Indoor identification and tracking using advanced multimodal approach

Significant research efforts have been directed toward human identification and tracking approaches as an infrastructure for supporting innovative smart home services. Even though various approaches have been proposed to tackle this problem, solutions still remain elusive due to various reasons. The aim of this paper is to develop and implement an advanced approach to detect and identify persons within opportune and calm smart home environment. A novel multimodal approach is proposed for non-tagged human identification and tracking. Smart floor and pyroelectric infrared sensors are combined through unique integration strategy to extract explicit information indicating a person's body surface area. This information significantly improved the identification accuracy. Data processing in the proposed approach has divided into different stages including: pattern segmentation and generation, features extraction, feature fusion, and pattern classification. Test cases were designed and carried out to test and evaluate the feasibility and effectiveness of the proposed approach.