An apparatus and a method of measuring bioinformation are provided. The apparatus for measuring bioinformation includes a first sensor configured to measure a first biosignal including arterial pulse wave information, a second sensor configured to measure a second biosignal including venous or capillary pulse wave information, and a bioinformation estimator configured to estimate bioinformation of a user based on a time delay between the first biosignal and the second biosignal.

An aerosol delivery device is provided. The aerosol delivery device comprises a control component and a digital pressure sensor. The digital pressure sensor is configured to measure a pressure imposed thereon, and generate a corresponding signal that indicates the pressure so measured. The control component or the digital pressure sensor is further configured to control at least one functional element of the aerosol delivery device based on the pressure indicated by the corresponding signal, or a condition of the aerosol delivery device or a user thereof determined from the corresponding signal. Control of the at least one functional element includes output of the pressure or the condition for presentation by a display.

Pressure sensing guidewire assemblies are described herein where the guidewire assembly may be comprised of an elongate guidewire body and multiple pressure sensors secured near or at a distal end of the guidewire body. The signals obtained from the guidewire connectors and aortic sensor modules may be synchronized to minimize signal acquisition delays. The signals may be further processed to equalize the pressure waveforms by shifting the connector waveform to align correctly with the aortic module waveform and improve output signals.

A medical device includes an elongate device and one or more processors coupled to the elongate device. The elongate device includes a steerable distal end and a shape sensor located along a length of the elongate device. While the elongate device is being traversed through one or more passageways of a patient, the one or more processors are configured to, based on information from a sensor, monitor an insertion motion of the elongate device, detect a data collection event, and capture, in response to detecting the data collection event, a plurality of points along the length of the elongate device using the shape sensor. The data collection event is at least partially based on a change in direction of the insertion motion of the elongate device.

A gripping force measurement device includes a body portion and a plurality of pressure sensors. The plurality of pressure sensors includes at least one first sensor and two or more second sensors including pressure-sensitive surfaces oriented in the same direction. A pressure-sensitive surface of the first sensor and the pressure-sensitive surfaces of the two or more second sensors are disposed such that, when a subject pressurizes the pressure-sensitive surface of the first sensor and the pressure-sensitive surfaces of the two or more second sensors, the gripping force measurement device is grippable by the subject.

Methods based on providing monitoring services and access to information to caregivers, patients with implanted pressure sensors and distributors.

According to one aspect of the present disclosure, an electronic device may include: a communication interface configured to receive, from a touch pen, force information about a force of the touch pen exerted onto an object when the object is in contact with the touch pen; a pulse wave measurer configured to measure a pulse wave of the object when the object is brought into contact with the electronic device by the force of the touch pen; and a processor configured to estimate a blood pressure of the object based on the force information and the pulse wave.

This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.

In one aspect, a device for measuring breast volume is disclosed, which comprises a housing comprising an enclosure having an opening, a flexible membrane sealingly covering said opening so as to provide an enclosed space within said enclosure, a gas disposed within said enclosed space, and at least one pressure sensor coupled to said enclosure so as to measure pressure of said gas within said enclosed space. The flexible membrane is configured to reversibly flex into said enclosed space in response to pressure of a breast against the membrane. The flexure of the membrane causes a change in the pressure and volume of the gas within said enclosed space.

An apparatus for estimating bio-information is provided. According to an embodiment of the present disclosure, the apparatus for estimating bio-information includes: a pulse wave sensor having a plurality of channels to measure a plurality of pulse wave signals from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the pulse wave sensor; and a processor configured to: obtain a first feature for each channel by inputting the plurality of pulse wave signals for each channel and the force signal, into a first neural network model; obtain a weight for each channel by inputting the first feature to a second neural network model; obtain a second feature by applying the weight to the first feature for each channel by using the second neural network model; and obtain bio-information by inputting the second feature to a third neural network model.