An integrated and holistic system that delivers clinical decision support, disorder prevention, and research services for chronic disorders is provided. In one embodiment, the system collects a variety of data about an individual including data from one or more of wearable motion sensors, self-reported questionnaires, medical imaging, and electronic medical records. A historical database of outcomes and similar data for other individuals is processed using advanced statistics, artificial intelligence, and machine learning to identify biomarkers and phenotypes that are indicative of outcomes with respect to zero or more interventions. The collected individual's data is then analyzed with respect to the identified biomarkers or phenotypes to predict outcomes with respect to zero or more interventions for the individual. The individual, and/or an associated agent, may then consider the predicted outcomes when selecting an intervention plan for the individual and monitor intervention impact over time.

In an aspect of the invention, a method of identifying sensory inputs affecting working memory load of an individual is provided. The method comprises monitoring (S101) working memory load of the individual using a sensor device, detecting (S102) an increase in the working memory load of the individual, and identifying (S103), in response to the detected increase, at least one sensory input affecting the working memory load of the individual.

An arousal level prediction apparatus and method are disclosed. The arousal level prediction apparatus obtains first biological information indicating current biological information of the user, obtains first environment information indicating a current environment around the user, and obtains living information of the user indicating an activity history of the user. The arousal level predication apparatus includes a process that calculates a first arousal level indicating a current arousal level of the user based on the first biological information, predicts a second arousal level, which is an arousal level of the user at a certain period of time later, based on the first arousal level, the first environment information and the living information, and outputs the second arousal level.

An arousal level prediction apparatus and method are disclosed. The arousal level prediction apparatus obtains first biological information indicating current biological information of the user, obtains first environment information indicating a current environment around the user, and obtains living information of the user indicating an activity history of the user. The arousal level predication apparatus includes a process that calculates a first arousal level indicating a current arousal level of the user based on the first biological information, predicts a second arousal level, which is an arousal level of the user at a certain period of time later, based on the first arousal level, the first environment information and the living information, and outputs the second arousal level.

Embodiments are directed towards identifying and decreasing operational pain and increasing system efficiency through health monitoring and management. This may be accomplished through measuring, monitoring, reducing meaningful incident behavior across an organization and using it to inform necessary changes in the organizational operations to improve efficiency, or the like. Ergonomic data or metrics collected by a resource management engine may be used to intelligently inform management decisions to increase human well-being across the organization's workforce to optimize overall system performance. Accordingly, resource management engines may identify areas in organizations that need improvement or repair. In some embodiments, resource management engines may be arranged to participate in a continuous feedback loop that may provide continuous overall system optimization.

A method for predicting an arousal level used by a computer of an arousal level prediction apparatus that predicts an arousal level of a user is provided. The method includes obtaining current biological information regarding the user detected by a sensor, and calculating a current arousal level of the user based on the current biological information. The method further includes obtaining current environment information indicating a current environment around the user, and predicting a future arousal level, which is an arousal level a certain period of time later, based on the current arousal level and the current environment information. Based on the predicted future arousal level, the method further issues a notification to the user, or controls an operation of a device.

Embodiments for intelligent monitoring of a health state of a user by a processor. A health state of a user may be learned while engaged in one or more activities associated with a computing device. One or more mitigating actions may be identified and recommended to implement by the user to minimize one or more possible negative impacts upon the health state of the user while engaged in the one or more activities associated with the computing device.

Implementations of this disclosure are directed towards the computer-implemented determination of interrupt events from a plurality of notification events generated by one or more of a plurality of computer systems. Interrupt events are associated with notifications designed to immediately alert a responder. A health score is generated as a weighted sum of one or more health sub-scores that are generated using one or more health sub-score models that take as input more health metrics representative of data associated with one or more interrupt events. The health sub-scores are representative of an impact relating to the management of operations of the plurality of computer systems and the weights used in the weighted score are representative of an estimation of the relative impact of the generated health sub-scores. The health sub-score models may be automatically modified according to an automated analysis of historic operational outcome metrics and historic scores.

A method for predicting an arousal level used by a computer of an arousal level prediction apparatus that predicts an arousal level of a user is provided. The method includes obtaining current biological information regarding the user detected by a sensor, and calculating a current arousal level of the user based on the current biological information. The method further includes obtaining current environment information indicating a current environment around the user, and predicting a future arousal level, which is an arousal level a certain period of time later, based on the current arousal level and the current environment information. Based on the predicted future arousal level, the method further issues a notification to the user, or controls an operation of a device.

A customizable work station is described with a computing device that has a local processor, a local display, a connector communicably coupled to the local processor, and a plurality of sensors that collect proximal information at the station to provide at least one personalized enhancement to a user of the station.