An Anti-Collision Scheme for RFID for Patient Tracking Using Linear Interpolation Estimation

Radio Frequency Identification (RFID) tags are widely used in the healthcare industry for patient tracking. A mainstream RFID implementation is based on a series of readers installed in a fixed location within a hospital or a nursing home and tags are embedded in the clothing worn by patients. Caregivers can readily obtain near real-time location information of individual patients from the tag locations. For implementation in washable clothing tags are often passive such that tag collision is a common problem within co-operation mechanism between tags. Tag anti-collision scheme is there an important consideration that affects the identification effectiveness. To address this issue, this paper proposes a dynamic frame slotted Aloha algorithm based on linear interpolation based estimation that adaptively adjusts the frame length. Simulation results show that the proposed algorithm yields an estimation error below 1.5% achieved in less than 10 iterations, it provides reduction in identification time while reduces the tags leakage probability in a clinical environment where patient tracking is automatically managed.