Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning (1711.08149v3)

Abstract: Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety.

• The paper introduces a combined CNN+ANN model that achieves 99.9% localization accuracy using BLE tags in a clinical setting.
• The paper incorporates temporal data into its deep learning framework, significantly outperforming methods like standalone CNNs and RSSI thresholding.
• The paper demonstrates that this innovative system can enhance clinical workflow and patient safety, paving the way for broader hospital applications.

The paper "Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning" explores the application of deep learning techniques for tracking the location of patients and clinical staff in a radiation oncology clinic. This paper leverages Bluetooth Low Energy (BLE) technology in combination with artificial neural networks (ANNs) and convolutional neural networks (CNNs) to achieve highly accurate real-time localization.

Key Contributions:

1. Innovative Use of BLE for Localization:
   • The research utilizes BLE tags worn by individuals in the clinic to gather location data. BLE is chosen for its low energy consumption and feasibility in a clinical environment.
2. Deep Learning Models:
   • The paper employs both ANNs and CNNs to process the BLE signal data. Specifically, a combined CNN+ANN network is proposed and tested against other localization methods.
3. Performance Comparison:
   • The combined CNN+ANN model achieved an impressive accuracy of 99.9% in identifying the precise locations of the BLE tags. This performance significantly outclasses a standalone CNN model, which achieved an accuracy of 94%.
   • It also outperformed traditional methods such as majority voting-based RSSI thresholding and triangulation classifiers, both of which attained an accuracy of 95%.
4. Temporal Information Utilization:
   • The method incorporates temporal information into the deep learning models, which enhances the localization accuracy. This aspect is crucial for maintaining high performance in dynamic clinical settings.

Implications and Future Work:

The results of this paper suggest that deploying a low-cost, real-time location system using BLE and deep learning can substantially improve clinical workflow and patient safety. The high accuracy of the proposed method opens avenues for its deployment in various hospital environments, where precise tracking of staff and patients is critical for operational efficiency and safety.

Future research efforts, as indicated by the authors, will focus on implementing this system in actual hospital settings to evaluate its effectiveness in real-world scenarios. The authors also mention exploring additional use cases within clinical environments, optimizing the system for different layouts, and possibly integrating it with other hospital management systems for enhanced functionality.

In summary, the paper demonstrates a significant advancement in the field of medical localization systems by combining BLE technology with state-of-the-art deep learning methods to achieve near-perfect accuracy. This paves the way for more intelligent and responsive healthcare environments.