Monitoring wellness using a wireless handheld device
First Claim
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1. A method for detecting a subject'"'"'s stress level associated with an activity, comprising:
- connecting the subject to a sensor that senses a value of a biometric;
during the activity,repeatedly sensing the value of the biometric over each of a plurality of time windows;
computing, for each time window, a difference between each sensed biometric value in the time window and an ambient value to determine a maximum difference for the time window and a minimum difference for the time window;
computing, for each time window, a deviation, for the sensed biometric values in the time window by subtracting the time window'"'"'s minimum difference from the time window'"'"'s maximum difference; and
detecting the stress level based on the computed deviations for the time windows, wherein detecting the stress level further comprises inferring a stress state transition from time window to time window based on a temporal Bayesian Network (TBN) model and corresponding computed deviations.
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Abstract
A method for detecting a subject'"'"'s stress level associated with an activity includes (a) connecting the subject to a sensor that senses a value of a biometric; (b) during the activity, (i) repeatedly sensing the value of the biometric over each of a plurality of time windows; and (ii) computing, for each time window, a deviation in the sensed values of the biometric; and (c) detecting the stress level based on the computed deviations. In one implementation, the value of the biometric is a skin temperature measurement. The method may be implemented as an application in a wireless handheld device, such as a cellular telephone.
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Citations
8 Claims
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1. A method for detecting a subject'"'"'s stress level associated with an activity, comprising:
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connecting the subject to a sensor that senses a value of a biometric; during the activity, repeatedly sensing the value of the biometric over each of a plurality of time windows; computing, for each time window, a difference between each sensed biometric value in the time window and an ambient value to determine a maximum difference for the time window and a minimum difference for the time window; computing, for each time window, a deviation, for the sensed biometric values in the time window by subtracting the time window'"'"'s minimum difference from the time window'"'"'s maximum difference; and detecting the stress level based on the computed deviations for the time windows, wherein detecting the stress level further comprises inferring a stress state transition from time window to time window based on a temporal Bayesian Network (TBN) model and corresponding computed deviations. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification