MobileRF: A Robust Device-Free Tracking System Based On a Hybrid Neural Network HMM Classifier.
| Autor: | Paul AS; EmbedRF LLC, Portland, OR, 97201., Wan EA; Electrical and Computer Engineering, Portland State University, Portland, OR 97207., Adenwala F; Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239., Schafermeyer E; Neurology, Oregon Health & Science University, Portland, OR 97239., Preiser N, Kaye J, Jacobs PG |
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| Jazyk: | angličtina |
| Zdroj: | Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) [Proc ACM Int Conf Ubiquitous Comput] 2014; Vol. 2014, pp. 159-170. |
| DOI: | 10.1145/2632048.2632097 |
| Abstrakt: | We present a device-free indoor tracking system that uses received signal strength (RSS) from radio frequency (RF) transceivers to estimate the location of a person. While many RSS-based tracking systems use a body-worn device or tag, this approach requires no such tag. The approach is based on the key principle that RF signals between wall-mounted transceivers reflect and absorb differently depending on a person's movement within their home. A hierarchical neural network hidden Markov model (NN-HMM) classifier estimates both movement patterns and stand vs. walk conditions to perform tracking accurately. The algorithm and features used are specifically robust to changes in RSS mean shifts in the environment over time allowing for greater than 90% region level classification accuracy over an extended testing period. In addition to tracking, the system also estimates the number of people in different regions. It is currently being developed to support independent living and long-term monitoring of seniors. |
| Databáze: | MEDLINE |
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