The implementation of a smartphone-based fall detection system using a high-level fuzzy Petri net |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taipei University, 151, University Rd., Sanhsia, New Taipei City 237, Taiwan;2. Department of Electronic Engineering, Min Chi University of Technology,84 Gungjuang Rd., Taishan Dist., New Taipei 24301, Taiwan;3. Department of Computer Science, University of Taipei, 1, Ai-Guo West Road, Taipei 10048, Taiwan;1. University of São Paulo at Ribeirão Preto College of Nursing, General and Specialized Nursing Department, Avenida dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, CEP 14040-902, SP, Brazil;2. Departamento de Análises Clínicas e Toxicológicas, Universidade Federal do Rio Grande do Norte (UFRN), Natal, Brazil;3. University of São Paulo at Ribeirão Preto School of Medicine, Pathology Department, Avenida dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, SP, Brazil;4. School of Pharmaceutical Sciences, Universidade Estadual Paulista Júlio de Mesquita Filho, Rodovia Araraquara, Jaú Km 1, Campos Ville, Araraquara, SP, Brazil;5. University of São Paulo at Ribeirão Preto School of Medicine, Medical Clinical Department, Avenida dos Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, SP, Brazil;1. Computer Science, Faculty of Computers and Informatics, Suez Canal University, Egypt;2. National Authority of Remote Sensing and Space Sciences, Cairo, Egypt |
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| Abstract: | The falling down problem has become one of the very important issues of global public health in an aging society. The specific equipment was adopted as the detection device of falling-down in the early studies, but it is inconvenient for the elderly and difficult for future application. The smart phone more commonly used than the specific fall detection equipment is selected as a mobile device for human fall detection, and a fall detection algorithm is developed for this purpose. What the user has to do is to put the smart phone in his/her thigh pocket for falling down detection. The signals detected by the tri-axial G-sensor are converted into signal vector magnitudes as the basis of detecting a human body in a stalling condition. The Z-axis data sets are captured for identification of human body inclination and the occurrence frequencies at the peak of the area of use are used as the input parameters. A high-level fuzzy Petri net is used for the analysis and the development of identifying human actions, including normal action, exercising, and falling down. The results of this study can be used in the relevant equipments or in the field of home nursing. |
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| Keywords: | Fall detection High-level fuzzy Petri net Smartphone G-sensor Homecare |
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