Using smart glasses in medical emergency situations, a qualitative pilot study

Schaer,; Melly,; Müller, Martin Anton; Widmer, Alexandra

doi:10.1109/wh.2016.7764556

Cited by 19 publications

(13 citation statements)

References 7 publications

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“…Similarly, greater network coverage and processing and data speeds have increased the utility of smartphones and wireless devices for prehospital emergency medicine. In recent simulation and pilot studies, remote experts were able to support paramedics, or emergency medical technicians (EMTs), based on relayed data via telemedicine [ 9 - 11 ]. In this review, the term EMT will be used broadly to include all first responders providing medical care for the injured and/or ill.…”

Section: Introductionmentioning

confidence: 99%

Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review

Kim¹,

Groombridge²,

Romero³

et al. 2020

J Med Internet Res

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Background Telemedicine offers a unique opportunity to improve coordination and administration for urgent patient care remotely. In an emergency setting, it has been used to support first responders by providing telephone or video consultation with specialists at hospitals and through the exchange of prehospital patient information. This technological solution is evolving rapidly, yet there is a concern that it is being implemented without a demonstrated clinical need and effectiveness as well as without a thorough economic evaluation. Objective Our objective is to systematically review whether the clinical outcomes achieved, as reported in the literature, favor telemedicine decision support for medical interventions during prehospital care. Methods This systematic review included peer-reviewed journal articles. Searches of 7 databases and relevant reviews were conducted. Eligibility criteria consisted of studies that covered telemedicine as data- and information-sharing and two-way teleconsultation platforms, with the objective of supporting medical decisions (eg, diagnosis, treatment, and receiving hospital decision) in a prehospital emergency setting. Simulation studies and studies that included pediatric populations were excluded. The procedures in this review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The Risk Of Bias In Non-randomised Studies–of Interventions (ROBINS-I) tool was used for the assessment of risk of bias. The results were synthesized based on predefined aspects of medical decisions that are made in a prehospital setting, which include diagnostic decision support, receiving facility decisions, and medical directions for treatment. All data extractions were done by at least two reviewers independently. Results Out of 42 full-text reviews, 7 were found eligible. Diagnostic support and medical direction and decision for treatments were often reported. A key finding of this review was the high agreement between prehospital diagnoses via telemedicine and final in-hospital diagnoses, as supported by quantitative evidence. However, a majority of the articles described the clinical value of having access to remote experts without robust quantitative data. Most telemedicine solutions were evaluated within a feasibility or short-term preliminary study. In general, the results were positive for telemedicine use; however, biases, due to preintervention confounding factors and a lack of documentation on quality assurance and protocol for telemedicine activation, make it difficult to determine the direct effect on patient outcomes. Conclusions The information-sharing capacity of telemedicine enables access to remote experts to support medical decision making on scene or in prolonged field care. The influence of human and technology factors on patient care is poorly understood and documented.

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Section: Introductionmentioning

confidence: 99%

Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review

Kim¹,

Groombridge²,

Romero³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

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“…The former is employed in building and aside road in video surveillance systems [27]. Mobile cameras can capture video stream at different locations [28].…”

Section: Related Workmentioning

confidence: 99%

“…Then, they also proposed an efficient, intelligent real-time emergency system for a prehospital emergency medical services (EMS) to improve the quality of EMS using live video streaming. Additionally, some glass-enabled applications have been developed [28], [31], [32], and the wearable mobile camera allows users to keep working while performing a remote video.…”

Section: Related Workmentioning

confidence: 99%

mVideo: Edge Computing Based Mobile Video Processing Systems

Sun

Sha

et al. 2020

IEEE Access

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Computer vision is widely used to detect anomalies in video processing systems for public safety. Applying Deep Neural Networks (i.e., DNNs) in computer vision can achieve a high detection accuracy but it requires a huge amount of computing power, storage space, and video data. Thus, DNNs-based video analytics is mostly deployed in the cloud with video data steaming from a set of stationary cameras. There are mainly three issues in this setting. First, steaming a huge amount of video data from cameras to cloud leads to high bandwidth consumption and latency. Second, when DNNs are deployed on resource-limited devices like edge nodes to reduce communication costs, it is hard to achieve a high detection accuracy. Third, stationary cameras can only collect a limited amount of video data that covers a small area, so it barely satisfies the needs of the real-time analytics in applications like public safety. We propose a mobile edge computing-based video stream processing platform, mVideo, which conducts video analytics making full use of resources at the collaborative edge and cloud nodes. On the mVideo, a mechanism is designed to partition a video analysis task based on available resources on the mobile edge node. Then, the edge nodes pre-process video data using a lightweight DNN model and upload the results to cloud nodes for further analysis. Thus mVideo not only collects video data that covers a large area, but also reduces the communication costs. To validate the proposed platform, a face recognition application is deployed on the mVideo prototype. Experimental results reveal that compared with the existing cloud computing model, mVideo reduces video data volume transmitted to the cloud nodes and power consumption by up to 99.5% and 96.2%, respectively. mVideo also improves the execution time by 90.0% to optimize mobile video analytics performance. INDEX TERMS Mobile video analytics, edge computing, mobile cameras, public safety.

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“…Various techniques for utilizing smart glasses have been developed by many researchers. For medical uses, techniques have been presented for clinical and surgical applications [8], medical emergency situations [9], and disaster medicine [10]. For recognition and interface, techniques have been presented for head gestures [11], face detection [12], eye movements [13], user authentication [14], estimation of respiration rate [15], speech interaction with eye blinking detection [16], context-aware lightning control [17], distance learning [18], and indoor localization [19], drowsiness and fatigue detection to increase road safety [20], countermeasures to phishing attacks [21], gait aid for Parkinson's disease patients [22], contextually-aware learning in physics experiments [23], and guiding for visually impaired users [24].…”

Section: Introductionmentioning

confidence: 99%

Thermal Model and Countermeasures for Future Smart Glasses

Matsuhashi

Kanamoto

Kurokawa

2020

Sensors

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The market for wearable devices such as smart watches and smart glasses continues to grow rapidly. Smart glasses are attracting particular attention because they offer convenient features such as hands-free augmented reality (AR). Since smart glasses directly touch the face and head, the device with high temperature has a detrimental effect on human physical health. This paper presents a thermal network model in a steady state condition and thermal countermeasure methods for thermal management of future smart glasses. It is accomplished by disassembling the state by wearing smart glasses into some parts, creating the equivalent thermal resistance circuit for each part, approximating heat-generating components such as integrated circuits (ICs) to simple physical structures, setting power consumption to the heat sources, and providing heat transfer coefficients of natural convection in air. The average temperature difference between the thermal network model and a commercial thermal solver is 0.9 °C when the maximum temperature is 62 °C. Results of an experiment using the model show that the temperature of the part near the ear that directly touches the skin can be reduced by 51.4% by distributing heat sources into both sides, 11.1% by placing higher heat-generating components farther from the ear, and 65.3% in comparison with all high conductivity materials by using a combination of low thermal conductivity materials for temples and temple tips and high conductivity materials for rims.

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Using smart glasses in medical emergency situations, a qualitative pilot study

Cited by 19 publications

References 7 publications

Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review

Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review

mVideo: Edge Computing Based Mobile Video Processing Systems

Thermal Model and Countermeasures for Future Smart Glasses

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