A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

A method of measuring signals from a surface. The method comprises: placing on the surface a flexible sensing device having an array of coated electrodes, wherein at least one electrode of the array is metallic and is at least partially coated by a polymer; and collecting signals from the sensing device.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

Various techniques are disclosed to provide for improved detection of elevated human body temperatures. In one example, a method includes receiving a thermal image. The method also includes processing the thermal image to detect a person's face and a characteristic associated with the person. The method also includes selecting a circadian rhythm model associated with the detected characteristic.

The method also includes determining an expected body temperature using the circadian rhythm model. The method also includes extracting a temperature associated with the person's face from the thermal image. The method also includes comparing the extracted temperature with the expected body temperature to detect an elevated body temperature condition. Additional methods and systems are also provided.

In a configuration in which a holder holding a controller is mounted on a seat part with a plate-shaped member, the exposure of the mounting part of the plate-shaped member on which the holder is mounted is eliminated. A seat device includes a pressure sensor measuring a value relating to the seated person's state, a vibration imparting device performing a vibration imparting operation, an ECU controlling the vibration imparting device corresponding to the measurement result of the pressure sensor, a holder holding the ECU, and a mounting bracket fixed to a lower frame such that the holder is mounted on the lower frame of a seat part. The mounting bracket includes a mounting projection on which side wall of the holder is mounted in a predetermined mounting direction. When the side wall is mounted on the mounting projection, the mounting projection is covered with the side wall.

A robot according to the present disclosure comprises: a microphone; a camera disposed to face a predetermined direction; and a processor configured to: inactivate driving of the camera and activate driving of the microphone, if a driving mode of the robot is set to a user monitoring mode; acquire a sound signal through the microphone; activate the driving of the camera based on an event estimated from the acquired sound signal; confirm the event from the image acquired through the camera; and control at least one constituent included in the robot to perform an operation based on the confirmed event.

A wearable device for healthcare and a method thereof, wherein the device is worn on a finger for measuring the health data of the user, including but not limited to, the heart rate, blood oxygen saturation, etc. The wearing size of the device is adjustable for different sizes of fingers. In one embodiment, the device includes a main body at least partially worn on a digit of a user; at least one physiological sensor attached to the main body for detecting physiological information; and at least two branches coupled to the main body for holding the digit while reducing the movement of the device. At least a part of at least one branch is changeable such that the wearing size of the device is adjustable for different sizes of digits. In another embodiment, at least a part of at least one branch is movable such that the wearing size of the device is adjustable for different sizes of digits.

Provided are systems for grouping users who have chosen to participate into one of a plurality of menstrual cycle groups based on data provided by and/or collected from those users. In some examples, a wearable computing device can include one or more sensors that can measure one or more physiological signals associated with the user. Based on the physiological signals gathered from the one or more sensors, the wearable computing device can determine biometric data for one or more users. Furthermore, the wearable computing device can enable a user to submit information about their menstrual cycle (e.g., via an interactive touch screen). These factors can be used to automatically determine some menstrual cycle data for a user.

Methods, systems, and devices for temperature analysis are described. The method may include receiving physiological data associated with a user collected via a first set of sensors of a wearable device. The physiological data may include skin temperature data. The method may include receiving surrounding temperature data associated with an environment surrounding the user. The surrounding temperature data may be collected via the first set of sensors, a second set of sensors, or both. The method may additionally include identifying one or more physiological characteristics associated with the user based at least in part on a comparison of the skin temperature data and the surrounding temperature data, and causing a graphical user interface (GUI) of a user device to display an indication of the one or more physiological characteristics, a message or alert associated with the one or more physiological characteristics, or both.

The present invention provides for a data acquisition system for EEG and other physiological conditions, preferably wireless, and method of using such system. The wireless EEG system can be used in a number of applications including both studies and clinical work. These include both clinical and research sleep studies, alertness studies, emergency brain monitoring, and any other tests or studies where a subject's or patient's EEG reading is required or helpful. This system includes a number of features, which enhance this system over other systems presently in the marketplace. These features include but are not limited to the having multiple channels for looking at a number of physiological features of the subject or patient, a built in accelerometer for looking at a subject's or patient's body motion, a removable memory for data buffering and storage, capability of operating below 2.0 GHz, which among other things allows for more channels, movement artifact correction including video, pressure sensors capable of measuring or determining airflow, tidal volume and ventilation rate, and capability of manual and automatic RF sweep.