A fall detection system includes a wearing apparatus for wearing by a user and a processor connected to the wearing apparatus. The wearing apparatus is set with an inertial sensor for detecting user motion data. The processor is connected with the inertial sensor of the wearing apparatus. When the user's fall state is recognized, further obtaining the motion data of the user at the time of the stand by the inertial sensor and comparing the motion data according to a normal posture condition and/or an abnormal posture condition in a database to determine damage severity of the user.

In the present invention, a system and associated method is provided for monitoring vital parameters of a patient. The monitoring system includes sensors disposed on the patient and operably connected to a monitor. The parameters that are sensed by the sensors are transmitted to the monitor and compared with alarm thresholds and operational criteria stored within the monitor. When an alarm condition is sensed by the system, the system can actively solicit patient assistance in the confirmation of the alarm condition based on a set of reactive inputs stored within the system to enable medical personnel to appropriately respond to clinically relevant sensed alarm condition(s).

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

The present disclosure relates to methods and systems suitable for use in identifying and providing personalised medicine to a patient. In some aspects, systems and method generate a co-therapy regimen for a patient suffering from a disease or condition. An identification of a co-therapy suitable to treat the disease or condition is received. A desired patient endpoint and a patient position are received, wherein the patient position is defined relative to the desired patient endpoint. A dataset relating to the patient is stored. The dataset comprises one or more patient data based on patient-related measurements. The dataset, the patient position and the desired patient endpoint are processed to generate a regimen for the co-therapy. The regimen is stored in a database.

A method of presenting data from a remote sensor that is monitoring a subject includes obtaining a data stream from the remote sensor, wherein the data stream includes a sensor metric, a metric identifier, dynamically updated integrity information about an accuracy of the sensor metric, and a diagnostic assessment of a health condition of the subject, and then displaying, via a display associated with the client device, the diagnostic assessment of the health condition of the subject, a metric statistic associated with the diagnostic assessment, and a recommendation as to an action to be taken by the subject.

A method of assessing or monitoring the normal skin sympathetic nerve activity in a living subject, the subject having a skin, comprises assessing or measuring electrodermal activity, wherein the electrodermal activity is skin conductance, galvanic skin response, electrodermal response, psychogalvanic reflex, skin conductance response, sympathetic skin response or skin conductance level. Skin conductance may be assessed by calculating skin conductance fluctuation peaks per time unit, and when the skin conductance fluctuations peaks are above a certain predefined value, the normal skin sympathetic nerve activity is defined in an analyzing window with a length of about 15 to 60 seconds, the normal skin sympathetic nerve activity is assessed as being obtained or successful. Alternatively, the skin conductance may be assessed by calculating rise time of the mean skin conductance level, the area under the skin conductance fluctuations or the size of the amplitude of the skin conductance fluctuation.

Systems and methods for pacing cardiac conductive tissue are described. An embodiment of a medical system includes an electrostimulation circuit to generate His-bundle pacing (HBP) pulses to stimulate a His bundle, and a cardiac event detector to detect a His-bundle activity within a time window following an atrial activity. The cardiac event detector may use a cross-chamber blanking, or an adjustable His-bundle sensing threshold, to avoid or reduce over-sensing of far-field atrial activity and inappropriate inhibition of HBP therapy. The electrostimulation circuit may deliver HBP in the presence of the His-bundle activity. The system may further recognize the detected His-bundle activity as either a FFPW or a valid inhibitory event, and deliver or withhold HBP therapy based on the recognition of the His-bundle activity.

A medical device is configured to sense an electrical signal and determine that signal to noise criteria are met based on electrical signal segments stored in response to sensed electrophysiological events. The medical device is configured to determine an increased gain signal segment from one of the stored electrical signal segments in response to determining that the signal to noise criteria are met. The medical device determines a noise metric from the increased gain signal segment. The stored electrical signal segment associated with the increased gain signal segment may be classified as a noise segment in response to the noise metric meeting noise detection criteria.

Embodiments provide a deep learning framework to accurately segment liver and spleen using a convolutional neural network with both short and long residual connections to extract their radiomic and deep features from multiparametric MRI. Embodiments will provide an “ensemble” deep learning model to quantify biopsy derived liver fibrosis stage and percentage using the integration of multiparametric MRI radiomic and deep features, MRE data, as well as routinely available clinical data. Embodiments will provide a deep learning model to quantify MRE-derived liver stiffness using multiparametric MRI, radiomic and deep features and routinely-available clinical data.

A quantitative gait training and/or analysis system includes one or more footwear modules that may include a piezoresistive sensor, an inertial sensor and an independent logic unit. The footwear module functions to permit the extraction of gait kinematics and evaluation thereof in real time, or data may be stored for later reduction and analysis. Embodiments relating to calibration-based estimation of kinematic gait parameters are described.