Bluetooth 5: A Concrete Step Forward toward the IoT

Collotta, Mario; Pau, Giovanni; Talty, Timothy; Tonguz, O.K.

doi:10.1109/mcom.2018.1700053

Cited by 161 publications

(86 citation statements)

References 7 publications

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“…In this study, the module with Bluetooth radio version 4.0 is the limiting factor for faster measurements. Recently, new modules with increased data rate powered by Bluetooth 5.1 have been released . A hybrid solution with a combination of temperature and field measurements can also be implemented.…”

Section: Discussionmentioning

confidence: 99%

MR safety watchdog for active catheters: Wireless impedance control with real‐time feedback

Özen

Silemek

Lottner

et al. 2020

Magnetic Resonance in Med

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Purpose: To dynamically minimize radiofrequency (RF)-induced heating of an active catheter through an automatic change of the termination impedance. Methods: A prototype wireless module was designed that modifies the input impedance of an active catheter to keep the temperature rise during MRI below a threshold, ΔT max . The wireless module (MR safety watchdog; MRsWD) measures the local temperature at the catheter tip using either a built-in thermistor or external data from a fiber-optical thermometer. It automatically changes the catheter input impedance until the temperature rise during MRI is minimized. If ΔT max is exceeded, RF transmission is blocked by a feedback system. Results: The thermistor and fiber-optical thermometer provided consistent temperature data in a phantom experiment. During MRI, the MRsWD was able to reduce the maximum temperature rise by 25% when operated in real-time feedback mode. Conclusion: This study demonstrates the technical feasibility of an MRsWD as an alternative or complementary approach to reduce RF-induced heating of active interventional devices. The automatic MRsWD can reduce heating using direct temperature measurements at the tip of the catheter. Given that temperature measurements are intrinsically slow, for a clinical implementation, a faster feedback parameter would be required such as the RF currents along the catheter or scattered electric fields at the tip. K E Y W O R D S active catheter, active implantable medical devices, bluetooth low energy, interventional MRI, MR safety, radio frequency induced heating | 1049 ÖZEN Et al.

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Section: Discussionmentioning

confidence: 99%

MR safety watchdog for active catheters: Wireless impedance control with real‐time feedback

Özen

Silemek

Lottner

et al. 2020

Magnetic Resonance in Med

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“…The most commonly used data transmission methods are Bluetooth and USB; however, Bluetooth is more convenient for portable design than USB. Bluetooth 5.0 has low power consumption and a data transmission speed of up to 2 Mbps, which is sufficient to transmit spectral data [16]. Figure 6 shows the data transmission module of the PSS-1.0.…”

Section: Data Transmission Modulementioning

confidence: 99%

Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology

et al. 2019

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A portable smart spectrometer (PSS-1.0) and related systems have been designed and implemented to provide rapid on-site spectral analysis and lower the operator knowledge threshold for the application of spectrometry. The PSS-1.0 employs spectral analysis models downloaded from the spectral analysis system for offline spectral analysis and displays the results in the field. Users are incentivized to upload spectral analysis data to the Internet for storage by big data technologies, so that the spectral data can be reused easily. By employing blockchain technology it can be ensured that the data has not been tampered with, which can be used in monitoring and data transaction scenarios. The design of this spectrometer and related systems can be used for regulatory uses that require spectroscopy. Experimental results are presented in this paper, which prove that the design of the PSS-1.0 is feasible. The purpose of this paper is to design a civilian and non-operator knowledge threshold spectrometer and provide some ideas for better processing and utilization of spectral data.

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“…Real-time location tracking can be achieved through several methods, including chokepoint schemes [6] [7] [8], time-difference-based schemes [9] [10] [11], and signal strength-based schemes [12] [13] [14]. We present a variation of the signal strength-based scheme, using Bluetooth Low Energy (BLE) as an observable signal [15] [16] [17] [18]. Our approach uses relatively mature technology that is widely available commercially, which reduced the cost of adoption.…”

Section: Introductionmentioning

confidence: 99%

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

et al. 2020

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An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy and robustness because Bluetooth signal strength is subject to fluctuation. We developed a machine learning-based solution using a Long Short-Term Memory (LSTM) network followed by a Multilayer Perceptron classifier and a posterior constraint algorithm to improve RTLS performance. Training and validation datasets showed that most machine learning models perform well in classifying individual location zones, although LSTM was most reliable. However, when faced with data indicating cross-zone trajectories, all models showed erratic zone switching. Thus, we implemented a history-based posterior constraint algorithm to reduce the variability in exchange for a slight decrease in responsiveness. This network increases robustness at the expense of latency. When latency is less of a concern, we computed the latency-corrected accuracy which is 100% for our testing data, significantly improved from LSTM without constraint which is 96.2%. The balance between robustness and responsiveness can be considered and adjusted on a case-by-case basis, according to the specific needs of downstream clinical applications. This system was deployed and validated in an academic medical center. Industry best practices enabled system scaling without substantial compromises to performance or cost.In EROC at UTSW, we deployed a fleet of 142 Raspberry Pis to host the Bluetooth sensors. Raspberry Pis are small, robust, low-cost ($35), and simple-to-install computers that run a Linux distribution. They are powered and connected by four 24V routing switches using power-over-Ethernet with a voltage transformer for each Pi. Thus, they require no additional 110V electric outlets, which eliminates a common safety concern for hospitals. Additionally, because we use a wired connection to communicate with the Raspberry Pis, data transmission is more reliable than with Wi-Fi, and there is no increased burden for the Wi-Fi that might affect patient and staff internet usage. The Raspberry Pis are installed in the ceiling, invisible from the ground, so they do not affect the building's appearance.Many RTLS implementations use fixed parts in the infrastructure, such as Wi-Fi routers, as transmitters and moving parts, such as smart phones, as receivers. While this scheme is convenient for brief applications, it is limited when it comes to mass deployment. For example, the battery life of the moving parts is extremely low (e.g., 1-2 days for smart phones) because of an excessive workload that includes continuous BLE signal scanning and communication with clouds, which makes tracking equipment infeasible. Moreover, smart phones or tags with communication functions are usually more expensive than simple active BLE beacons, and their communication with clouds is not as robust or efficient as wired devices. Thus, considering battery life...

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Bluetooth 5: A Concrete Step Forward toward the IoT

Cited by 161 publications

References 7 publications

MR safety watchdog for active catheters: Wireless impedance control with real‐time feedback

MR safety watchdog for active catheters: Wireless impedance control with real‐time feedback

Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology

Development of a real‐time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications

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