Abstract: Disclosed embodiments describe techniques for physiological analysis. The physiological analysis is based on the use of a wearable sensor array. A plurality of sensors and conductors is coupled to a compression garment, where the compression garment has stretchable portions that stretch, e.g., in a single dimension. The garment can include a shirt, a sports bra, or a vest. Associated on-board electronics are mounted to at least one of the compression garment and the hub and electrically connected to the hub and generate a physiological profile, based on at least one output from each of the sensors. At least one aspect of the physiological profile is communicated to a processor configured to analyze the at least one aspect and generate an assessment based on the analysis.

Abstract: A method of computing a heart rate value compensates for noise derived from the subject, the sensor, the transmission, and/or other variables that can lead to false R-peak detection or missed R-peak detection. The method computes an updated heart rate based on a window of approximately ten seconds of digitized ECG readings broadcast from a sensor. A new value is calculated approximately every second such that the window of ECG readings overlaps considerably between consecutive calculations. The method compensates for noisy data by discarding heart rate samples that differ by more than a threshold amount from a previously calculated heart rate value. The threshold is adjusted based on a standard deviation of differences between heart rate samples. Prior to calculating a heart rate value, a forward-looking, pre-filter logic may be applied in situations where the raw data has an extremely low signal-to-noise ratio.

Abstract: A method of computing a heart rate value compensates for noise derived from the subject, the sensor, the transmission, and/or other variables that can lead to false R-peak detection or missed R-peak detection. The method computes an updated heart rate based on a window of approximately ten seconds of digitized ECG readings broadcast from a sensor. A new value is calculated approximately every second such that the window of ECG readings overlaps considerably between consecutive calculations. The method compensates for noisy data by discarding heart rate samples that differ by more than a threshold amount from a previously calculated heart rate value. The threshold is adjusted based on a standard deviation of differences between heart rate samples. Prior to calculating a heart rate value, a forward-looking, pre-filter logic may be applied in situations where the raw data has an extremely low signal-to-noise ratio.

Abstract: Video of one or more vehicle occupants is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in cognitive state analysis. The heart rate information and resulting cognitive state analysis are correlated to stimuli, such as digital media, which is consumed or with which a vehicle occupant interacts. The heart rate information is used to infer cognitive states. The inferred cognitive states are used to output a mood measurement. The cognitive states are used to modify the behavior of a vehicle. The vehicle is an autonomous or semi-autonomous vehicle. Training is employed in the analysis. Machine learning is engaged to facilitate the training. Near-infrared image processing is used to obtain the video. The analysis is augmented by audio information obtained from the vehicle occupant.

Abstract: A method of computing a heart rate value compensates for noise derived from the subject, the sensor, the transmission, and/or other variables that can lead to false R-peak detection or missed R-peak detection. The method computes an updated heart rate based on a window of approximately ten seconds of digitized ECG readings broadcast from a sensor. A new value is calculated approximately every second such that the window of ECG readings overlaps considerably between consecutive calculations. The method compensates for noisy data by discarding heart rate samples that differ by more than a threshold amount from a previously calculated heart rate value. The threshold is adjusted based on a standard deviation of differences between heart rate samples. Prior to calculating a heart rate value, a forward-looking, pre-filter logic may be applied in situations where the raw data has an extremely low signal-to-noise ratio.

Abstract: A method of computing a hear rate compensates for noise in data transmission that can lead to false R-peak detection or missed R-peak detection. The method computes an updated heart rate based on a window of approximately ten seconds of digitized ECG readings broadcast from a sensor. A new value is calculated approximately every second such that the window of ECG readings overlaps considerably between consecutive calculations. The method compensates for noisy data by discarding heart rate samples that differ by more than a threshold amount from a previously calculated heart rate value. The threshold is adjusted based on a standard deviation of differences between heart rate samples.

Abstract: Video of one or more vehicle occupants is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in cognitive state analysis. The heart rate information and resulting cognitive state analysis are correlated to stimuli, such as digital media, which is consumed or with which a vehicle occupant interacts. The heart rate information is used to infer cognitive states. The inferred cognitive states are used to output a mood measurement. The cognitive states are used to modify the behavior of a vehicle. The vehicle is an autonomous or semi-autonomous vehicle. Training is employed in the analysis. Machine learning is engaged to facilitate the training. Near-infrared image processing is used to obtain the video. The analysis is augmented by audio information obtained from the vehicle occupant.

Abstract: Video of one or more people is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media, which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The inferred mental states are used to output a mood measurement. The mental state analysis, based on the heart rate information, is used to optimize digital media or modify a digital game. Training is employed in the analysis. Machine learning is engaged to facilitate the training.

Abstract: Video of one or more people is obtained and analyzed. Heart rate information is determined from the video. The heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media, which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The inferred mental states are used to output a mood measurement. The mental state analysis, based on the heart rate information, is used to optimize digital media or modify a digital game. Training is employed in the analysis. Machine learning is engaged to facilitate the training.

Abstract: Video of one or more people is obtained and analyzed. Heart rate information is determined from the video and the heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The mental state analysis, based on the heart rate information, can be used to optimize digital media or modify a digital game.

Abstract: A system and method for evaluating heart rate variability for mental state analysis is disclosed. Video of an individual is captured while the individual consumes and interacts with media. The video is analyzed to determine heart rate information with heart rate variability (HRV) being calculated and being understood to be in response to stimuli from the media. The analysis of heart rate variability is based upon a sympathovagal balance derived from a ratio of low frequency heart rate values to high frequency heart rate values. Heart rate variability is analyzed to determine changes in an individual's mental state related to the stimuli. Heart rate variability is determined and thereby mental state analysis is performed to evaluate media.

Abstract: A method for accurate pain analysis using a combination of electrodermal data and self-report information is described. To obtain pain measurements, electrodermal activity is collected and analyzed. One or more biosensors obtain electrodermal data from a person. The electrodermal data is filtered to produce skin conductance values. The skin conductance values are associated with self-report pain information from a person over multiple time regions. The associating allows electrodermal activity to be related to self-reported pain (SRP) data to derive an objective pain measurement.

Abstract: Video of one or more people is obtained and analyzed. Heart rate information is determined from the video and the heart rate information is used in mental state analysis. The heart rate information and resulting mental state analysis are correlated to stimuli, such as digital media which is consumed or with which a person interacts. The heart rate information is used to infer mental states. The mental state analysis, based on the heart rate information, can be used to optimize digital media or modify a digital game.