A method for performing context inference for a mobile device comprising: receiving a first data sample from at least a first sensor; performing a first classification of the first data sample from the at least first sensor; determining a confidence value for the first classification, wherein the confidence value is based on a comparison of the first data sample to data samples in a historical data set to estimate a most likely, nearest classification of the first data sample from the at least first sensor, and wherein the confidence value is the difference between the probability of said nearest classification and the probability of the next nearest classification of the first data sample from the at least first sensor; detecting a change in classification from a prior data sample classification immediately prior in time, of a prior data sample from the first sensor, to the first classification, wherein a database of probable and improbable classification changes is accessed to determine, if the change in classification of said first sensor matches a change in classification predetermined to trigger a confidence failure; activating a second sensor, either if the confidence value of the first classification fails to meet a confidence threshold, or based on a determination that the change in classification of said first sensor is an improbable change triggering a confidence failure, wherein said activation made based on a determination that the change in classification triggers a confidence failure is made independent from the confidence value of the first classification; and performing a second classification of a second data sample from the activated second sensor jointly with the first data sample from the at least first sensor. receiving a first data sample from at least a first sensor; performing a first classification of the first data sample from the at least first sensor; determining a confidence value for the first classification, wherein the confidence value is based on a comparison of the first data sample to data samples in a historical data set to estimate a most likely, nearest classification of the first data sample from the at least first sensor, and wherein the confidence value is the difference between the probability of said nearest classification and the probability of the next nearest classification of the first data sample from the at least first sensor; detecting a change in classification from a prior data sample classification immediately prior in time, of a prior data sample from the first sensor, to the first classification, wherein a database of probable and improbable classification changes is accessed to determine, if the change in classification of said first sensor matches a change in classification predetermined to trigger a confidence failure; activating a second sensor, either if the confidence value of the first classification fails to meet a confidence threshold, or based on a determination that the change in classification of said first sensor is an improbable change triggering a confidence failure, wherein said activation made based on a determination that the change in classification triggers a confidence failure is made independent from the confidence value of the first classification; and performing a second classification of a second data sample from the activated second sensor jointly with the first data sample from the at least first sensor. <EOS>