32results about How to "Assess health" patented technology

The present invention relates to systems and methods for detecting anomalies in computer network traffic with fewer false positives and without the need for time-consuming and unreliable historical baselines. Upon detection, traffic anomalies can be processed to determine valuable network insights, including health of interfaces, devices and network services, as well as to provide timely alerts in the event of attack.

Provided are embodiments of systems, computer medium and computer-implemented methods for monitoring the health of an employee. A method for monitoring the health of an employee including collecting health data corresponding to at least one of biometric health data and biomechanical health data for an employee acquired via a plurality of health sensors located throughout the employee's workstation, determining a health profile for the employee based at least in part on the collected health data, and serving, to an employee computer for display, health profile content comprising an avatar configured to communicate information relating to the health profile for the employee. The plurality of health sensors including at least one of biometric sensors and biomechanical sensors.

Provided are embodiments of systems, computer medium and computer-implemented methods for sensing health characteristics of a user using a computer mouse including, a temperature sensor, a blood condition sensor and a blood pressure sensor. A method for sensing health characteristics of a user using a computer mouse including receiving, from the temperature sensor, temperature data corresponding to a sensed body temperature of the user, receiving, from the blood condition sensor, blood condition data corresponding to a sensed blood saturation level of the user, receiving, from the blood pressure sensor, blood pressure data corresponding to a sensed blood pressure of the user, and transmitting, to the computer workstation, health data corresponding to the temperature data, the blood condition data, and the blood pressure data for use in determining the body temperature, the blood saturation level, and the blood pressure of the user.

Provided are embodiments of systems, computer medium and computer-implemented methods to monitor the health of an employee. A method for monitoring the health of an employee while the employee is located in an employee workstation including collecting, from a plurality of biometric and biomechanical sensors disposed throughout an employee workstation, health data indicative of biometric and biomechanical characteristics of the employee. The plurality of biometric and biomechanical sensors adapted to sense biometric and biomechanical characteristics of the employee and output corresponding health data indicative of the biometric and biomechanical characteristics of the employee. The method including determining an employee health profile based at least in part on the collected health data, generating a health plan for the employee based at least in part on the health profile, and displaying health content including the employee health profile and the health plan for the employee.

Provided are embodiments of systems, computer medium and computer-implemented methods for sensing health characteristics of a user using a chair pad including a set of health sensors integrated therewith and including temperature sensors, body position sensors, and body fat sensors. A method for sensing health characteristics of a user including receiving, from the temperature sensors, temperature data corresponding to a sensed body temperature of the user, receiving, from the body position sensors, body position data corresponding to a sensed body position of the user, receiving, from the body fat sensors, body fat data corresponding to a sensed body fat of the user, and transmitting, to a computer workstation, health data corresponding to the temperature data, the body position data, and the body fat data for use in determining the body temperature, the body position, and the body fat of the user.

Provided are embodiments of systems, computer medium and computer-implemented methods for monitoring the health of an employee while the employee is located in an employee workstation. A method including collecting, from a plurality of biomechanical sensors disposed throughout the employee workstation, biomechanical health data indicative of biomechanical characteristics of the employee, determining an employee biomechanical health profile based at least in part on the collected biomechanical health data. The employee biomechanical health profile including biomechanical health characteristics, biomechanical health conditions and/or biomechanical health risks for the employee. The method including generating a health plan for the employee based at least in part on the biomechanical health profile, and displaying the employee biomechanical health profile and the health plan to the employee.

Provided are embodiments of systems, computer medium and computer-implemented methods for sensing health characteristics of a user using a floor mat including a set of health sensors integrated therewith. The set of health sensors including temperature sensors, body position sensors, and body fat sensors. A method including receiving, from the temperature sensors, temperature data corresponding to a sensed body temperature of the user, receiving, from the body position sensors, body position data corresponding to a sensed body position of the user, receiving, from the body fat sensors, body fat data corresponding to a sensed body fat of the user, and transmitting, to a computer workstation, health data corresponding to the temperature data, the body position data, and the body fat data for use in determining the body temperature, the body position, and the body fat of the user.

Provided are embodiments of systems, computer medium and computer-implemented methods for sensing health characteristics of a user using a chair pad including a set of health sensors integrated therewith and including temperature sensors, body position sensors, and body fat sensors. A method for sensing health characteristics of a user including receiving, from the temperature sensors, temperature data corresponding to a sensed body temperature of the user, receiving, from the body position sensors, body position data corresponding to a sensed body position of the user, receiving, from the body fat sensors, body fat data corresponding to a sensed body fat of the user, and transmitting, to a computer workstation, health data corresponding to the temperature data, the body position data, and the body fat data for use in determining the body temperature, the body position, and the body fat of the user.

A condition assessment and end-of-life prediction system that includes a virtual condition assessment instrument and a virtual end-of-life prediction instrument. The virtual condition assessment instrument measures the condition of the equipment and includes a data capture subsystem for sampling a set of analog signals and converting them into digital signals, a model-based component to estimate disturbances and predict an expected response, a signal-based component to process output from the model-based component, a classification component to process output from the signal-based component, a fuzzy logic expert component to combine information from the classification component and the model-based component in order to assess the condition of the equipment, and a condition assessment panel to display the condition of the equipment. The a virtual end-of-life prediction instrument predicts the equipment end-of-life and includes a condition prediction end-of-life prediction component to analyze information from the virtual condition assessment instrument to predict condition and end-of life, a prediction condition and end-of-life uncertainty estimation component to estimate the uncertainty of the condition and end-of-life prediction, and an end-of-life panel for displaying the condition and end-of-life prediction and uncertainty.

A metrics management module may be employed to determine organizational health based on data collected for control metrics of an organization. The metrics management module may determine a set of metric health ratings based on the control metric data collected and selects a subset of metric health ratings from the set of metric health ratings. The metrics management module may determine one or more aggregate health ratings based, at least in part, on the subset of selected metric health ratings. The aggregate health ratings may indicate the organizational health of at least a portion of the organization.

Disclosed is a self-test device and method for assessing the functional performance of a reader device that is configured to interface with a wireless sensor. The self-test device may be configured to analyze the accuracy of a signal transmitted from the reader device or the accuracy of a signal received by the reader device. In one embodiment, the reader device may be configured to engage the self-test device to allow the reader to transmit a signal, such as a short pulse of energy or a short burst of radio frequency energy to cause the self-test device to output a resonant signal. The self-test device may receive the transmit signal from the reader and evaluate it against predetermined values. The evaluated signals may be used to assess the accuracy of the transmit signal of the reader device to identify potential calibration issues and initiate remedial action by an automated system or human intervention if needed. The self-test device may be built into a docking station where the reader is placed when not in use.

Systems and methods are provided for evaluating an alarm condition for a monitored patient in a population of one or more patients. One or more physiological parameters pertaining to the patient, or physiological variables, are used to form a time series describing the patient status. A quantile threshold is determined for a patient. A monitor is initialized and begins generating a binary raw alarm signal. The binary signal is filtered with a low pass filter, and the filtered data is subjected to a quantile operation to determine if a particular sample exceeds the determined quantile threshold. If the quantile threshold is exceeded, then a check is performed to see if the raw binary alarm signal also indicates an alarm. If both the preliminary alarm indication and the raw binary signal indicate an alarm, then an alarm is emitted; otherwise monitoring continues.