445 results about "Health indicator" patented technology

A system that facilitates device and/or machinery diagnostics, prognostics and control by way of condition sensing, such as sensing the condition of the device and/or a fluid of the device (e.g., fluid health indicators). The system can employ a plurality of sensors to determine a current state and estimate a future state of the fluid and/or device, as well as providing control of the device, e.g., in order to increase the remaining useful life of the fluid and/or operation of the device. The sensors can communicate wirelessly with each other, with the device, and/or with a central control system that provides, e.g., sensor fusion, prognostics and control integration. In addition, the sensors can be powered locally based upon the physical or chemical properties of the environment.

A patient monitoring system can display one or more configurable health monitors on a configurable user interface. The health indicators are configured to display a physiological signal from a patient. The patient monitoring system can calculate ranges of values for the health indicator that correspond to a status of the patient. The health indicators can display different outputs based on the value of the physiological signal.

A real-time body status monitoring system (RBMS) is presented in this invention. A wearable monitoring apparatus (WMA) worn by users consists of one or a few sensor nodes and a computing module. Sensor nodes communicate with the computing module via either wired or wireless protocols. RBMS incorporates a monitoring center that connects and serves many WMAs. Together with the sensors and context-aware information fusion and analysis, the system in the invention goes beyond sampling rare events that may be of profound diagnostic, prognostic, or therapeutic importance. It measures the physiological responses to therapeutic interventions during daily activities, which constitute direct and practical health indicators for the patient.

Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

A system and method for determining service requirements of elements in a network. A collection program is used to gather performance data from an element for which a service requirement is desirable by an entity. The performance data is stored in a database. The performance data is then retrieved by a microprocessor. The microprocessor processes the performance data to calculate a network health indicator associated with the performance data. Using the network health indicator, a service requirement of the associated element can be determined. When at least two network health indicators or service requirements of different elements are compared, service priorities among the elements can be established.

A computerized monitoring system and corresponding method of monitoring the status and health of a blowout preventer. The system includes a graphics display at which a graphical user interface (GUI) displays the health of various sealing elements and control systems by way of “traffic light” indicators. The health indicators are evaluated, by the monitoring system, based on a risk profile for each of the indicated elements and control systems. The risk profiles are evaluated based on inputs such as measurement inputs, feedback signals, mechanical positions, diagnostic results, drilling conditions, and other status information of the blowout preventer at a given time and based on levels of redundancy and levels of deviation from normal conditions. The GUI includes recent history of changes in operating condition, and alarm indications such as poor health, along with the times of those events.

An apparatus and method for measuring the key elements of human primary health is disclosed. The apparatus is in the form of a medical diagnostics unit capable of measuring Electrocardiogram (ECG), height, weight, body mass index (BMI), body temperature, hearing efficiency, lung function, pulse, blood oxygen levels, blood pressure, urology and vision testing. The medical diagnostics unit includes an enclosure with a data card and/or fingerprint entry. The enclosure includes medical measuring devices which allow a patient to follow instructions on a touch screen visual display unit to conduct the desired tests and obtain the patient's health information. This information is stored locally as well as being transmitted to a doctor for review and evaluation. The automated medical diagnostics unit reduces the staffing requirements to obtain a patient's basic health information.

A health monitoring and management device, system, and/or method can include a sensor adapted to detect changes in one or more health indicators and transmit data related to the health indicators. The system can further include an interventional element adapted to receive a health intervention command and provide a health intervention related to the health indicators. The system can further include a microprocessor adapted to receive and analyze the health indicator data transmitted by the sensor, formulate the health intervention command related to the health indicator data according to pre-determined parameters, and transmit the health intervention command to the interventional element. The health intervention command can be transmitted to the interventional element within a clinically relevant time period.

Methods and apparatus are provided for analyzing a complex system that includes a number of subsystems. Each subsystem comprises at least one sensor designed to generate sensor data. Sensor data from at least one of the sensors is processed to generate binary evidence of a sensed event, and complex evidence of a sensed event. The complex evidence has more sophisticated mathematical properties than the binary evidence. The complex evidence comprises one or more of: a condition indicator (CI), a health indicator (HI), and a prognostic indicator (PI). A system fault model (SFM) is provided that defines statistical relationships between binary evidence, complex evidence, and an underlying failure mode (FM) that is occurring in the complex system. The binary evidence and the complex evidence are processed to identify failure modes taking place within one or more of the subsystems. Based on the binary evidence and the complex evidence and the SFM, diagnostic conclusions can be generated regarding adverse events that are taking place within the complex system, and prognostic conclusions can be generated regarding adverse events that are predicted to take place within the complex system.

Health metrics can be received from at least one person wearing a health monitoring device in an unencumbered fashion that permits free motion. The health monitoring device can wirelessly convey the heath metrics to a remotely located computing device. The received health metrics can be compared against at least one configurable, yet previously established threshold. A change in a situation proximate to the person can be inferred based upon comparison results. A programmatic event can be fired based upon the inferred change. At least one programmatic action can be automatically initiated responsive to the firing of the programmatic event, wherein the programmatic action initiates a response to the inferred change.

Disclosed are techniques used in connection with determining a health indicator (HI) of a component, such as that of an aircraft component. The HI is determined using condition indicators (CIs) which parameterize characteristics about a component minimizing possibility of a false alarm. Different algorithms are disclosed which may be used in determining one or more CIs. The HI may be determined using a normalized CI value. Techniques are also described in connection with selecting particular CIs that provide for maximizing separation between HI classifications. Given a particular HI at a point in time for a component, techniques are described for predicting a future state or health of the component using the Kalmán filter. Techniques are described for estimating data values as an alternative to performing data acquisitions, as may be used when there is no pre-existing data.

According to the present invention, physical health indicators computed for a particular user are received in a common transmittable data format at a portable computer system associated with the particular user, wherein each of the physical health indicators is computed by an electronic health measurement device from among multiple diverse electronic health measurement devices monitoring the physical health of the particular user. Each of the physical health indicators is analyzed at the portable computer system in view of determined acceptable health levels for the particular user. Output of an indicator of acceptability of the physical health indicators for the particular user is controlled from the portable computer system, in response to the analysis of each of the physical health indicators, such that a single portable computer system monitors the physical health of an individual from multiple physical health indicators received from multiple diverse electronic health measurement devices.

The invention discloses a system, method and computer program for generating a Global Personal Health Record Timeline (GPHRT) is provided. A plurality of data items and information points associated with at least one relationship between users associated with a single patient, healthcare provider, a group of healthcare providers. Ordered data items are stored in a personal health record, transmitted and shared via a plurality of storage and transmittal technologies such as, (BLE), barcode scanner, and a plurality of other storage and transmittal technologies that will interface with the Global Personal Health Record Timeline platform. These aforementioned embodiments would further be integrated into an Enterprise Resource Planning Electronic Medical Records Software Environment (ERP/EMRSE) which would interface with a Database Repository with a web-based Global Patient Health Record Timeline interface portal, security interface, Customer Resource Management platform, Practice Management platform, e-commerce interface, “HIPPA” compliant security filter.

The application of the invention discloses an intelligent air conditioning system. The intelligent air conditioning system comprises a human body health state monitoring device, an indoor environmental monitoring device, a health controlling device and an air conditioner controller; the input end of the air conditioner controller is connected with the human body health state monitoring device and the indoor environmental monitoring device, and the output end of the air conditioner controller is connected with the health controller; the air conditioner controller is used for acquiring human body health indicator information and indoor environmental information in real time, finding information data which cannot meet requirements by judging and analyzing all information data, and controlling actuators corresponding to the information data which cannot meet the requirements to perform corresponding adjusting operations. As can be seen, the intelligent air conditioning system can adjust a condition by combining indoor environment and a human body health state; compared with the prior art that only indoor environment is taken into consideration during adjusting, the intelligence level of an intelligent air conditioner is improved greatly, so that the real intelligent health experience is brought to a user.

The invention discloses an aero-engine remaining life prediction method based on multi-stage information fusion. The method comprises the steps that multi-source monitoring parameter denoising processing and feature extraction are conducted; stable analysis is conducted on multi-source monitoring time sequences, sudden change points of all parameter monitoring time sequences are calculated and parameter degeneration proportions at the positions of the sudden change points are calculated; multi-stage division is conducted on multi-source parameters, a regression fusion model is established, sample training is conducted by using historical monitoring data and parameters, in multiple stages, of the fusion model are obtained; according to monitoring data in a training set, the multi-source monitoring parameters are fused and a health indicator HI is obtained; by using the Kalman filtering algorithm, best fit is conducted on an engine in the whole process that the performance fails from being complete, and the error of a prediction model is minimized; according to real-time monitoring data in a test set, the multi-source monitoring parameters are fused and a health indicator HI is obtained; time-varying parameters of the prediction model are estimated in real time by using the Kalman filtering algorithm; the prediction model is determined, the time mechanism is introduced and the failure time of the engine is estimated in real time.

A sensor for detecting changes in spinal tissue is disclosed. The implantable sensor or surgical instrument includes a sensing element adapted for detecting indicators of tissue health. The sensor is configured for transmitting the tissue data outside of the body. Further a system and method are provided to utilize the sensed tissue properties to treat the patient's condition.

Disclosed are techniques used in connection with determining a health indicator (HI) of a component, such as that of an aircraft component. The HI is determined using condition indicators (CIs) which parameterize characteristics about a component minimizing possibility of a false alarm. Different algorithms are disclosed which may be used in determining one or more CIs. The HI may be determined using a normalized CI value. Techniques are also described in connection with selecting particular CIs that provide for maximizing separation between HI classifications. Given a particular HI at a point in time for a component, techniques are described for predicting a future state or health of the component using the Kalman filter. Techniques are described for estimating data values as an alternative to performing data acquisitions, as may be used when there is no pre-existing data.

An operations support system for an engine includes a diagnostics unit configured to receive engine data from the engine and to generate condition indicators based on the engine data using a thermodynamic model. The thermodynamic model is based on component maps associated with the engine. The system further includes a fault classification unit coupled to the diagnostic unit and configured to generate health indicators based on the condition indicators.

The invention relates to a stirring type cereal sour milk and a method of preparing the same. The cereal sour milk is made through lactobacillus and probiotics ferment by using raw materials with the following weight components: raw fresh milk of 700-875 parts, cereal powder 1-30 parts; additive agent for dairy food of 5-44 parts; lactobacillus strain/functionality probiotics strain of 0.2-0.8 parts; corrective of 48-82 parts; stabilizing thickening agent of 3-10 parts; homogeneous pourable dense quasi-fluid which is the external appearance showing the color of added cereal powder with suspended corresponding cereal powder; the tissue is fine, smooth and uniform and a few whey is allowed to be precipitated; organoleptic attribute, nutritive index, health indicator and content of lactobacillus series of the cereal sour milk are all better than that in the GB2746 cultured milk quality index. The present invention is characterized in that the cereal powder that is formed by pulverizing and swelling processes reserves the nutrient of cereal to the utmost extent and improves the hot water leaching rate and absorption rate of its nutrient components; the technique is more compact and the operation is easy; the production efficiency is high and the consumption human populations of the products are widened; the cereal sour milk is easy to digested and absorbed, and the taste is better.

Embodiments of the present invention relate to a system for monitoring the characteristics of key components of aeronautical machines (e.g., airplanes, helicopters, etc.), processing obtained data, and delivering prognostic health indicators to improve machine performance and detect early warning signs of failure. In one embodiment, a method of maintaining prognostic health management accuracy of an aeronautic system comprises providing a control module and a sensor pod having a plurality of sensors, and physically positioning the plurality of sensors on a mechanical component of an aircraft; obtaining operational data from the sensors while the aircraft is operating in a native environment; transmitting operational data to the control module and determine real-time system performance characteristics; processing real-time system performance characteristics against a set of historical records containing past system performance characteristics and generating predictive indicators for forecasting remaining component lifetime and future component failures; and providing predictive indicators on an indicator means.

The invention discloses an aeroengine remaining service life prediction method based on an LSTM network and an ARIMA model. The method comprises the following steps: n engine health index (LSTM-HI) models based on an LSTM depth neural network are established according to engine historical degradation data; According to the engine sensor data, the ARIMA model is trained and the engine sensor parameters are predicted backward in many steps. By predicting the sensor parameters, according to LSTM-HI index is used to evaluate whether the engine is degraded to failure, and the remaining service lifeand its probability distribution are obtained. The invention provides a novel method for predicting the remaining service life of an aeroengine, which has high accuracy and feasibility, and plays anactive role in promoting the real-time health management of the aeroengine and reducing the maintenance cost.

A method and apparatus for determining the operating health of a hydraulic system are provided. The method may include the steps of determining a plurality of operating parameters of the hydraulic system during operation of the hydraulic system, determining an estimated working condition value of the hydraulic system, modifying the estimated working condition value as a function of the operating parameters, and determining the operating health of the hydraulic system as a function of the working condition value. In one method, the working condition value may be indicative of an effective bulk modulus value of an operating fluid within at least part of the hydraulic system.

A method for designating vegetation as potential impact vegetation includes identifying data relating to target vegetation from a dataset and determining a reach distance of the target vegetation. The reach distance includes a distance from a point on a ground to a point of the target vegetation that is farthest from the point on the ground. The method also includes comparing the reach distance of the target vegetation with a closest distance between the point on the ground and a structure to produce a compared reach distance value and determining a health indicator value for the target vegetation. The method further includes comparing the health indicator value of the target vegetation to a health indicator threshold value and designating the target vegetation as having a potential to impact the structure based on the compared reach distance value and the comparison of the health indicator to the health indicator threshold.