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 workplace system comprises a work table, a height-adjustable work chair with a gas spring device, a plurality of sensors, an evaluation unit and a feedback device. At least one of the plurality of sensors is mounted on the worktable. At least one further of the plurality of sensors is mounted on or in the gas spring device and arranged to detect posture data of a user of the work chair. The evaluation unit is configured to receive the posture data and further measurement data from the plurality of sensors, to determine a well-being measure of the user on the basis of a joint evaluation of the received posture data and the measurement data, and to control the feedback device as a function of the well-being measure.

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.

The invention comprises a system that monitors heart activity through embedded ECG sensors in a desk, and desk-related amenities such as a chair, a computer keyboard, a mouse, and a floor mat. The invention also comprises pressure sensors for monitoring a user's presence at the desk. Signals measured from the ECG or pressure sensors are transmitted (in a wired, or wireless fashion) to a computer processing device which applies algorithms to refine the collected signals, and passively estimate the user's ECG.

A teleconferencing system for providing remote assistance comprises a local user apparatus that captures both the field of view of the local user along with physiological state of the local user, and a remote user apparatus to allow the remote user to view what the local user is seeing and doing. The local user and remote user have a shared view and a shared audio link, and the remote user can provided assistance to the local user in performing tasks at the local site. Additionally the physiological state of the local user is monitored, and from the physiological state data, an estimate of the emotional state or physiological state of the local user is provided to the remote user. The remote user can interact or assist the local user, including controlling the amount and/or type of assistance provided to the local user based on their current emotional state.

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 includes obtaining current biological information regarding the user detected by a first sensor, calculating a current arousal level of the user on the basis of the current biological information, obtaining current environment information indicating a current environment around the user detected by a second sensor, predicting a future arousal level, which is an arousal level a certain period of time later, on the basis of the current arousal level and the current environment information, and (i) issuing a notification to the user or (ii) controlling another device, on the basis of the future arousal level.

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.

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 dry eye syndrome alert system through posture and work detection, includes: a data collecting unit configured to detect a posture of a user to collect posture data of the user and preprocess the posture data; an eye blink frequency calculating unit configured to identify a posture change of the user on the basis of the posture data, calculate a motion variability on the basis of the posture change, and estimate an eye blink frequency of the user on the basis of the motion variability; and a diagnosis and alert output unit configured to store data regarding the estimated eye blink frequency, compare the estimated eye blink frequency with a preset reference value, and output an alert to the user when the estimated eye blink frequency is less than or equal to the preset reference value.

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 includes obtaining current biological information regarding the user detected by a first sensor, calculating a current arousal level of the user on the basis of the current biological information, obtaining current environment information indicating a current environment around the user detected by a second sensor, predicting a future arousal level, which is an arousal level a certain period of time later, on the basis of the current arousal level and the current environment information, and (i) issuing a notification to the user or (ii) controlling another device, on the basis of the future arousal level.