Impact of Using Drones in Emergency Medicine: What Does the Future Hold?

The use of unmanned aerial vehicles or drones has expanded in the last decade, as their technology has become more sophisticated, and costs have decreased. They are now used routinely in farming, environmental surveillance, public safety, commercial product delivery, recreation, and other applications. Health-related applications are only recently becoming more widely explored and accepted. The use of drone technology in emergency medicine is especially promising given the need for a rapid response to enhance patient outcomes. The purpose of this paper is to describe some of the main current and expanding applications of drone technology in emergency medicine and to describe challenges and future opportunities. Current applications being studied include delivery of defibrillators in response to out-of-hospital cardiac arrest, blood and blood products in response to trauma, and rescue medications. Drones are also being studied and actively used in emergency response to search and rescue operations as well as disaster and mass casualty events. Current challenges to expanding their use in emergency medicine and emergency medical system (EMS) include regulation, safety, flying conditions, concerns about privacy, consent, and confidentiality, and details surrounding the development, operation, and maintenance of a medical drone network. Future research is needed to better understand end user perceptions and acceptance. Continued technical advances are needed to increase payload capacities, increase flying distances, and integrate drone networks into existing 9-1-1 and EMS systems. Drones are a promising technology for improving patient survival, outcomes, and quality of life, particularly for those in areas that are remote or that lack funds or infrastructure. Their cost savings compared with ground transportation alone, speed, and convenience make them particularly applicable in the field of emergency medicine. Research to date suggests that use of drones in emergency medicine is feasible, will be accepted by the public, is cost-effective, and has broad application

Unmanned aerial vehicle-to-wearables (UAV2W) indoor radio propagation channel measurements and modeling

In this paper, off-body ultra-wide band (UWB) channel characterization and modeling are presented between an unmanned aerial vehicle (UAV) and a human subject. The wearable antenna was patched at nine different body locations on a human subject during the experiment campaign. The prime objective of this work was to study and evaluate the distance and frequency dependent path loss factors for different bandwidths corresponding to various carrier frequencies, and also look into the time dispersion properties of such unmanned aerial vehicle-to-wearables (UAV2W) system. The environment under consideration was an indoor warehouse with highly conductive metallic walls and roof. Best fit statistical analysis using Akaike Information Criteria revealed that the Log-normal distribution is the best fit distribution to model the UWB fading statistics. The study in this paper will set up a road map for future UAV2W studies to develop enhanced retail and remote health-care monitoring/diagnostic systems

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic.

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push-pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 min approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted

A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions

The Internet has made several giant leaps over the years, from a fixed to a mobile Internet, then to the Internet of Things, and now to a Tactile Internet. The Tactile Internet goes far beyond data, audio and video delivery over fixed and mobile networks, and even beyond allowing communication and collaboration among things. It is expected to enable haptic communication and allow skill set delivery over networks. Some examples of potential applications are tele-surgery, vehicle fleets, augmented reality and industrial process automation. Several papers already cover many of the Tactile Internet-related concepts and technologies, such as haptic codecs, applications, and supporting technologies. However, none of them offers a comprehensive survey of the Tactile Internet, including its architectures and algorithms. Furthermore, none of them provides a systematic and critical review of the existing solutions. To address these lacunae, we provide a comprehensive survey of the architectures and algorithms proposed to date for the Tactile Internet. In addition, we critically review them using a well-defined set of requirements and discuss some of the lessons learned as well as the most promising research directions

Statistical analysis and channel modeling in next generation wireless communication systems

In this thesis, statistical analysis and channel modeling in next generation wireless communication systems is presented in detail. The primary focus of this thesis is on the statistical modeling of interference temperature (IT) in cognitive radio systems, and empirical study of wireless channel characterization of unmanned aerial vehicle (UAV)-assisted communications at ultra-wideband (UWB) and at millimeter wave (mmWave) frequencies.Firstly, in the cognitive radio system, a novel idea to statistically model the dynamic interference threshold (IT) from user traffic demand is presented in detail. It is shown that the cognitive radio system with dynamic IT will have high capacity performance with less outage probability over a system that does not utilize dynamic IT. The detailed theoretical analysis with expressions for mean capacity and outage probability in general operation region, and in high power region are derived and subsequently, validated with the simulations results. In addition, the effect of secondary user interference on primary user is also examined in this part.In the second part, wireless channel characterisation for unmanned aerial vehicle-to-wearables (UAV2W) at UWB frequency is analysed, and studied empirically in an indoor warehouse environment. The frequency and distance dependent path gain analysis at different bandwidths for a corresponding carrier frequency with time dispersion characteristics is presented in detail. Furthermore, from statistical modeling, it was shown that the Log-normal distribution is the best fit distribution model to characterize fading in these UAV2W systems.Finally, a novel emulation method for UAV motion by a robotic arm is presented to study the mmWave channel characteristics (Doppler spreading and path loss) at 28 GHz. In addition to that, empirical study is carried out to analyze the propeller modulation effect caused by the propellers in UAVs with an actual UAV setup. These important statistical analysis, and channel modeling discussed in this thesis are very critical in designing, analysing, and in implementation of fifth generation (5G) and beyond 5G (B5G) communication for the future. This thesis is a stepping stone in that direction

The social construction of the humanitarian health drone: socialising the UAV

As unmanned aerial vehicles become increasingly present across everyday domains and lives, understanding the social factors driving and shaping the form of these machines gains in urgency. Inspired by socio-philosophical frameworks, this thesis seeks to determine the social drivers and outcomes of this technology, exploring humanitarian health applications in added depth as an empirical example from which future research can derive. A discourse analysis explores recurring themes and phrases from primary and secondary sources which participate in the construction of drone-related narratives, which through the eye of visions and Utopianism can be seen to promote pro-drone conceptualisations of the future. This piece concludes that using a ‘visions’ framework may help frame narratives and guide balanced and considered regulation for the future

The social construction of the humanitarian health drone: socialising the UAV

As unmanned aerial vehicles become increasingly present across everyday domains and lives, understanding the social factors driving and shaping the form of these machines gains in urgency. Inspired by socio-philosophical frameworks, this thesis seeks to determine the social drivers and outcomes of this technology, exploring humanitarian health applications in added depth as an empirical example from which future research can derive. A discourse analysis explores recurring themes and phrases from primary and secondary sources which participate in the construction of drone-related narratives, which through the eye of visions and Utopianism can be seen to promote pro-drone conceptualisations of the future. This piece concludes that using a ‘visions’ framework may help frame narratives and guide balanced and considered regulation for the future

Augmented reality (AR) for surgical robotic and autonomous systems: State of the art, challenges, and solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human–robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future