A computer-implemented method includes: receiving, by a computing device, information identifying a user's activity; determining, by the computing device, the user's tasks based on the information identifying the user's activity; determining, by the computing device, the user's context switches based on the user's tasks; receiving, by the computing device, biometrics data associated with the user via an application programming interface (API); determining, by the computing device, the user's stress levels at various times based on the biometrics data; storing, by the computing device, information linking the user's stress level with the user's context switches; and outputting, by the computing device, the information linking the user's stress level with the user's context switches.

A computer-implemented method includes: receiving, by a computing device, information identifying a user's activity; determining, by the computing device, the user's tasks based on the information identifying the user's activity; determining, by the computing device, the user's context switches based on the user's tasks; receiving, by the computing device, biometrics data associated with the user via an application programming interface (API); determining, by the computing device, the user's stress levels at various times based on the biometrics data; storing, by the computing device, information linking the user's stress level with the user's context switches; and outputting, by the computing device, the information linking the user's stress level with the user's context switches.

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.

Embodiments are directed towards embodiments for monitoring or predicting user health with respect their use of connected computers, such as, mobile phones, tablets, or the like. Activities associated with a user and a computing device may be monitored to determine activity events. One or more sub-scores may be provided in real time based on metrics provided as input to sub-score models such that the metrics are associated with the activity events. A health score associated with a probability of an occurrence of adverse user outcomes may be provided based on a health model that uses the sub-scores. An analysis engine may compare the health score to other health scores to predict in real time the adverse outcomes. The analysis engine may recommend one or more actions to decrease the probability of the occurrence of the adverse outcomes based on the result.

A method may include receiving implicitly collected computer interaction data of a user from a computing device; accessing a data store of previously collected computer interaction data, the previously collected computer interaction data correlated with sleep patterns of users; comparing the users implicitly collected computer interaction data to the previously collected computer interaction data; and inferring the user's sleep pattern based on the comparing. The method may provide an indication of real-world cognitive performance that varies throughout the day, and which is influenced by both circadian rhythms, chronotype (morning/evening preference), and prior sleep duration and timing.

A method (100) for monitoring data entered into an electronic medical records (EMR) system (270), comprising: (i) estimating (104) an amount of information to be entered into the EMR system during a first monitoring period; (ii) determining (106) an amount of information entered into the EMR system during the first monitoring period; (iii) comparing (108) the determined amount of information entered into the EMR system during the first monitoring period to the estimated amount of information to be entered into the EMR system; (iv) estimating (110) a likelihood of error for information entered into the EMR system; and (v) assigning (112), based on the estimated likelihood of error, a reliability measure to the information entered into the EMR system or to information missing from the EMR system.

An embodiment of the invention includes a system that tracks a user's pupillary response to content located on a web page. The system then determines a cognitive load for the user that is based on the measured response. Cognitive load refers to the total amount of mental activity imposed on working memory in any one instant. Further, the system may aggregate the cognitive load data for one user over time, for many different users, and/or for many different users over time. The cognitive load may be determined for different portions of a displayed page, such as a document object model (DOM) included on the page. The cognitive load may be specified for different elements that make up the DOM. Also, cognitive load may be apportioned over several different DOM elements at one moment in time or over a period of time. Other embodiments are described herein.

A cough detection apparatus includes a receiving unit and a cough detection unit. The receiving unit receives motion information and sound information of a person who sits on a seat. The motion information and the sound information are transmitted from a motion detector and a sound detector. The motion detector and the sound detector are provided for the seat. The cough detection unit detects a cough of the person on the basis of the received motion information and the received sound information.

Methods and system for monitoring and assessing employee moods are disclosed. A proposed enterprise employee monitoring system includes surveillance cameras, a facial recognition module, an emotional analyzer module, and an employee database. The surveillance cameras capture image data including employee individuals within the enterprise. The facial recognition module identifies the individuals in the image data, and the emotional analyzer module determines an emotional state of the individuals based upon the image data. The employee database stores employee information and the emotional state information from the emotional analyzer module, based upon the identification performed by the facial recognition module.

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.