title: MobileRF: A Robust Device-Free Tracking System Based On a Hybrid Neural Network HMM Classifier.
abstract: We present adevice-freeindoor 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.
authors.affiliation: EmbedRF LLC, Portland, OR, 97201.
authors.forename: Anindya S
authors.initials: AS
authors.lastname: Paul
authors.affiliation: Electrical and Computer Engineering, Portland State University, Portland, OR 97207.
authors.forename: Eric A
authors.initials: EA
authors.lastname: Wan
authors.affiliation: Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239.
authors.forename: Fatema
authors.initials: F
authors.lastname: Adenwala
authors.affiliation: Neurology, Oregon Health & Science University, Portland, OR 97239.
authors.forename: Erich
authors.initials: E
authors.lastname: Schafermeyer
authors.forename: Nick
authors.initials: N
authors.lastname: Preiser
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Kaye
authors.forename: Peter G
authors.initials: PG
authors.lastname: Jacobs
keywords: Indoor localization; device-free passive localization; health care; indoor tracking; machine learning; mobility; neural network; tag-free tracking