Fig. 5

Robustness of machine learning methods to different levels of noise for 4 out of 7 activity classes. At low levels of noise, lower that 20%, non-linear methods performed well achieving performance higher than 0.7 MCC units for most of the tested datasets. Instead, at higher level of noise, equal to or higher than 30%, performance for most algorithms dropped below 0.7 MCC and in several occasions even lower than 0.6 at 50% of noise. Naïve Bayes method was found to be the least affected method achieving in several tested datasets performance higher than 0.6 MCC even at the highest level of noise tested 50% and outperforming more complex methods