The Experts below are selected from a list of 177 Experts worldwide ranked by ideXlab platform

Chih-sheng Chen - One of the best experts on this subject based on the ideXlab platform.

  • A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System
    IEEE journal of biomedical and health informatics, 2014
    Co-Authors: Lih-jen Kau, Chih-sheng Chen
    Abstract:

    We propose in this paper a novel algorithm as well as architecture for the Fall Accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the Fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a Fall Accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished Fall Accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

  • A smart phone-based pocket Fall Accident detection system
    2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014), 2014
    Co-Authors: Lih-jen Kau, Chih-sheng Chen
    Abstract:

    A smart phone-based pocket Fall Accident detection system is proposed in this paper. To realize the system, the angles acquired by the electronic compass and the waveform sequence of the triaxial accelerometer on the smart phone are used as the input signals of the proposed system. The acquired signals are then used to generate an ordered feature sequence and examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current stage, it can proceed to next stage; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. With the proposed cascade classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished Fall detection accuracy up to 96% on the sensitivity and 99.71% on the specificity can be obtained when a set of 400 test actions in eight different kinds of activities are estimated by using the proposed approach, which justifies the superiority of the proposed algorithm.

Pllc Provident Law - One of the best experts on this subject based on the ideXlab platform.

Wei-ping Wang - One of the best experts on this subject based on the ideXlab platform.

  • Safety Pre-Control of Stope Roof Fall Accidents Using Combined Event Tree and Fuzzy Numbers in China’s Underground Noncoal Mines
    IEEE Access, 2020
    Co-Authors: Jie Wang, Longjun Dong, Chang Jian, Wei-ping Wang
    Abstract:

    Among the Accidents in China’s underground noncoal mines including ferrous metal mines, nonferrous metal mines and nonmetallic mines, roof Fall Accidents are always extremely bad. The application of risk assessment to avoid roof Fall Accidents in coal mines is widespread. However, the traditional method has defects in its accuracy, cost, and simplicity. It cannot fundamentally ensure that workers survive a roof Fall Accident. Therefore, there is no great referential significance for noncoal mines. In this paper, supposing that roof Fall Accidents in underground noncoal mines are inevitable, we assess where we should start to take measures to prevent worker deaths. On the basis of a detailed investigation, units including those working far from the roof, noticing the signs, etc. were identified, and then a concise event tree model was built. Under this framework, the event tree model has 2 consequences, including being alive and death, and 13 scenarios related to Accident occurrence. In this research, the probability of unit events was evaluated by using triangular fuzzy numbers. In addition, we found 3 scenarios with death, and then we calculated their respective probabilities. Finally, regarding the death scenario with the highest risk value, the paper recommends that priority measures should be taken to prevent deaths in roof Fall Accidents. The method in this paper provides a theoretical basis for pre-control measures and it is efficient, inexpensive, and simplified. Even if a roof Fall Accident is inevitable, we know where to start to take pre-control measures to prevent worker deaths.

Lih-jen Kau - One of the best experts on this subject based on the ideXlab platform.

  • A Smart Phone-Based Pocket Fall Accident Detection, Positioning, and Rescue System
    IEEE journal of biomedical and health informatics, 2014
    Co-Authors: Lih-jen Kau, Chih-sheng Chen
    Abstract:

    We propose in this paper a novel algorithm as well as architecture for the Fall Accident detection and corresponding wide area rescue system based on a smart phone and the third generation (3G) networks. To realize the Fall detection algorithm, the angles acquired by the electronic compass (ecompass) and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current state, it can proceed to next state; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. Once a Fall Accident event is detected, the user's position can be acquired by the global positioning system (GPS) or the assisted GPS, and sent to the rescue center via the 3G communication network so that the user can get medical help immediately. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished Fall Accident detection accuracy up to 92% on the sensitivity and 99.75% on the specificity can be obtained when a set of 450 test actions in nine different kinds of activities are estimated by using the proposed cascaded classifier, which justifies the superiority of the proposed algorithm.

  • A smart phone-based pocket Fall Accident detection system
    2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014), 2014
    Co-Authors: Lih-jen Kau, Chih-sheng Chen
    Abstract:

    A smart phone-based pocket Fall Accident detection system is proposed in this paper. To realize the system, the angles acquired by the electronic compass and the waveform sequence of the triaxial accelerometer on the smart phone are used as the input signals of the proposed system. The acquired signals are then used to generate an ordered feature sequence and examined in a sequential manner by the proposed cascade classifier for recognition purpose. Once the corresponding feature is verified by the classifier at current stage, it can proceed to next stage; otherwise, the system will reset to the initial state and wait for the appearance of another feature sequence. With the proposed cascade classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated. Moreover, as we will see in the experiment that a distinguished Fall detection accuracy up to 96% on the sensitivity and 99.71% on the specificity can be obtained when a set of 400 test actions in eight different kinds of activities are estimated by using the proposed approach, which justifies the superiority of the proposed algorithm.

Yoshifumi Nishida - One of the best experts on this subject based on the ideXlab platform.

  • Data-driven child behavior prediction system based on posture database for Fall Accident prevention in a daily living space
    Journal of Ambient Intelligence and Humanized Computing, 2020
    Co-Authors: Tsubasa Nose, Koji Kitamura, Mikiko Oono, Yoshifumi Nishida, Michiko Ohkura
    Abstract:

    Ten thousand children are admitted to emergency rooms due to Accidents every year in Tokyo. The most frequent Accident is a Fall Accident. Fall Accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. Predicting children’s behavior is necessary in order to improve the environment. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating cameras, a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. Thirty five children participated in an experiment to collect climbing posture data. A simulation is performed based on the posture database and allows us to visually understand how children climb up in daily living space. This makes it possible to improve to achieve a safe environment for children without the need for specialized knowledge, which is useful for parents, nursery teachers, nurses, etc. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.

  • data driven prediction system for an environmental smartification approach to child Fall Accident prevention in a daily living space
    Procedia Computer Science, 2019
    Co-Authors: Tsubasa Nose, Koji Kitamura, Mikiko Oono, Yoshifumi Nishida, Michiko Ohkura
    Abstract:

    Abstract Ten thousand children are admitted to emergency rooms due to Accidents every year in Tokyo. The most frequent Accident is a Fall Accident. Fall Accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating RGB-D cameras (Microsoft Kinect), a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.

  • EUSPN/ICTH - Data-driven prediction system for an environmental smartification approach to child Fall Accident prevention in a daily living space
    Procedia Computer Science, 2019
    Co-Authors: Tsubasa Nose, Koji Kitamura, Mikiko Oono, Michiko Ohkura, Yoshifumi Nishida
    Abstract:

    Abstract Ten thousand children are admitted to emergency rooms due to Accidents every year in Tokyo. The most frequent Accident is a Fall Accident. Fall Accidents may occur when climbing to a high place in a daily living space. Since injury prevention by human supervision does not work well, the World Health Organization recommends an environmental modification approach as an effective preventive countermeasure to this problem. However, even for advanced human modeling technology, predicting where children can climb in everyday life situations remains difficult. In the present study, the authors developed a new method for predicting places that children can climb in a data-driven manner by integrating RGB-D cameras (Microsoft Kinect), a behavior recognition system (OpenPose), and a climbing motion planning algorithm based on a rapidly exploring random tree. The present paper describes fundamental functions of the developed system and presents an evaluation of the feasibility of the prediction function.