System and method for providing medical attention

Ameliorative actions are taken using a system equipped with sensors and equipment for attending to a patient. Operation of the system is controlled at a remote site or alternatively at the location of an injury. Injury assessment and the patient's affective and/or cognitive state may be facilitated through the analysis of data obtained by the sensors.

FIELD

The present disclosure relates to the identification of injured persons or animals and the immobilization of injured limbs through the application of foam using unmanned aerial vehicles (UAVs).

BACKGROUND

Unmanned aerial vehicles have been developed for a number of uses, including surveillance, aerial filming, agricultural applications, and recreation. They are commonly referred to as drones. UAV designs include fuselage/wing assemblies resembling planes as well as helicopter and quadcopter configurations. Sensors such as gyroscopes, accelerometers, altimeters, GPS modules, cameras and/or payload monitors may be incorporated within UAVs. Gimbals may be used to mount cameras in UAVs. Radio signals generated by a transmitter/receiver, a smartphone, a tablet or other device can be used to control a UAV. UAVs can be designed to operate partially or completely autonomously. Functions such as hovering and returning to home can, for example, be provided autonomously. Data obtained by UAVs can be stored onboard using, for example, SD cards, or transmitted wirelessly. UAVs have been employed in the agriculture industry for purposes such as monitoring livestock and crops as well as crop dusting.

Orthopedic injuries such as bone fractures, sprains, and damaged ligaments and/or tendons often require prompt medical attention and stabilization to avoid further damage to the injured extremity and pain experienced by a patient. In remote settings, however, it is difficult to provide prompt and efficient medical treatment. Immediate and intense pain followed by numbness and tingling are among the symptoms of a bone fracture. Swelling, tenderness, bruising and possible blood loss are among the complications that may arise from a fractured limb. Deformation of the limb and decreased range of motion may further be observed. Current techniques often require multiple steps and components, thereby prolonging pain, swelling, and potential secondary injury.

SUMMARY

Embodiments of the present disclosure provide a an unmanned aerial vehicle having video capability and a spray nozzle for creating a foam splint.

A method for assisting a patient entails obtaining an unmanned aerial vehicle including a payload including one or more materials for forming a polymer foam, a dispenser for dispensing the one or more materials in the payload, and one or more sensors. The method further includes flying the unmanned aerial vehicle to a location in proximity to a patient, identifying the patient at the location using the one or more sensors, and dispensing the one or more materials for forming the polymer foam on a limb of the patient, thereby forming a foam splint on the limb.

Additional aspects of the disclosure are directed to a system for assisting an injured patient. The system includes an unmanned aerial vehicle having a payload including one or more materials for forming a polymer foam, a dispenser for dispensing the one or more materials in the payload, and one or more sensors. A processor is configured for processing outputs from the one or more sensors to identify a patient at a location using the one or more sensors, obtain physical characteristics data relating to the patient based on an output of the one or more sensors, and determine whether it is likely that a limb of the patient is injured based on the physical characteristics data. An actuator is provided for actuating the dispenser.

DETAILED DESCRIPTION

The subject matter of the instant application will be described with reference to illustrative embodiments. For this reason, numerous modifications can be made to these embodiments and the results will still come within the scope of the invention. No limitations with respect to the specific embodiments described herein are intended or should be inferred.

A system50is disclosed herein that includes an unmanned aerial vehicle (UAV)60including one or more sensors62for locating a person or animal (patient65) with an injury and detecting or inferring the nature of the injury. The UAV60includes a payload66including material(s) for immobilizing an injured portion of a body, for example the components of a foamable composition, and one or more delivery devices (e.g. nozzle68) such as a spray nozzle for applying the material(s) to the body. Based on the detection or inference of the injury, the UAV is further enabled in some embodiments to take further amelioration action with respect to the injury, such as the application of antibiotics, anesthetics or other material(s). The payload66accordingly includes a plurality of compartments in some embodiments. Actuators70within the UAV control functions such as the dispensing of material(s) from the payload66by the delivery device68. The actuators further control other operations of the UAV, such as speed controllers and other mechanisms affecting flight.

The UAV is configured for detecting or inferring an injury such as a bone fracture within a limb or other type of injury requiring immobilization. Information detected by the sensors62is processed using programs stored in the memory78or a module64configured for analyzing raw data from one or more sensors. Injury detection may be based on visual identification using deep neural nets, acoustic analysis, and/or other detected information. Visual identification can be assisted by a wireless transmitter/receiver Tx/Rx72from the UAV to a remote observer (e.g. physician or paramedic) and/or stored in a memory78within the UAV for analysis conducted within the UAV and/or the remote observer. In some embodiments, a high-definition video feed is provided to a remote professional. In some embodiments, the UAV includes an internal processor74that facilitates determination of whether there is an injury that should be immobilized and/or addressed with other ameliorative actions, possibly referencing analytics models and injury databases stored on the UAV or remotely. In other embodiments, processing is also conducted at a remote location and instructions are provided to the UAV from the remote location. Neural nets can, for example, be used to help identify injuries either alone or in association with communications from the patient that are transmitted by the UAV to a remote healthcare professional.

The exemplary UAV60includes geolocation features such as tracking hardware and software79that enable, for example, GPS tracking. Recorded GPS data can be stored in the memory78and/or transmitted by the wireless transmitter/receiver Tx/Rx72to a central location. Based on the detected injury, appropriate type(s) and amount(s) of material(s) to be administered can be determined as described further below. In some embodiments, the UAV can identify individuals and/or injuries using a unique identification that may be applied to the patient's body (e.g. barcode, color pattern). Patients provided with RFID tags or such other identifiers allows them to be identified and monitored through time by the UAV-based system. The UAV may further incorporate devices for emitting radiation of selected wavelengths incorporated with the actuators70or elsewhere. The emission of light in the visible spectrum can facilitate diagnosis of injuries and patient identification where ambient light is insufficient. Emission of radiation of other wavelengths can be also be used in diagnosing injuries. Patient identification and information relating to pain and/or injuries is communicated by the patient to a healthcare professional via the UAV in some embodiments where the patient or someone nearby is able to communicate audibly and/or through gestures.

The UAV is controlled, at least in part, by a device80including a transmitter/receiver82. The device80can be a laptop computer, a smartphone, a tablet, or other suitable device. In addition to the transmitter/receiver82, the device includes a processor84and a memory/display86. Applications for controlling UAVs using such devices are known to the art. One or both of the device80and UAV60may include a GPS logger. Time is preferably correlated with GPS log data. It will be appreciated that the system50can be employed in conjunction with cloud-based systems that receive, maintain and process information obtained by the UAV, as discussed further below.

The sensors62incorporated within the UAV60include one or more cameras for obtaining high-definition digital images, acoustic detector(s) such as piezoelectric sensors, back scattering devices, and/or chemical detector(s). High-definition visual images, possibly coupled with audio transmissions by the UAV, can facilitate self-reporting of injuries by a patient and decision-making by healthcare professionals with respect to possible action to be taken by the UAV. Analysis of digital images can alternatively or additionally be conducted electronically. Acoustic detection facilitates analysis of the environment surrounding the site of the injury. This may involve audio or vibration sensors located on the UAV. For example, analog signals detected by an acoustic detector can be amplified and processed to obtain digital signals that are stored in the memory78, transmitted to the healthcare professional, and/or cause the UAV to take action or refrain from taking action. Speech recognition software incorporated within the UAV can be employed to allow the patient65or a nearby individual to provide instructions to the UAV to control selected actions, such as the application of a foam spray and/or discontinuation of spraying. Combinations of visual, audio and/or other types of data (e.g. temperature, chemical) can be employed to increase the confidence level regarding the decision as to whether the UAV should take ameliorative action. Videoanalytic software is incorporated with some embodiments of the UAV to determine whether a detected object is a person and to further identify the torso and limbs of the person and possible injuries to the torso and limbs. The assessment may further include detecting the body temperature of the patient65and/or inspecting wounds. The UAV may carry a tuning fork in the payload for use by the patient or nearby individual to increase or decrease the confidence level of an injury diagnosis. While lacking the reliability of sophisticated diagnostic equipment such as Mill machines or 3D imagers, tuning forks may be easily used at an injury site by a patient receiving instructions via the UAV. When the tuning fork is struck, then held in contact with the bone, high-frequency vibrations travel into the bone. A sharp pain may be experienced by the patient if, for example, a stress fracture exists. Based on said assessment and risk management factor(s), the UAV60takes amelioration action such as immobilizing an injury by applying a foam cast, applying medication or anesthetics, or taking other actions that benefit the patient. Such action may additionally or alternatively be taken by other UAVs configured for spraying foam on an injured person or applying other remedial substances. The UAV may or may not include processing capability for processing the data obtained by the onboard sensors62. The UAV optionally transmits video data, acoustical data, physiological data and/or temperature data to a remote server and/or a professional for such processing.FIG. 1includes a diagram showing an exemplary system50.

Referring toFIG. 2, a flow diagram100includes exemplary steps for employing a UAV60in determining whether ameliorative action is warranted and taking one or more actions based on the determination. A request is made in step105for the UAV60to fly to an accident location L. The request includes location information and may be made from a remote location or from the accident location L itself. In some embodiments, the patient65or a nearby person reports a possible limb injury at the location L. In response to the request, the UAV60is launched and flies to the location L. The patient is identified in step110using the sensors62and associated analytics in some embodiments or by a healthcare professional using a video and/or audio feed from the UAV in other embodiments. As discussed above, UAVs can be controlled using smartphones or other devices capable of wirelessly communicating with UAVs. As known in the art, the UAV can be controlled manually and/or autonomously, depending on the function to be performed. In some embodiments, the UAV is provided with location information and autonomously flies to the location L using, for example, the GPS system79. In locations known to be potentially hazardous, radio transmitter(s) at or near the location L can facilitate UAV identification of the site and injured person or animal. The sensors62include an infrared sensor in some embodiments to facilitate location of the patient, particularly at night. The processor74(and/or84) in some embodiments includes an image processor208operatively associated with a logic program including software routines and memory to perform multi-resolution image analysis. Such a program can be stored in the memory78or other electronic memory such as a distinct software module. Different resolutions of an image are checked to assist in object recognition. Initial resolution may be defined by detection of any large, presumably human object causing a change in a background environment. In some embodiments, the sensors62include a motion detector and a motion direction detection routine is performed. Edges of an image are computed and a saliency map (not shown) may be generated. Saliency map algorithms for facilitating analysis of an image are known to the imaging art. After successful localization and multiple segmentations, the saliency map blends edges to achieve intensity regions in the image. Highest peak values in the intensity regions have the highest probability of being the subject patient and are selected for further processing. Based on local edges and regional features, a refinement of an area of interest is determined for localization in a torso detection routine and a limb detection routine. Hand detection routines are disclosed, for example, in U.S. Pub. No. 2002/0090146 to Heger and U.S. Pat. No. 6,252,598 to Segen, both of which are incorporated by reference herein. Segmentation provides locations that are likely to include the targeted elements to be detected. This includes addressing illumination effects, positions, rotations, and distances of the patient from the UAV camera. Comparisons to normalized correlation models or templates are performed to find similar illuminations, distances, orientations etc. in order to facilitate the removal of the background from the image. To reduce illumination effects on the image, before classification, each sub-image is normalized with respect to brightness and contrast. This sub-image minus the background preferably has a fixed size. After successful localization, multiple segmentation hypotheses are created by an algorithm based on nonlinear projections onto a baseline image. The recognition system preferably employs a convolutional neural network classifier or other statistical classifier which identifies, for example, a human arm or leg, and a confidence value for the limb detected. Based on the confidence measure, the segmentation hypotheses with the highest overall confidence may be accepted or, if the confidence level does not meet a predetermined level of reliability, the UAV or a healthcare professional viewing the patient using the video feed from the UAV may issue instructions to the detected patient instructing him to (if possible) alter his position or orientation with respect to the camera so as to improve the quality of the image undergoing analysis. Such instructions can also be provided upon the first detection of human motion, possibly confirmed by an infrared sensor and/or acoustic analysis, at the location L so that the patient can assume a position or be assisted to a position that is conducive to limb detection and subsequent foam splint application. By incorporating an analytics module64within the UAV, identifying a patient at the location L becomes possible even where the patient cannot be found using high definition images transmitted by the UAV to a remotely located health care professional. In some embodiments of the process100, step110includes use of a unique identification such as a bar code, color pattern, RFID tag signal, or other indicia applied to the patient at the location L that is electronically detected by the UAV. Facial recognition software may also be incorporated within an analytics module to facilitate patient identification based on the images obtained by the UAV. Facial expression software is further included in some embodiments and is used for assessing the emotional state of the patient65in step115as described below. Facial recognition and facial expression software is commercially available and continues to be developed. Raw facial expression data obtained from a webcam or other imaging device may, for example, be transmitted by the UAV60to the device80for importation into, for example, a statistical analysis processing module or analyzed within the UAV itself or a cloud-based system. Statistics packages such as IBM's SPSS Statistics are among those than may be used for interpreting facial recognition and facial expression data. Classification algorithms translate the facial features into emotional states and/or other metrics.

Step110A involves the use of UAV sensors62to monitor the identified patient and the surrounding environment at the location L. Noticeable injuries on the patient can be detected in this step. A digital imaging device on the UAV can be employed for obtaining high definition images of the injuries that are analyzed by the UAV as part of step115or by a remote system. Other sensors are alternatively or additionally employed in combination with the digital imaging device. In some circumstances, injuries can be detected by a remote healthcare professional based on the video feed from the UAV or audio input from the patient speaking to the UAV at the location L. Limb dislocation or deformation are among the symptoms of an injury that may be diagnosed by a healthcare professional having access to the video feed or results of 3D image analysis. Environmental conditions, such as the presence of gasoline, damaged vehicle(s), or other noticeably injured persons are among the other conditions at the location L that can be detected and possibly be factored into the decision as to whether the UAV is to take ameliorative action with respect to the identified patient. The sensors62include chemical sensors in some embodiments of the UAV that detect gasoline and/or other volatile substances. Electrochemical gas detectors, infrared point sensors and infrared image sensors are among the types of chemical sensors that can be incorporated within the UAV for detecting volatile hydrocarbons.

Step115involves using analytics to interpret the data obtained from the sensors62for determining the position P of the patient (to the extent such position has not been determined in step110), UAV detection of the damage coverage area DCA of the patient's limb(s) or torso, assessment of the damage level DL by the UAV, and assessing the overall patient condition C (including the patient's cognitive or emotional state) of the patient. Pattern recognition and machine learning facilitate the interpretation of raw data in a variety of applications and can be applied to at least steps110and115as described herein. Assessment of the damage level DL may determine, for example, whether the patient's limb is bruised or broken. Such information allows determination of whether the risk level is low and therefore conducive to the application of a foam spray by the UAV to immobilize a limb or high and therefore requiring inaction or further processing. For example, the patient's injured limb may or may not be in a position conducive to the application of a foam splint by the UAV. As discussed above, image processing by the UAV and other data obtained by the sensors62and used by the UAV and/or a healthcare professional allow identification of a patient as well as identification of torso, limb and head areas. Damage to a part of a patient's body (damage area DA) is ascertained using video-analytics in some embodiments, including for example neural network classification to diagnose particular injuries and the level of such injuries. The UAV includes an xray back-scattering radiographic system in some embodiments that obtains images that are analyzed using such classification. Such images may include information related to the internal structure of the patient's limb (e.g. bone structure) or only the outer surface of the limb itself. Back-scattering allows observation of a patient's limb beneath layer(s) of clothing. Bone fractures are often accompanied by limb distortion, breaking of the skin, swelling and/or skin discoloration. Diagnosis is facilitated by detection of one or more of such conditions. While imaging of a bone itself would likely provide a diagnosis of both the damage area DA and damage level DL having the highest confidence level, it is not necessarily required in order for the UAV to diagnose the probability of a fracture or similar injury, such as a serious ligament injury or rupture. For example, the patient could communicate the presence of pain in an arm to a UAV that employs speech recognition software. If swelling and discoloration of the painful arm are also detected by a camera and/or back-scattering system using, for example, pattern recognition and/or image color recognition software, the confidence level of a fracture diagnosis is increased. In some embodiments, images of the arms and/or legs of a patient obtained by the UAV are compared with each other for differences in physical characteristics that may suggest an injury in one limb but not another. Upon analysis of the damage coverage area DCA, the damage level DL, the position P of the patient, the patient's condition C, and possibly additional factors, a risk level determination is made. The system50may use various online or offline injury databases (e.g. European Injury database (IDB)—international burn injury database (iBID), and/or injury databases from various healthcare facilities wherein an injury can be characterized by a type, shape, behavior, danger level, expansion capacity. Environmental context information can also be considered: e.g. weather, type of accident). Such databases can be used to build various learning models which can be deployed within the system to detect, classify and characterize injuries in real-time. Thus, during an emergency situation, visual analytics and/or deep neural net modules may be able to detect, classify, and further characterize injuries as observed by the UAV. Audiovisual information transmitted from the UAV to a remote healthcare professional can further assist in obtaining injury information.

If processing of the risk level factors discussed above by the UAV results in a determination that the risk level is high, the UAV takes no immediate action with respect to the patient. A high risk level may be based on one or more findings, such as the failure by the UAV to detect a damage area, an unfavorable position of the patient for the application of a foam splint, and/or evidence of the patient being in a disturbed or agitated state that would make the application of a foam splint by the UAV problematical. There may also be insufficient data that would allow the UAV to determine the correct foam spray composition. Risk level may alternatively be based on a combination of the findings in some circumstances where no single factor is sufficient to result in a “high risk” determination. The exemplary UAV60is configured to transmit high-definition images in step115A and/or other data obtained by the sensors62to a remote system for further processing and analysis by a health care professional using the transmitter/receiver72. The remote system includes the device80used for controlling the UAV in some embodiments, though data from the UAV can alternatively be sent to another remote site or multiple sites configured for receiving the data. Step115B is then conducted at the remote site. This step involves performing further assessment including i) risk assessment based on the inquiry type and characteristics, using online or offline databases (e.g. IDB, iBID) in some embodiments as well as contextual information (e.g. accident type); ii) determining the possible need for specialized emergency UAVs and/or emergency personnel; iii) remote conversation/data exchange with the patient or person(s) near the patient via UAV-enabled telemedicine; iv) monitoring the condition of the patient using a videofeed from the UAV and optionally signaling the UAV to perform further actions from a remote site. Such additional analysis, possibly aided by image enhancement of the images obtained by the UAV, may increase the confidence level of a diagnosis, thereby reducing the risk level and causing a signal to be sent to the UAV60with instructions relating to the application of a foam splint to the patient's limb. Such instructions, which may be digitally encoded, control: 1) the distance between the UAV and the patient, 2) the dispensing of foam on a limb, 3) movement of the UAV and/or foam nozzle during the application of foam. In some embodiments, the composition of the applied foam is also controlled.

Analytics models and data sources are stored on a cloud in some embodiments, and are accessible by one or more of the UAV60, the remote device80, or a remotely located healthcare professional. Injury databases, incident databases, location databases, historic feedback, foam databases (for example recommended foam patterns, amounts, compositions) are among the stored data in some embodiments. As shown inFIG. 2, step115C involves the accessing of cloud-based analytic models and/or data sources by a remote system in the processing of information received from the UAV60. In some embodiments, the UAV60accesses the cloud computing environment directly. Cloud computing is described in further detail below.

Based on the determinations made by the UAV and/or remote system as described above, with possible reference by either to cloud-based analytics models or data, a determination is made in step120with respect to one or more of a foam spray pattern, a foam spray volume to be dispensed by the UAV, spraying duration, and/or foam composition. Such determination is based on the damage coverage area DCA and the damage level DL. The use of a polyurethane foam for the purpose of forming a rigid or semi-rigid structure such as a splint may involve the mixing of precursor materials during the course of application to a patient. The mixed materials rapidly cure to form the splint. The payload66in some embodiments of the UAV60accordingly includes pressurized containers containing each precursor material. A two-component foam discussed in the art for forming foam splints includes a polyisocyanate component and a polyol component that are mixed in or near a nozzle to form a polyurethane foam. Such foams are commercially available and are also used in the construction industry. The foam density is controlled by changing the amount and/or type of blowing agent, for example a hydrofluorocarbon that enhances frothing. Water is optionally employed in the blowing agent. Lower water contents are associated with foams of greater rigidity. The UAV may further include a heater for keeping the payload, including the foam precursor materials, sufficiently warm when attending to a patient in cold outdoor temperatures. The UAV dispenser68is in the form of one or more dispensing guns and associated nozzles in some embodiments. Different foam precursors and/or spray patterns may be associated with each of the dispensing guns. Disposable nozzles are preferably employed to allow the operator to change nozzles after each use or to select nozzles having particular spray patterns considered appropriate for one or more situations. Nozzles may, for example, have circular ends, flared ends for producing a fan-shaped spray, or rectangular ends. Foam dispensing guns and associated nozzles for dispensing two-component polyurethane foams in situ are known to the art and continue to be developed. The components of a two-component polyurethane foam are typically mixed within the dispensing nozzle. While two-component polyurethane foams are employed in some embodiments for forming a splint to immobilize an injured limb, it will be appreciated that foams having compositions other than those disclosed herein may be employed, provided they have the abilities to form an effective splint. As known in the art, the foam chosen for forming a splint in situ should be relatively quick-setting and should not generate temperatures above the range that is comfortable for a patient. In one or more embodiments, the applied foam sets sufficiently within a minute or two in order to stabilize an injured limb or associated extremity such as a hand or foot.

Step125includes controlling the position of the UAV with respect to the injured limb of the patient and spraying the damage coverage area (DCA) with the foam. It further includes discontinuing spraying after a set time has elapsed, when a decision is made is discontinue spraying automatically using visual analytics, or pursuant to instructions received by the UAV from the patient or healthcare professional. In some embodiments, spray duration is set in step120and spraying is discontinued after a set time has elapsed. If the dispensing rate of the foam material is known, the spray duration also determines the amount of foam material that is dispensed. In other embodiments of the method, images of the sprayed area taken by the UAV, possibly in combination with additional factors, are analyzed to determine whether there is sufficient foam coverage of the damaged area. In some embodiments, the processing of visual sensor information, possibly in combination with flow rate, spraying time and/or other factors, using for example a deep neural network, results in the assignment of a confidence score that a foam splint has the desired size (coverage area) and thickness to be effective. Once an appropriate confidence level has been reached that sufficient foam has been applied to form an effective splint, a signal is sent to the actuator for the dispenser68(e.g. foam dispensing gun) and foam application is discontinued by the UAV. An analytics module may be contained within the UAV or a cloud-based system that includes a model configured for analyzing images of the foam splint as it is formed and takes time, flow rate, and/or other available information such as the size of the patient and the foam composition in controlling the application of foam. Feedback from the patient may further be included if available in determining whether to continue spraying the injured limb. Such feedback may relate to whether the damaged area DA is sufficiently covered and/or whether the patient has experienced a reduction in pain. Speech recognition software is employed by the UAV in some embodiments for facilitating operation of the UAV during the application of a foam splint. The UAV is configured to respond to voice commands or other patient feedback (e.g. gestures) in such embodiments by, for example, changing the UAV elevation or its flight path above the patient, discontinuing foam application, or initiating foam application. The use of a UAV having hovering capability is preferred for controlling the formation of the foam splint.

Control of the UAV during step125may be exercised by remotely located healthcare professionals using a device80as described above in some embodiments. A high-definition video feed to a healthcare facility or cloud-based system accessed by a healthcare professional is employed to observe the spraying operation and splint formation. Based on such observations, the UAV and/or spraying equipment is maneuvered with respect to the patient to form a splint having the desired configuration and properties. In some embodiments, the UAV is positioned just above the patient and traverses an injured limb while the foam spray is applied. In other embodiments, the UAV is maintained (hovers) in a stationary position just above the patient and the spray gun or nozzle is articulated to cover the damage coverage area DCA with foam. Such UAV/spray gun movements and foam spray emission can be controlled by a remotely located healthcare professional, by the patient, or the UAV itself. The spray pattern determined in step120is stored in the UAV memory78or the remote device memory86in some embodiments such that the UAV automatically forms a foam splint according to the spray pattern through movement of the UAV and/or spray nozzle(s) with respect to the injured limb. The spray pattern and/or the amount of foam dispensed can be adjusted based on feedback from the patient and/or further analysis of the visual images obtained by the UAV. The foam splints formed by the UAV do not envelope the entireties of the damaged areas in at least some embodiments so that possible further swelling of the injured limb is allowed to minimize pain. The splints are formed either on the patient's skin or over the patient's clothing. In some embodiments, the UAV60is employed to form a spine board by applying foam to the back of a patient followed by contacting the foam with a board before the foam sets. A person at the injury location L would need to assist the patient with the positioning of such a board.

Step130involves taking further ameliorative action(s) using the UAV60or other UAVs. The action(s) are responsive to the detection of further injuries such as traumatic vascular injuries on the patient and/or environmental factors potentially affecting the injured patient. In one exemplary embodiment, the UAV60sensors include a volatile hydrocarbon sensor that detects gasoline at the location L. This information is possibly supplemented by data received from other UAV sensor(s) such as visual data obtained from a digital camera that affect the confidence level of a possible decision to take further ameliorative action. Ameliorative actions following the detection of a gasoline spill include the application by the UAV60or another UAV of a fire suppressant at the location L and/or notification of firefighters of the detected issue(s) at the location L. In a second exemplary embodiment, an open wound on the patient is detected by the UAV. Prior to or following formation of the foam splint, the UAV60or another UAV carrying the appropriate payload administers a spray containing an antibiotic, a blood clotting formulation, an anesthetic, or other material considered likely to benefit the patient.

It will be appreciated that, while the UAV60is capable of forming foam splints for immobilizing a patient's limbs and/or back, it may alternatively be deployed for other purposes in which the application of a foam spray would be beneficial. For example, the foam could be applied to insulate downed electrical wires, plug leaks in tanks or lines, provide a protective covering over jagged metal or broken glass to facilitate rescue, to make a damaged infrastructure safer for first responders, or to cover potentially hazardous materials at the location L. The UAV60may be part of a system including a plurality of UAVs for responding to natural disasters, accident scenes, or other situations involving a large number of injuries. The system may coordinate a group of UAVs attending injured patients, optimizing and prioritizing application of foam and/or other agents, thereby performing triage. For example, the needs of each injured person at an accident scene can be assessed and prioritized where the supply of foam and/or other agent is limited. The system may include specialized UAVs for assessing injuries and other UAVs configured for addressing the assessed injuries through the application of foam and/or other agents such as antibiotics, anesthetics or hemostatic compositions. It will accordingly be appreciated that, in some embodiments, a first UAV is used to obtain data relating to the physical characteristics of a patient while a second UAV is used to take ameliorative action with respect to the patient based on assessments based on the physical data. A smaller, lighter UAV may accordingly be used to obtain visual images and/or sound data. Larger, heavier UAVs carrying payloads of foam-creating materials or other treatment materials would then be used to treat the patient. A UAV that performs both functions could, in real-time, detect and then quickly help the patient, particularly is diagnosis if conducted using analytic software as opposed to manual examination by a healthcare professional who may not be immediately available. There would accordingly be very little delay between detection and aid rendered to the patient.

Given the discussion thus far and with reference to the exemplary embodiments discussed above and the drawings, it will be appreciated that, in general terms, an exemplary method for assisting an injured patient includes obtaining an unmanned aerial vehicle60that includes a payload66including one or more materials for forming a polymer foam, a dispenser68such as a spray gun with attached nozzle for dispensing the one or more materials in the payload, and one or more sensors. The unmanned aerial vehicle is flown to a location L in proximity to a patient and the patient65is identified at the location L. Foam is dispensed on a limb of the patient using the dispenser by dispensing the one or more materials in the payload, thereby forming a foam splint. Physical characteristics data relating to the patient based on an output of the one or more sensors is obtained in one or more embodiments. Based on the physical characteristics data, it is determined whether it is likely that a limb of the patient is injured. The one or more materials for forming the polymer foam on the patient are dispensed if it is determined that the limb of the patient is likely injured to form a foam splint. The physical characteristics data may be based on digital images of the limb which are assessed remotely in some embodiments. The digital images, which may be included in audiovisual transmissions by the UAV, include external features of the limb and/or internal features of the limb. In some embodiments, at least one of the sensors62is an acoustic sensor and the method further includes detecting sounds at the location using the acoustic sensor, analyzing the detected sounds using speech recognition software, and actuating the dispenser based on the analysis of the detected sounds by the speech recognition software. The method may further include obtaining digital images of the foam splint from the one or more sensors62during the forming of the foam splint, and discontinuing forming the foam splint based on the digital images. The step of determining whether it is likely that a limb of the patient is injured further includes using a neural network classification to diagnose a particular injury from the physical characteristics data in some embodiments. The decision as to whether to dispense foam from the UAV involves consideration of images of a patient's limb, the position of the patient and/or the patient's possibly injured limb, and the patient's emotional state as determined using facial expression software in some embodiments. The patient's indication of pain, which may be obtained via speech recognition processing, may further influence the decision to dispense foam on an injured limb. The electronic processing of the data obtained by the sensors, either by the UAV or at a remote location, facilitates prompt decision-making as to whether or not a foam splint is to be formed. In some situations, electronic processing using convolutional neural network classifiers or other statistical classifiers, facial expression modules, and/or other computer-controlled techniques to determine the patient's position, injuries, and cognitive state do not allow a high confidence decision to be made. In such situations, high definition images of the patient and location can be transmitted by the UAV to a healthcare professional for review. The healthcare professional could confer with the patient via the UAV, which in some embodiments includes acoustic detectors and speakers for transmitting sound. In some embodiments, speech recognition technology enables the patient or a nearby individual to at least partially control the UAV and its dispensing of foam on an injured limb.

A system for assisting an injured patient includes an unmanned aerial vehicle60including one or more materials for forming a polymer foam, a dispenser68for dispensing the one or more materials in the payload, and one or more sensors62. A processor is configured for processing outputs from the one or more sensors to: 1) identify a patient at a location using the one or more sensors; 2) obtain physical characteristics data relating to the patient based on an output of the one or more sensors, and 3) determine whether it is likely that a limb of the patient is injured based on the physical characteristics data. The UAV further includes an actuator70that, in some embodiments, is controlled by the processor. The actuator70is provided for actuating the dispenser68. The sensors include an image sensor and the UAV includes a transmitter72configured for transmitting data obtained by the image sensor. The sensors further include an acoustic sensor, the transmitter being further configured for transmitting data obtained by the acoustic sensor. Acoustic sensors may be used to detect environmental conditions at the injury location L and/or to permit audio communication between the patient and the UAV. The UAV includes a facial expression module configured for analyzing data obtained by the image sensor in some embodiments, thereby facilitating identification of emotional states. The image sensor can be configured for detecting visible light, backscattered xrays, infrared radiation, and/or other radiation facilitating analysis of the patient or patient injuries. In some embodiments, the UAV sensors further include a chemical sensor for detecting volatile hydrocarbons.

The above-described embodiments are intended to be illustrative only. Other embodiments may, for example, utilize different materials and processing steps from those expressly set forth above to achieve embodiments falling within the scope of the present disclosure. These many alternative embodiments will be apparent to one having ordinary skill in the relevant arts.

All the features disclosed herein may be replaced by alternative features serving the same, equivalent, or similar purposes, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivale The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. Terms such as “above” and “below” are used to indicate relative positioning of elements or structures to each other as opposed to relative elevation. It should also be noted that, in some alternative implementations, the steps of the exemplary methods may occur out of the order noted in the figures. For example, two steps shown in succession may, in fact, be executed substantially concurrently, or certain steps may sometimes be executed in the reverse order, depending upon the functionality involved.

Characteristics are as Follows:

Service Models are as Follows:

Deployment Models are as Follows:

Any element in a claim that does not explicitly state “means for” performing a specified function or “step for” performing a specified function is not to be interpreted as a “means for” or “step for” clause as specified in AIA 35 U.S.C. § 112(f). In particular, the use of “steps of” in the claims herein is not intended to invoke the provisions of AIA 35 U.S.C. § 112(f).