There is provided a human falling detection system including an image sensor, a thermal sensor and a microphone. The image sensor captures an image frame that is used to identify a face and a height-width ratio of a human image. The thermal sensor is used as a filter for filtering out a living body and captures a thermal image that is used to identify a height-width ratio of a human thermal image. The microphone records a time stamp of an abrupt sound appearing.

The present technology relates to the field of medical monitoring. Patient monitoring systems and associated devices, methods, and computer readable media are described. In some embodiments, a patient monitoring system includes one or more sensors configured to capture first data related to a patient and a monitoring device configured to receive the first data. In these and other embodiments, the patient monitoring system can include an image capture device configured to capture second data related to the patient. In these and still other embodiments, the one or more sensors can be configured to instruct the patient monitoring system to display the second data.

An information collecting apparatus according to an embodiment includes a sensor data acquisition unit configured to acquire sensor data related to work performed by a worker, a work information data generation unit configured to generate work information data related to work performed by the worker for a specific period of time based on the sensor data acquired by the sensor data acquisition unit, and a danger information specification unit configured to specify work information data related to an action that indicates danger as dangerous work information data indicating dangerous work performed by the worker when an action of the worker in a predetermined period of time, which is indicated by the work information data generated, is the action that indicates danger.

One or more techniques and/or systems are disclosed for providing for improved monitoring of an individual, wherein imaging data is received from a plurality of imaging sensors. The received imaging data is aggregated and patterns at targeted data segments of the aggregated imaging data are analyzed using one or more algorithms to determine one or more values. The one or more values determined from the analyzed patterns are classified to represent at least one of a physiological state determination or a positioning of the individual. Data corresponding to the classified one or more values is then output and displayed.

Methods, apparatus, systems, and articles of manufacture are disclosed for non-contact sensing of vital signs. An example electronic device to measure vital signs includes a camera to capture an image; a radar antenna to transmit and receive radar signals; and processing circuitry to: identify a subject in the image; identify a location of the subject in an environment; control the radar antenna to steer the radar signals toward the location; and determine a vital sign of the subject based on a reflected radar signal.

To provide a technology that can easily acquire body weight information of a patient. A processing part that deduces the body weight of a patient based on a camera image of the patient lying on a table of a CT device, including a generating part that generates an input image based on the camera image, and a deducing part that deduces the body weight of the patient when the input image is input into a learned model. The learned model is generated by a neural network executing learning using a plurality of learning images C1 to Cn generated based on a plurality of camera images, and a plurality of correct answer data G1 to Gn corresponding to the plurality of learning images C1 to Cn, where each of the plurality of correct answer data G1 to Gn represents a body weight of a human included in a corresponding learning image.

An effect of a treatment on an organ, e.g., a lung, is assessed by acquiring a first measurement for each of a plurality of regions of the organ, and then acquiring a second measurement for each of the plurality of regions of the organ, after acquisition of the first measurements. A regional change measurement is obtained for each of the plurality of regions of the organ based on the first measurement and the second measurement of the region. A treatment effect is then determined based the plurality of regional change measurements and treatment information of the treatment delivered to the organ.

Disclosed is a biosensor arrangement structure, including: a plurality of biosensors that is disposed in a seat for supporting an occupant and that measures a health condition of the occupant. The seat includes: a seat body for holding the occupant; and an auxiliary supporter for supporting a body part of the occupant except for a torso and thighs. At least one of the plurality of biosensors is disposed at the auxiliary supporter.

A system for screening persons' temperatures comprises an imaging device configured to generate imagery data, including infrared image data, of a first scene and a second scene. The system receives imagery data from the first scene, identifies persons in the scene using the imagery data, and determines each person's temperature. The system further compares the persons' temperatures to a threshold and indicates a person should be directed to the second scene if the person's temperature is above a threshold. Subsequently, the system determines and compares the person's temperature in the second scene and provides an indication if the person's temperature exceeds a second threshold. Further, a confidence factor can be associated with determinations of persons' temperatures.

A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.