A fall detection system includes first sensing devices, second sensing devices, positioning modules, a data server and a display device. The first sensing device is configured to detect a posture of a body part of a user for obtaining body part posture data. The positioning modules are configured to detect positions of the first and second sensing devices, so as to obtain corresponding body part position data. Each of the second sensing devices is disposed on a shoe to detect a posture of a user's feet and to measure a distance from an ambient object for obtaining feet posture data and distance measurement data. The data server is configured to receive the body part posture data, the body part position data, the feet posture data and the distance measurement data to determine if the user falls down.

A smart monitoring system comprising a plurality of sensor devices within a home or facility, at least one of the plurality of sensor devices comprising sensor elements configured to detect activity of an individual in the home or facility, and a computing device configured to receive activity signals from the plurality of sensor devices associated with the individual from the home or facility, parse the activity signals into scoring components by correlating the plurality of sensor devices to the scoring components, the scoring components including a sleep score, a motion score, and an event score, compute a current scoring of the individual based on the scoring components, compare the current scoring of the individual to historic scoring and peer scoring, generate a quality of life score for the individual based on the based on the comparison, and process a clinical analysis of the individual based on the quality of life score, the clinical analysis including graphical representations of the quality of life score and the scoring components.

A computer-implemented method includes obtaining movement data associated with a subject and measured by a stationary motion sensor. The movement data includes a series of values representing a series of movement events of the subject crossing fields of view of the stationary motion sensor. Each movement event in the series of movement events is associated with a respective time stamp. The computer-implemented method further includes extracting a plurality of features from the movement data, determining that the movement data is consistent with symptoms of an illness using a machine-learning model and based upon the plurality of features, and generating an output indicating a result of the determination.

A method is for controlling the operation of a medical apparatus. The method includes determining input data including patient data relating to a patient, operational status data relating to operational status of the medical apparatus, position data describing position of at least one of the wireless, handheld mobile operating device and the patient in relation to the medical apparatus, and workflow data describing at least one of a medical apparatus workflow and a current position in the medical apparatus workflow; determining, using an artificial intelligence analysis algorithm, at least one of a subsequent operating action of an operator of the wireless, handheld mobile operating device and prediction data describing an item of information needed next by the operator; and at least one of selecting and adapting a user interface to be displayed on the touch screen, based upon the prediction data and displaying the user interface on the touch screen.

The present disclosure relates to a monitoring system configured to monitor the activities of individuals without having to keep the surrounding area under the surveillance of a camera thereby maintaining the privacy of the individual. In particular, the monitoring system includes a network of sensitive sensor units that are installed onto indoor flooring to record human footstep induced vibrations. The data collected from the sensors can then be processed to identify individual occupants, determine the number of occupants, estimate the location of footsteps, and track the trajectory of each occupant. The extracted trajectory information can be used to assess an occupant's personal activity and social interaction, which can then be used to analyze the individual's physical and psychological health.

The present disclosure relates to systems and methods for positioning a subject. The method may include generating a first image of the subject disposed on a scanning board of an imaging device. The first image may include position information of the subject. The method may further include generating a second image of the subject which includes information associated with one or more organs of the subjects. Additionally, the method may include determining the position of a ROI based on the first image and the second image. The method may further include operating the imaging device to scan a target portion of the subject.

A method for creating a virtual patient model includes acquiring image data of the patient from a number of directions and in a number of positions of the patient. A virtual patient is created based upon the acquired image data. The virtual patient is adjusted to a posture of the patient during a planned examination with a medical engineering examination apparatus. A patient model creation apparatus is also described. Moreover, a medical engineering examination system is described.

Disclosed herein is a method for analyzing a path traversed by a person within a physical space. The method comprises: receiving, from a plurality of smart floor tiles in the physical space, data associated with the path traversed by the person, wherein the plurality of smart floor tiles comprise a plurality of sensing devices capable of sensing pressure at a plurality of locations in the physical space; identifying, based on the pressure sensed by the plurality of sensing devices of the smart floor tiles, a pattern in the path traversed by the person in the physical space; determining, based on the pattern, the person has a potential medical condition; and in response to determining the potential medical condition for the person, performing a preventative action.

A system, method and tangible computer readable storage medium for receiving, from each of a plurality of sensors disposed in one of a plurality of patient rooms in a hospital ward, data relating to a corresponding patient housed in the one of the plurality of patient rooms, receiving data relating to an output destination, generating a prioritized list including one or more of the plurality of the patients based on the data relating to the plurality of patients and the data relating to the output destination and sending the list to the output destination.

A bed exit detection device (100) according to an embodiment of the present invention is mounted to a privacy rail (11) provided in the vicinity of a bed (10) to be monitored. The device (100) comprises a thermographic sensor (130) and a range sensor (40). The thermographic sensor comprises an array of sensing elements operable to generate an output value matrix from the output signals of each individual sensing element. The device (100) is also provided with a processing unit (150) operable to process the output value matrix of the thermographic sensor (130) and the distance output by the range sensor (140) so as to determine the position of a bed occupant and thereby (10) determine the likelihood of a bed exit event.