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- W2890711375 abstract "Human mobility and walking pattern interpret a lot about the activities and behavior of an individual. Tracing these patterns and detecting abnormalities in the traced patterns can be used for health and fitness monitoring. Human resident sensors like the pocket IMUs uses the Body Area Network (BAN) for motion tracking, measuring vitals and other human behavior. Several works have been carried out to determine the posture by researchers. From the survey, it is found that no attempt has been made to detect abnormality. Hence, in this work, a model is proposed to determine abnormalities in walking and transition pattern using artificial neural network. The experiments were conducted using Castalia Wireless Body Area Network (WBAN) simulator for standard UCI data set. The simulator result shows that the proposed model detects irregularity more accurately." @default.
- W2890711375 created "2018-09-27" @default.
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- W2890711375 date "2018-09-18" @default.
- W2890711375 modified "2023-09-25" @default.
- W2890711375 title "Walking and Transition Irregularity Detection Using ANN Approach for Wireless Body Area Network" @default.
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- W2890711375 doi "https://doi.org/10.1007/978-981-10-8681-6_85" @default.
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