Systems and associated methods are provided for monitoring a user while operating a computing device and providing active feedback to said user regarding health and safety best practices associated with operating said computing device. The methods comprise obtaining user biometric data; converting said biometric data into actionable instances of health and safety user device operation use cases; and interacting with the user based on said actionable instances in order to improve or remedy any deviations from recommended health and safety user device operation practices.

Techniques and technologies for determining and enhancing productivity are described. In at least some embodiments, a system for includes a processing component operatively coupled to a memory; a productivity analyzer at least partially disposed in the memory, the productivity analyzer including one or more instructions that when executed by the processing component perform operations including: receive productivity data associated with usage of one or more productivity tools by at least one user during a time period; receive biometric data associated with one or more biometric aspects of the at least one user during the time period; analyze one or more aspects of the productivity data and the biometric data; and determine based on the analysis at least one productivity-related operation intended to enhance at least one productivity metric of the at least one user.

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 information processing apparatus includes a communication unit, an information acquiring unit, a setting unit, and a controller. The communication unit communicatively connects with a terminal device having a sensor. The information acquiring unit acquires information detected by the sensor from the terminal device communicatively connected via the communication unit. The setting unit sets a preferential terminal device to be preferentially connected with the communication unit. The controller performs control to connect with the preferential terminal device preferentially over a terminal device that is not a preferential terminal device within a capacity range of the communication unit.

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.

A visual tracking system, comprises an eye-tracking device and a cognitive load detection device disposed in electrical communication with the eye-tracking device, the cognitive load detection device comprising a controller having a memory and a processor. The controller is configured to receive eye-movement data from the eye-tracking device, the eye-movement data comprising pupil dilation event data and at least one of saccade event data and fixation event data, apply a classification function to the eye-movement data to detect a cognitive load associated with the eye-movement data and corresponding to a visual location of a field of view of the user, and output a notification regarding the cognitive load associated with the eye-movement data.

Embodiments of the present disclosure provide for an apparatus for opportunistic measurements and processing of a user's context. In one instance, the apparatus may include a processing block, a first sensor having first and second electrodes disposed on a work surface of the apparatus, to provide first readings of a user's physiological context in response to a contact between the electrodes and respective hands of a user, and a second sensor coupled with the processing block and having a sensitive surface embedded in one of the first or second electrode. The second sensor may provide second readings of the user's physiological context and a wake-up signal to the processing block in response to proximity of one of the hands to the sensitive surface. The processing block may facilitate process the user's physiological context in response to a receipt of the wake-up signal. Other embodiments may be described and/or claimed.

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.

Systems and methods are provided for detecting mental fatigue by a user of an Information Handling System (IHS). Upon the user initiating operation of the IHS, activity by the user and physiological parameters of the user are monitored using capabilities of the IHS and, in some cases by wearable devices. Estimates of the user’s mental fatigue during the session are generated based on the monitoring. Embodiments identify when the user takes a break from operating the IHS and also identify when the user resumes use of the IHS. Upon the user resuming the session, the user is prompted for their level of mental fatigue prior to taking the break, thus providing a reliable source of feedback data that is utilized in updating and improving machine learning models that are used to generate the estimates of the user’s mental fatigue based on observed physiological parameters and activity by the user.

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.