A health monitoring system comprises at least one sensor configured to measure at least one parameter relating to a condition of a subject; and a health monitoring module in communication with the at least one sensor and configured to receive from the at least one sensor a measurement of the at least one parameter; based on the measurement, generate a message; and send the message to a health operator related to the subject. Moreover, in some embodiments, the health monitoring module is further configured to determine a confidence factor for the measurement. Further, in some embodiments the health monitoring module is also configured to receive from the at least one sensor a plurality of measurements of the at least one parameter; derive a trend for the plurality of measurements; and based on the trend, determine a health state for the subject.

The abnormal data processing system is provided with: a storage unit for holding a multiple-subject DB in which data on multiple subjects are accumulated and individual-subject DB in which data on individual subjects are accumulated; an individual-subject DB divergence-degree calculation unit for calculating an individual-subject DB divergence degree which is the degree of divergence of the new data from the individual-subject DB; a multiple-subject DB divergence degree calculation unit for calculating a multiple-subject DB divergence degree which is the degree of divergence of the new data from the multiple-subject DB; and a composite divergence degree calculation unit for determining a composite divergence by compositing the individual-subject DB divergence degree and the multiple-subject DB divergence degree using the number of data instances in the individual-subject DB. The abnormal data processing system determines whether or not the new data is abnormal on the basis of the composite divergence degree.

System and method for capturing a movement sequence of a person. The method comprises capturing a plurality of images of the person executing a movement sequence by means of a contactless sensor, the plurality of images representing the movements of the body elements of the person, generating at least one skeleton model having limb positions for at least some of the plurality of images, and calculating the movement pattern from the movements of the body elements of the person by comparing changes in the limb positions in the at least one skeleton model generated. In addition, vital signs and/or signal processing parameters of the person can be acquired and evaluated.

The in-home care of seniors is augmented using Internet of Things (IOT) technologies. In-home sensors monitor a senior and their caregiver. Physical conditions and psychological conditions may be monitored. In some implementations, a machine learning system has a classifier trained to detect a specified condition, such as depression. The system may perform various transformations of raw sensor data into a format indicative of a particular condition. In one implementation, a psychological or medical condition has symptoms in which each symptom has one or more measurable events. Mappings between symptoms, events, sensor data, and sensor transformation functions may be supported.

A method, system, apparatus, and/or device that may include: a first sensor operable to take a first physiological measurement; a second sensor operable to take a second physiological measurement; and a processing device operatively coupled to the first sensor and the second sensor. The processing device may be operable to: receive a first measurement data for the first physiological measurement; receive a second measurement data for the second physiological measurement; generate an event data set based on the first measurement data with the second measurement data; determine an event occurred based on the event data set; determine a type of the event; and in response to the event being a safety event: determine a type of the safety event; identify a risk level of the safety event; and in response to the risk level exceeding a threshold level, notify a second device of the safety event.

Provided is a 3D bone density and bone age calculation apparatus using an artificial intelligence-based rotation manner. The 3D bone density and bone age calculation apparatus includes a main body, and the main body includes a rotary drum including a drum shaft gear, an X-ray generator, an intensifying screen, and an image data capturer, a drum driver including a motor shaft gear connected to the drum shaft gear so as to rotate the rotary drum, a motor, support rollers and one of an origin sensor and an encoder, an outer case and an inner case, a front case and a rear case, a capturing holder, and a controller configured to select an image-captured position of the rotary drum, and configured to input a current age, sex and nutritional status of a patient, etc. The controller includes a display configured to display captured images and a diagram indicating bone age.

Embodiments of the present disclosure relate generally to systems and methods of measuring speed and, more particularly, to systems and methods of measuring gait speed using a plurality of sensors. The systems described herein may include sensing units comprising one or more motion sensors for detecting a patient walking along a testing distance. In some embodiments, a controller may calculate a gait speed based at least in part on the testing distance and on signals received from the sensors. In some embodiments, an initiation input may be provided to activate the systems. In some embodiments, the initiation input may also provide a target to which a patient can walk.

A pessary system for providing pelvic floor support for USL and other ligaments. The pessary has an elongated probe with independently inflatable balloons each located substantially the same distance from the insertion end of the probe and which inflate into separate radial sectors. The probe can be inserted into a vaginal cavity and the balloons inflated provide mechanical support to the USLs. Independent inflation of each balloon allows the mechanical USL support provided to be varied on left and right sides to compensate for differences in the degree of degradation and positioning of the USL ligaments on either side.

Emotional loneliness is referred as the absence of an attachment figure in one's life and someone to turn to. The existing methods use installation of sensors for tracking the movement, behaviour and activity of the person, but most of the efforts are obtrusive in nature. A non-obtrusive method and system for detection of emotional loneliness of a person have been provided. The disclosure is utilizing multiple varied techniques to understand the emotional loneliness. The multiple techniques comprise room change movement anomalies, living room stay anomalies, correlating the living room stay with the bedroom stay and outdoor movement anomalies. The methodology also ensures reduced variance and false positives, as emotional loneliness is finally determined based on more than two positives of above methods. The detection of person's movement is done using a featured engineered dataset based on collection of raw time series data collected from a plurality of motion sensors.

A pessary system for providing pelvic floor support for USL and other ligaments. The pessary has an elongated probe with independently inflatable balloons each located substantially the same distance from the insertion end of the probe and which inflate into separate radial sectors. The probe can be inserted into a vaginal cavity and the balloons inflated provide mechanical support to the USLs. Independent inflation of each balloon allows the mechanical USL support provided to be varied on left and right sides to compensate for differences in the degree of degradation and positioning of the USL ligaments on either side.