In one embodiment, a monitoring system includes a monitoring device configured to removably attach to a tracheotomy tube, the monitoring device including a skin sensor configured to detect contact with skin of a patient's neck.

An example of a system for monitoring and treating respiratory distress in a patient may include signal inputs, a signal processing circuit, and a respiratory distress analyzer. The signal inputs may be configured to receive patient condition signals indicative of autonomic balance of the patient. The signal processing circuit may be configured to process the patient condition signals and to generate patient condition parameters indicative of the autonomic balance using the processed patient condition signals. The respiratory distress analyzer may be configured to determine a state of the respiratory distress using the patient condition parameters, and may include a parameter analysis circuit configured to analyze the autonomic balance of the patient and to determine the state of the respiratory distress using an outcome of the analysis.

A method of managing a medical suction device through a network may be embodied by the management server through the steps of: storing operation information of at least one medical suction device in a storage unit provided in the management server, generating an operation start determination reference value of the medical suction device based on the operation information, receiving information on conditions of a patient from a medical suction device installed at an outside, and determining whether to start an operation of the medical suction device that has transmitted the information on conditions of the patient based on the operation start determination reference value and the information on conditions of the patient.

An automated driving vehicle (200) includes a communication device (220), a biometric information acquirer (240), and an automated driving controller (250). The communication device (220) is configured to transmit biometric information acquired by the biometric information acquirer (240) to an external device and receives a response signal including attribute information for the transmitted biometric information. The automated driving controller (250) is configured to execute automated driving according to route information formed on the basis of the attribute information included in the received response signal.

A sensor system may have a force sensor formed from a piezoelectric film. The piezoelectric film may comprise a number of tuned microstructures that are configured to resonate at a particular frequency. In accordance with the tuning of the microstructures, frequency signals corresponding to the microstructure resonance may be mechanically amplified before being processed by associated processing electronics. The processing electronics may be configured to identify a type of biological vibration detected by the force sensor.

A method for predicting sleep apnea from neural networks that mainly includes the following steps: a) retrieving an original signal; b) retrieving at least one snoring signal from the original signal by a snoring signal segmentation algorithm and converting the snoring signal into one with one-dimensional vector; c) applying a feature extraction algorithm to process the snoring signal with one-dimensional vector and transform the snoring signal into a feature matrix of two-dimensional vectors; and d) classifying the feature matrix by a neural network algorithm to obtain the number of times of sleep apnea and sleep hypopnea from the snoring signal. The method thereby is able to decide whether the snoring signal has revealed indications of sleep apnea or sleep hypopnea or not.

Trained machine learning models can be used for analysis of signals obtained through an in-ear or on-body device. Signals can be analyzed to determine information related to activities such as eating, chewing, drinking, coughing, or sneezing. In addition, data from an in-ear thermometer or other data sensors can be analyzed in conjunction with the machine learning models to provide data or recommendations to a user on a user device or initiate an action.

The disclosed technology provides solutions for protecting the health of ride-sharing passengers by detecting passenger illnesses, and taking precautions to safely address potentially exposed vehicles. A process of the disclosed technology can include steps for: collecting sensor-data corresponding with one or more AV passengers, determining a likelihood that at least one of the AV passengers is suffering from a physical illness, and transmitting a wellness notification to a fleet management system if the likelihood exceeds a predetermined threshold. Systems and machine-readable media are also provided.

The present invention relates to device, system and method for detecting a cardiac and/or respiratory disease of a subject. The proposed device comprises a sound input (20) for obtaining a sound signal representing sounds generated by the subject's body; a motion input (21) for obtaining a motion signal representing motions generated by the subject's body; and a processor (22) for processing the obtained sound signal and motion signal. This processing includes identifying inhalation and/or exhalation periods of the subject based on the motion signal, detecting abnormal lung sounds during inhalation and/or exhalation periods based on the sound signal, determining abnormal lung sound characteristics of the detected abnormal lung sounds, determining breathing characteristics of the subject's breathing based on the sound signal, determining the phase of the abnormal lung sounds in the inhalation-exhalation cycle, and detecting a cardiac and/or respiratory disease of the subject based on the determined abnormal lung sound characteristics, the determined breathing characteristics and the determined phase of the abnormal lung sounds in the inhalation-exhalation cycle.

A blood pressure measurement device measures a blood pressure of a subject by a blood pressure measurement unit, detects a body sound of the subject during blood pressure measurement by a sound detection unit. The measured blood pressure and the detected body sound are recorded in association with each other by time information. The factor for the increase of measured blood pressure value can be specified by showing the chronological blood pressure measurement result along with checking the presence or absence of a body sound such as snoring etc.