Providence University, Taiwan Blvd Sec 7 No 200, Taichung City, 433, Taiwan.
J Med Syst. 2020 Sep 2;44(10):181. doi: 10.1007/s10916-020-01647-x.
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.
射频识别（RFID）标签在医疗保健行业中被广泛用于患者跟踪。一种主流的 RFID 实现是基于在医院或疗养院的固定位置安装的一系列阅读器，并且标签被嵌入到患者所穿的衣物中。护理人员可以从标签位置方便地获得各个患者的近乎实时的位置信息。对于可水洗衣物中的标签，通常采用被动式标签，因此在标签之间的协作机制中标签碰撞是一个常见问题。标签防碰撞方案是一个重要的考虑因素，会影响识别效率。针对这个问题，本文提出了一种基于线性插值的基于估计的动态帧时隙 Aloha 算法，该算法自适应调整帧长度。仿真结果表明，该算法在不到 10 次迭代中就能实现低于 1.5%的估计误差，在自动管理患者跟踪的临床环境中，它可以减少识别时间，同时降低标签泄漏概率。
