An electrocardiogram measurement device according to an aspect of the present disclosure is a portable electrocardiogram measurement device, and the electrocardiogram measurement device includes: a first electrode disposed at a position that can be touched with one upper limb of a measurement subject; a second electrode disposed at a position that can be touched with the other upper limb of the measurement subject; a calculation unit that calculates a potential difference between the first electrode touched with the one upper limb of the measurement subject and the second electrode touched with the other upper limb of the measurement subject; a guide unit that generates guidance information to guide the measurement subject to bring the other upper limb into contact with a predetermined portion of the second electrode; and a display unit that displays the potential difference calculated by the calculation unit and/or the guidance information generated by the guide unit.

A system, method and computer readable medium for emergency health response, including sensors for measuring health conditions of a user, a local machine learning device to predict abnormalities in health status of the user based on the measurements, a communications device for transmitting an emergency alert message to emergency response providers that are within range of the communications device, and for receiving response messages from emergency response providers that are available to provide emergency treatment. A health condition controller selecting a provider. When the provider is a hospital, the subject vehicle will set its destination to the hospital and will transmit health status information of the user to the provider. When the provider is an emergency response vehicle, the subject vehicle will communicate coordinates as a meeting destination for meeting the provider response vehicle and will transmit health status information of the user to the provider response vehicle.

A system, method and computer readable medium for emergency health response, including sensors for measuring health conditions of a user, a local machine learning device to predict abnormalities in health status of the user based on the measurements, a communications device for transmitting an emergency alert message to emergency response providers that are within range of the communications device, and for receiving response messages from emergency response providers that are available to provide emergency treatment. A health condition controller selecting a provider. When the provider is a hospital, the subject vehicle will set its destination to the hospital and will transmit health status information of the user to the provider. When the provider is an emergency response vehicle, the subject vehicle will communicate coordinates as a meeting destination for meeting the provider response vehicle and will transmit health status information of the user to the provider response vehicle.

An ECG system and a method for operating a handheld device that provides input to an ECG system are disclosed. The handheld device has a plurality of receiving channels. Each receiving channel includes an electrode that is adapted for receiving electrical signals from a patient's body when the electrode is pressed against the patient's body at a predetermined location on the patient's body. The method includes monitoring an output signal from each of the channels for any of a plurality of invalid signal conditions during a period of time in which the output signal is used to generate a standard lead or precordial lead trace, signaling a user that the handheld device is improperly positioned on the patient's body, and instructing the user on how to correct a placement of the handheld device based on the detected invalid signal condition.

Automatically determining a medical recommendation for a patient based on multiple medical images from multiple different medical imaging modalities. In some embodiments, a method may include receiving a first and second medical images of a patient from first and second medical imaging modalities, mapping a first region of interest (ROI) on the first medical image to a second ROI on the second medical image, generating first annotation data related to the first ROI and second annotation data related to the second ROI, generating first medical clinical data related to the first ROI and second medical clinical data related to the second ROI, inputting, into a machine learning classifier, the first and second annotation data and the first and second medical clinical data, and automatically determining, by the machine learning classifier, a medical recommendation for the patient related to a medical condition of the patient.

A grip-type electrocardiographic measuring device includes a main body portion that has a spheroid shape, a plate-shaped flange portion mounted on a side surface of the main body portion, a first electrocardiographic electrode disposed at a back surface side of the main body portion and making contact with one hand when the main body portion is gripped by the one hand, and a second electrocardiographic electrode disposed on a surface of the flange portion and making contact with a finger of the other hand when the flange portion is pinched by the other hand. The flange portion has flexibility and is bendable from a mounting portion on the main body portion.

A seat assembly with an adjustable head restraint assembly capable of being adjusted based on an approximate stature or spatial location of an occupant in the seat assembly. A seat assembly may include a sensor in the seat to detect if an occupant is present. The sensor may include a plurality of bladders and pressure sensors, a radar system, or a neuro-monitoring sensor. The sensor may send a signal to a controller indicating the presence of an occupant. The controller may approximate the size and/or spatial location of the occupant and send an adjustment signal to the head restraint adjustment mechanism to adjust the head restraint assembly to an in-use position. The controller may determine that an occupant is not present and send an adjustment signal to the head restraint adjustment mechanism to adjust the head restraint assembly to a non-use position.

A bio-electrode composition contains: (A) a polymer compound containing a repeating unit-a having a structure selected from the group consisting of salts of ammonium, sodium, potassium, and silver formed with any of fluorosulfonic acid, fluorosulfonimide, and N-carbonyl-fluorosulfonamide; and (B) a silicone compound having a polyglycerin structure. This bio-electrode composition is able to form a living body contact layer for a bio-electrode which enable quick signal collection after attachment to skin.

A bio-electrode composition contains: (A) a polymer compound containing a repeating unit-a having a structure selected from the group consisting of salts of ammonium, sodium, potassium, and silver formed with any of fluorosulfonic acid, fluorosulfonimide, and N-carbonyl-fluorosulfonamide; and (B) a silicone compound having a polyglycerin structure. This bio-electrode composition is able to form a living body contact layer for a bio-electrode which enable quick signal collection after attachment to skin.

A wearable body composition measurement system includes: a sensor device including a first measurement circuitry configured to measure body composition; a second measurement circuitry configured to measure heart activity; and a strap arranged to attach the sensor device around a torso of a user; at least one processor coupled with the sensor device; and at least one memory storing a computer program code configured to cause the at least one processor to perform operations comprising: in a first measurement mode wherein the sensor device is attached around an abdominal area of the user, receiving body composition measurement data measured by the first measurement circuitry, computing a quantity of fat tissue in the abdominal area on the basis of the received body composition measurement data, and outputting the quantity of fat tissue via an interface; and in a second measurement mode wherein the sensor device is attached around a chest of the user, receiving heart activity measurement data from the second measurement circuitry, computing at least one heart activity parameter on the basis of the received heart activity measurement data, and outputting the at least one heart activity parameter via the interface.