5 results about "Mental load" patented technology

The invention discloses a cognitive impairment risk intelligent matching intervention system based on multi-modal data, and belongs to the technical field of cognitive impairment, and the cognitive impairment risk intelligent matching intervention system specifically comprises the following steps: collecting a tactile interaction sequence and a face video data stream when a user executes a cognitive intervention task, and constructing an original multi-modal data set; extracting tactile operation track features and reconstructing a heart rate variability sequence, and generating a synchronous multi-mode feature set; identifying a difference interval of state fluctuation and extracting behavior and physiological features in the interval to form an instant physical and mental load parameter set; in combination with an interaction efficiency index in the task execution record, generating a joint state feature vector, and outputting a personalized adaptation instruction through a pre-trained sub-state-parameter association model; and constructing an adaptive strategy model by using the multi-modal features and an adaptive instruction to realize millisecond-level dynamic adjustment from the real-time multi-modal features to task parameters. According to the method, real-time perception and accurate matching of the cognitive emotion load of the user are realized, and the individuation degree and instantaneity of intervention are improved.

The invention belongs to the technical field of pilot mental load assessment design, and particularly relates to a pilot mental load assessment model construction method based on multi-physiological parameter fusion, which comprises the following steps: step 1, collecting physiological parameters of a pilot under different mental load levels, and establishing a corresponding relationship between the physiological parameters of the pilot and the mental load levels; the mental workload levels comprise high, medium and low levels; the physiological parameters comprise eye movement, electrocardio and electroencephalogram characteristics; step 2, based on the established corresponding relation between the physiological parameters of the pilot and the mental load level, using a Transform model to construct a pilot mental load assessment model, using a convolutional neural network (CNN) method to optimize the pilot mental load assessment model, obtaining a Transform + CNN pilot mental load assessment model, and establishing a pilot mental load assessment model; and the Transform + CNN pilot mental load evaluation model is further optimized through a feature level fusion optimization method, and a feature level fusion Transform + CNN pilot mental load evaluation model is obtained.

The embodiment of the invention provides a mental load threshold determination method and system based on manager risk preference, and relates to the technical field of human factor engineering, system design and human resource management, and the method comprises the steps: collecting not less than 30 task samples in the same task scene in a target field, obtaining mental load intensity data and a corresponding task error rate, wherein mental load intensity is calculated based on a multi-resource theoretical model; establishing a monotonically increasing mental load intensity-error rate linear regression model in a set interval by adopting a least square method, and verifying the significance of the model through F test or t test; the acceptable maximum error rate input by the manager is substituted into the mental load intensity-error rate linear regression model, and an analytical method is used for inverse solution to obtain a mental load threshold value; and outputting a mental load threshold value for personnel post compilation, task process design or man-machine function distribution. And a scientific and quantitative decision basis is provided for man-machine function distribution, post compilation and process optimization in a system design stage.

The invention relates to the technical field of man-machine interaction, and discloses a multi-modal physiological feature-based cross-individual mental load personalized recognition system and method, and the system comprises a data processing module, a first model construction module, a second model construction module and a model evaluation module. The data processing module is used for acquiring a multi-modal physiological signal and subjective mental load of an operator in the process of executing long-time continuous complex multiple tasks, and extracting a multi-modal data set with consistent time sequence; the first model construction module and the second model construction module construct a cross-individual mental load recognition model for the physiological features output by the feature extraction module based on domain adversarial mechanism alignment inter-domain feature distribution and dual-classifier adversarial mechanism alignment inter-category feature distribution, and the model evaluation module recognizes a result according to the mental load. According to the method, individual difference adaptation and category semantic unification are realized, the cross-individual and cross-scene generalization ability is improved, and targeted identification of individuals is realized, so that the identification accuracy is improved.

One or more example embodiments of the present invention describes a method for monitoring a person in an environment, including integrating the person into the environment, wherein the person has a task to solve in the environment, measuring with a sensor system a number of physical conditions of the person while the person interacts with the environment, the sensor system providing a sensor-dataset including measured physical values about the person from which mental conditions of the person can be inferred, generating assessment-data from the sensor-dataset, the assessment-data reflecting at least one of an ability of the person to accomplish the task or the mental load of the person while solving the task, repeating the measuring and the generating a plurality of times and outputting the generated assessment-data or data based on the generated assessment-data.