compared with other commercially available AFP detecting kits. These results show that the present two-site sandwich ELISA method is a rapid, sensitive, and reliable procedure for detecting AFP in human serum.

• Science of Emotion and Sensibility
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• v.8 no.1
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• pp.1-7
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• 2005

This paper reports several emotion detecting methods based on various attributes of human voice. These methods have been developed at our Engineering Systems Laboratory. It is noted that, in all of the proposed methods, only prosodic information in voice is used for emotion recognition and semantic information in voice is not used. Different types of neural networks(NNs) are used for detection depending on the type of voice parameters. Earlier approaches separately used linear prediction coefficients(LPCs) and time series data of pitch but they were combined in later studies. The proposed methods are explained first and then evaluation experiments of individual methods and their performances in emotion detection are presented and compared.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.94-102
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• 2023

Anomaly detection in human movements can improve safety in indoor workplaces. In this paper, we design a framework for detecting anomalous trajectories of humans in indoor spaces based on a variational autoencoder (VAE) with Bi-LSTM layers. First, the VAE is trained to capture the latent representation of normal trajectories. Then the abnormality of a new trajectory is checked using the trained VAE. In this step, the anomaly score of the trajectory is determined using the trajectory reconstruction error through the VAE. If the anomaly score exceeds a threshold, the trajectory is detected as an anomaly. To select the anomaly threshold, a new metric called D-score is proposed, which measures the difference between recall and precision. The anomaly threshold is selected according to the minimum value of the D-score on the validation set. The MIT Badge dataset, which is a real trajectory dataset of workers in indoor space, is used to evaluate the proposed framework. The experiment results show that our framework effectively identifies abnormal trajectories with 81.22% in terms of the F1-score.