10 results about "Alertness" patented technology

A device relating to a floater for use by a non-swimmer for buoyancy comprising a floating flatbed with coded descriptions (lateral ends: front end 14, back end 12) and a plurality of paired straps with buckles (20,22) to secure the floater flatbed (10) fixed in the swimmer’s body. This is designed to keep the non-swimmer afloat while concentrating on their hand and leg exercises. The floating flatbed is wrapped either at the upper limbs posterior or anterior portion of the body; a webbing belt with safety mechanisms buckled around the body which can be fixed at the levels; waist, abdominal, and upper back (posterior) or chest part (anterior). An adjustable strap is buckled across shoulders (24, 26) to fasten the device in place below the jaw with a plane surface of a floater which allows use purposely for either supine or prone floating mechanics fundamental in learning the basic skills for any appropriate floating position. Apparently, problems like drowning and other water accidents happen in just any part of the globe usually caused by panic and water anxiety. The device contains characteristics easy for use by non-swimmers at any age level for water safety and training device for non-swimmers particularly those who manifest water anxiety. Acquired skill in floating somehow creates mental alertness as a greater opportunity for survival during atrocities.

This invention discloses a method for assessing sleep quality based on multimodal features in a waking state, comprising: S1, guiding subjects to complete a sleep quality scale, collecting pulse wave signals, facial thermal images, and corresponding temperature matrices of subjects in a resting state, and conducting a psychological alertness task test; S2, extracting heart rate, heart rate variability, and pulse wave morphology features from the pulse wave signals; S3, identifying key points on the face, determining regions of interest, and extracting facial temperature features; S4, analyzing the acquired psychological alertness task test data, statistically analyzing and extracting reaction time and attention maintenance-related indicators; S5, constructing a training dataset; S6, using the training dataset to train a classifier and construct a sleep quality classification model; S7, inputting the multimodal features collected from new users in a waking state into the sleep quality classification model and outputting the assessment results. This invention provides a method for assessing sleep quality in a daytime waking state.

Methods, systems, and devices are described for providing physiological state related insights associated with a user. One method includes receiving physiological data associated with a user from at least one wearable device, the physiological baseline providing reference user alertness data. The method also includes obtaining a physiological baseline associated with the user based at least in part on the baseline physiological data associated with the user, the physiological baseline providing reference user alertness data. The method also includes receiving additional physiological data associated with the user from the at least one wearable device. The method also includes obtaining current user alertness data based at least in part on the additional physiological data associated with the user. The method also includes identifying a trigger condition for providing a physiological state related insight associated with the user based at least in part on a comparison between the current user alertness data and the reference user alertness data, the physiological state related insight related to a travel event involving at least one vehicle. The method also includes causing a user device to provide the physiological state related insight associated with the user.

For each of a plurality of time intervals: a power spectrum is generated using a corresponding portion of EEG data; a normalized power spectrum is generated; and a preferred frequency (a frequency with a largest z-score or a highest normalized power in the normalized power spectrum corresponding to the time interval) or dominant frequency for the time interval is determined. A set of time windows is defined, each including multiple time intervals of the plurality of time intervals. For each of a set of time windows, a number of the time intervals is determined for which the preferred or dominant frequency is within a Beta band (12-30 Hz frequencies) or a portion thereof. For a time window, it is determined that an alert condition is satisfied using the number of the plurality of time intervals where the preferred or dominant frequency is within the Beta band or a portion thereof.

This application relates to the field of computer technology and discloses a method, device, and physiological modeling system for alertness calibration. The method includes: acquiring the reaction time of each subject in each task block for psychomotor alertness tasks; each task block consists of a psychomotor alertness task and an n-back task, or is composed solely of psychomotor alertness tasks; calculating the mean and standard deviation of each subject's task reaction time based on the reaction time of each task; calculating a personalized alertness grading threshold for each subject based on the mean and standard deviation; and generating an alertness level label for each subject corresponding to each task block based on the reaction time of each task using the personalized alertness grading threshold. This application can obtain objective, accurate, and realistic alertness label calibration results that conform to the actual changes in human alertness states.

A dialog device includes an alertness level calculation unit that calculates an alertness level of an occupant of a vehicle based on state information of the occupant, a dialog output unit that, in a case where the calculated alertness level is less than a predetermined value, outputs a dialog to the occupant, an interest degree calculation unit that calculates a degree of interest of the occupant in the dialog based on the state information after the dialog is output, and a dialog generation unit that determines a topic of the dialog based on the calculated degree of interest.

A system for testing and training a brain capability of planning and executing motion activity for a user who has sustained a functional movement deficit; said system comprising an electroencephalographic sensor arrangement attachable to the head of said user; a processor configured for receiving and analyzing electroencephalographic signals obtained from said user during visualization of an action; a memory storing instructions when executed by said processor for instructing said user to visualize executing said motion action; measuring electroencephalographic signals on said electroencephalographic sensor arrangement; calculating at least one characteristic selected from the following: a concentration index; a motor control index; an alertness index; providing said user with a feedback pattern based on at least one said concentration, motor control, alertness and motion readiness; recurring steps to be if needed.

For each of a plurality of time intervals: a power spectrum is generated using a corresponding portion of EEG data; a normalized power spectrum is generated; and a preferred frequency (a frequency with a largest z-score or a highest normalized power in the normalized power spectrum corresponding to the time interval) or dominant frequency for the time interval is determined. A set of time windows is defined, each including multiple time intervals of the plurality of time intervals. For each of a set of time windows, a number of the time intervals is determined for which the preferred or dominant frequency is within a Beta band (12-30 Hz frequencies) or a portion thereof. For a time window, it is determined that an alert condition is satisfied using the number of the plurality of time intervals where the preferred or dominant frequency is within the Beta band or a portion thereof.

This invention discloses an alertness detection method and system based on uncertainty measurement and cross-modal knowledge distillation, belonging to the fields of physiological signal processing and deep learning. Specifically, the method first preprocesses and extracts features from raw EEG and EOG signals to obtain structured EEG and EOG feature matrices, and generates alertness state labels based on the subject's behavioral reaction time index. Then, a teacher model is constructed and trained using EEG-EOG bimodal data as input; simultaneously, a student model is constructed using EOG unimodal data as input. Finally, using the trained teacher model as the knowledge source, the student model undergoes uncertainty-guided cross-modal knowledge distillation training. This method can effectively improve the alertness detection performance of the EOG unimodal student model under limited sample conditions.

A computer-implemented method for estimating awake-time alertness of a user includes: performing a recursive alertness prediction procedure by performing operations including: obtaining measurement data measured, from a user, by at least one sensor device; determining at least a sleep time, a sleep quality, and a circadian phase of the user; selecting, in response to a preceding awake-time alertness pattern enabled for usage in the recursive alertness prediction procedure, the preceding awake-time alertness pattern as an alertness pattern template; selecting, otherwise, a default alertness model as the alertness pattern template; determining an awake-time alertness pattern; and determining whether to enable or disable the awake-time alertness pattern determined for a subsequent recursive alertness prediction procedure; and causing displaying, to the user via a user interface, the awake-time alertness pattern determined.