Abstract: A method includes, for each user in a population: audio recording the user reciting a story configured to elicit an emotion associated with a psychiatric condition; concurrently recording a set of biosignals of the user; generating a set of psychophysiological markers correlating instances of the emotion and the set of biosignals; assessing a clinical assessment for the condition in the user; correlating the set of psychophysiological markers and the clinical assessment; and compiling the correlations into a model configured to predict risk of condition onset in a user. The method also includes: accessing a series of biosignals of a first user; identifying a series of psychophysiological markers in the series of biosignals; based on the series of psychophysiological markers and the model, calculating a risk of onset of the condition in the first user; and, in response to the risk exceeding a threshold, serving the notification to the first user.

Abstract: A method includes: prompting a user to recite a story associated with a first target emotion; recording the user reciting the story and recording a first timeseries of biosignal data via a set of sensors integrated into a wearable device worn by the user; accessing a first timeseries of emotion markers extracted from the voice recording; labeling the first timeseries of biosignal data according to the first timeseries of emotion markers; generating an emotion model linking biosignals to emotion markers for the user based on the first emotion-labeled timeseries of biosignal data; detecting a second instance of the first target emotion exhibited by the user based on a second timeseries of biosignal data and the emotion model; and notifying the user of the second instance of the first target emotion.

Abstract: A method includes, during a first time, for each user in a population: accessing a clinical assessment for depression in the user; accessing a set of biosignal data of the user; deriving correlations between the set of biosignal data and the clinical assessment for depression; and compiling the correlations into a depression model configured to predict risk of future depression diagnosis. The method further includes, during a second time: accessing a series of biosignal data collected by a device worn by a first user; accessing a target duration; calculating a risk of presentation of depression symptoms by the first user within the target duration based on the series of biosignal data; and in response to the risk exceeding a threshold, serving a notification to a care provider associated with the first user, the notification indicating the risk and including a prompt to investigate the first user for treatment.

Abstract: One variation of a method includes: accessing a first timeseries of biosignal data collected by a wearable device worn by a user during a first time period; deriving a first insomnia profile, representative of a set of health indicators exhibited by the user during the first time period, based on the first timeseries of biosignal data; and selecting a treatment pathway for implementation by the user based on the first insomnia profile. The method further includes: accessing a second timeseries of biosignal data collected for the user by the wearable device during a second time period; deriving a second insomnia profile, representative of the set of health indicators exhibited by the user during the second time period, based on the second timeseries of biosignal data; and characterizing effectiveness of the treatment pathway based on a difference between the first insomnia profile and the second insomnia profile.

Abstract: A method includes: prompting a user to recite a story associated with a first target emotion; recording the user reciting the story and recording a first timeseries of biosignal data via a set of sensors integrated into a wearable device worn by the user; accessing a first timeseries of emotion markers extracted from the voice recording; labeling the first timeseries of biosignal data according to the first timeseries of emotion markers; generating an emotion model linking biosignals to emotion markers for the user based on the first emotion-labeled timeseries of biosignal data; detecting a second instance of the first target emotion exhibited by the user based on a second timeseries of biosignal data and the emotion model; and notifying the user of the second instance of the first target emotion.

Abstract: A method includes: prompting a user to recite a story associated with a first target emotion; recording the user reciting the story and recording a first timeseries of biosignal data via a set of sensors integrated into a wearable device worn by the user; accessing a first timeseries of emotion markers extracted from the voice recording; labeling the first timeseries of biosignal data according to the first timeseries of emotion markers; generating an emotion model linking biosignals to emotion markers for the user based on the first emotion-labeled timeseries of biosignal data; detecting a second instance of the first target emotion exhibited by the user based on a second timeseries of biosignal data and the emotion model; and notifying the user of the second instance of the first target emotion.

Abstract: One variation of a method for characterizing ADHD includes: accessing results of a cognitive evaluation executed by a user; accessing a timeseries of biosignal data collected via sensors worn by the user during execution of the cognitive evaluation; accessing a global model linking results of the cognitive evaluation and biosignal data to cognitive states of users diagnosed with ADHD; and interpreting a confidence score, representing probability of an ADHD diagnosis, based on results of the cognitive evaluation, the timeseries of biosignal data, and the global model. The method further includes, in response to the confidence score exceeding a threshold confidence: selecting a treatment pathway for the user based on results of the cognitive evaluation and the timeseries of biosignal data; and deriving an ADHD model for the user linking biosignal data to cognitive states based on results of the cognitive evaluation, the timeseries of biosignal data, and the global model.

Abstract: A method includes: prompting a user to recite a story associated with a first target emotion; recording the user reciting the story and recording a first timeseries of biosignal data via a set of sensors integrated into a wearable device worn by the user; accessing a first timeseries of emotion markers extracted from the voice recording; labeling the first timeseries of biosignal data according to the first timeseries of emotion markers; generating an emotion model linking biosignals to emotion markers for the user based on the first emotion-labeled timeseries of biosignal data; detecting a second instance of the first target emotion exhibited by the user based on a second timeseries of biosignal data and the emotion model; and notifying the user of the second instance of the first target emotion.

Abstract: One variation of a system for unobtrusively recognizing emotions of users includes: a galvanic skin response sensor; a heart rate sensor; a skin temperature sensor; a motion sensor; and a controller configured to sample a sequence of biosignal values of a user from the galvanic skin response sensor, the heart rate sensor, the skin temperature sensor, and the motion sensor, to implement statistical tests, self-organising maps, and clustering techniques to identify of significant changes in characteristics of the sequence of biosignal values, and to predict an emotional status of the user based on the significant changes in characteristics of the sequence of biosignal values.