The present disclosure relates to a smart tunnel having a tunnel structure having a quarantine section and a disinfection section, which is capable of regularly performing a quarantine function for determining whether an infectious disease is introduced and a disinfection function for preventing spread of an infectious disease.

A method includes generating, via a camera, image data that is reproducible as an image of at least a portion of a subject. The method also includes receiving, via a sensor, first biomeasurement data associated with the subject, the first biomeasurement data including a first biomeasurement of the subject. The method also includes verifying the first biomeasurement of the subject based at least in part on a comparison between at least a portion of the image data and at least a portion of the first biomeasurement data.

A physiological acoustic monitoring system receives physiological data from an acoustic sensor, down-samples the data to generate raw audio of breathing sounds and compresses the raw audio. The acoustic monitoring system has an acoustic sensor signal responsive to tracheal sounds in a person. An A/D converter is responsive to the sensor signal so as to generate breathing sound data. A decimation filter and mixer down-samples the breathing sound data to raw audio data. A coder/compressor generates compressed audio data from the raw audio data. A decoder/decompressor decodes and decompresses the compressed audio data into decompressed audio data. The decompressed audio data is utilized to generate respiration-related parameters in real-time. The compressed audio data is stored and retrieved so as to generate respiration-related parameters in non-real-time. The real-time and non-real-time parameters are compared to verify matching results across multiple monitors.

An active medical device, comprising a processor, a memory unit, and at least one of an accelerometer and a detection unit configured to detect a body impedance. During operation, the active medical device carries out the following steps a) measuring a body impedance of a patient with the detection unit during a first period of time to obtain time-dependent impedance data and calculating a power spectral density of the impedance data; b) alternatively or additionally, to step a), measuring an acceleration of a body of the patient with the accelerometer during the first period of time to obtain time-dependent acceleration data and calculating a power spectral density of the acceleration data; c) identifying coughing of the patient on the basis of the calculated power spectral density if at least 1% of all values of the power spectral density have a frequency of at least 1 Hz.

A method for determining a disease state prediction, relating to a potential disease or medical condition of a subject, includes accessing a set of subject images, the subject images capturing a part of a subject's body, and accessing a set of clinical factors from the subject. The clinical factors are collected by a device or a medical practitioner substantially contemporaneously with the capture of the subject images. The subject images are inputted into an image model to generate disease metrics for disease prediction for the subject. The disease metrics generated by the image model and the clinical factors are inputted into a classifier to determine the disease state prediction, and the disease state prediction is returned.

A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

A method of predicting a personalized pollen allergy includes generating a personal allergic symptom diary by recording a daily allergic symptom and daily drug taking information of a user, calculating a daily symptom index using a pollen calendar of a region corresponding to a location of the user and the daily allergic symptom, extracting allergy generation risk grades for each pollen generation species and allergy-sensitive tree species of the user by using the pollen generation species and a pollen generation grade extracted from the pollen calendar, and the daily symptom index, and generating a personalized pollen calendar based on the extracted information, and generating a personalized risk forecast for each city and county for the user by applying the allergy generation risk grades for each pollen generation species and the allergy-sensitive tree species to a Metrological Administration pollen forecast.

A system is provided to provide triage recommendations for animals. The system accesses a syndrome mapping of syndromes to complaints, each syndrome having a triage category. The system accesses a complaint mapping of complaints to symptoms. Each symptom for a complaint has a weight. The system receives indications of a current complaint and current symptoms of an animal. Each current symptom has a score. The system identifies the triage category based on a syndrome to which the current complaint and the current symptoms apply, factoring in the weights and the scores. The system provides a recommendation for the animal based on the identified triage category.

A system for quantitatively measuring dyspnea experienced by a person includes a hemodynamic sensor system configured to be held in close proximity to, or in contact with, the person's head or neck, and measure hemodynamic parameters of the person. The system includes a respiratory sensor system to measure respiratory metrics of the person, and a data processor configured to communicate with the hemodynamic sensor system to receive the hemodynamic parameters and with the respiratory sensor system to receive the respiratory metrics. The data processor is configured to calculate, based on the hemodynamic parameters, a quantitative score to represent physical discomfort experienced by the person, and make a determination, based on the quantitative score and the respiratory metrics, that the physical discomfort is caused by dyspnea rather than physical pain. The determination includes a sub-determination that the respiratory metrics have changed by a particular amount within a particular period of time.

A method includes obtaining, by an electronic device, an audio segment comprising one or more audio events of a target subject. The method also includes extracting, by the electronic device, audio embeddings from the one or more audio events using an embedding model, the embedding model comprising a trained machine learning model. The method further includes comparing, by the electronic device, the extracted audio embeddings with a match profile of the target subject, the match profile generated during an enrollment stage. The method also includes generating, by the electronic device, a label for the audio segment based on whether or not the extracted audio embeddings match the match profile, wherein the label enables correlation of the audio segment with the target subject for monitoring a health condition of the target subject.