Deploying and Adapting an Indoor Positioning System in the Clinical Setting

Author(s):  
James Stahl ◽  
Julie Holt ◽  
Michael Lye

We are living in an era where the demands on our healthcare system are relentlessly rising while at the same time key resources, such as, the number of physicians and time available to see patients, are declining. In order to diagnose what is wrong and treat it appropriately we need to be able to objectively measure and describe how our healthcare system behaves. At Massachusetts General Hospital, an innovative project weaves together industrial design, operations research, outcomes research with emerging technologies to provide a means for objectively and reliably measuring time in the primary care setting. The RFID in Clinical Workflow Project aims to provide a tool with which to understand resource allocation and to shape appropriate and effective policy. In order to successfully incorporate the use of an emerging technology that enables accurate and reliable measurement into the demanding and critical clinical setting, the multidisciplinary team used a hybrid of design techniques sourced from the different disciplines represented on the team.

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.


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