Sensor-Assisted Human Mobility Model Estimation forarticle-Filter-Based Location System
Date Issued
2009
Date
2009
Author(s)
Chen, Yu-Jung
Abstract
Nowadays, aging population becomes a common problem in the world. Elderly caring is a getting emphasized issue. Nursing center turns out to be a community for elderly people to centralize safety monitoring and caring. Suang-lien nursing center is one of them. There are over 350 members reside in it. The nursing center provides a wide variety of caring services for elderly people, hence the most important problem is their safety concern. In order to monitoring large amount of the elderly people, human resource on the caring is costly. e apply our RSSI based localization system to help safety monitoring. Our localization algorithm is based on KNN estimation plus particle filter to smooth position output. The KNN estimation is easily affected by wireless signal instability, which devastates the location accuracy. Thus the particle filter is used to solve the issue. Outdoor environment is much different than indoor. In indoor environment, human moves within the restriction of limited space. Therefore, set the mobility model inside particle filter with average mobility is enough to solve human moving patterns in indoor environment. However, human might have various mobility patterns in outdoor, including running, jogging, walking…etc. Extreme mobility cases are easily to occur in outside environment. Set the mobility in average case is not enough to solve the tracking problem. Our work comes up an idea that we could use an extra added accelerometer and analyze acceleration values by FFT. After that, we could get the target stride frequency, and multiply with target’s stride length. The target speed can be approximated on-line. With the speed approximation, the particle filter can simultaneously adjust the mobility model for position estimation, therefore, enhance the location estimation accuracy.
Subjects
KNN estimation
stride frequency
FFT
mobility model
Type
thesis
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