I am a novice at HMMs but I have tried to build a code using Jahmm for the UCI Human Activity Recognition data set. The data set has 561 features and 7352 rows, and also includes the xyz inertial values of both the accelerometer and gyroscope, and it is mainly for recognizing 6 activities: Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, and Laying. The data is normalized [-1,1], but not z-scaled. I can only get decent results after scaling (scale() function in R). After scaling, I have tried PCA, correlation of 90+%, and randomForest importance measures with mtry=8 for dimensional reduction, but so far, randomForest was the only one that seemed to work, but results are still quite low (80%). Also, sometimes, some activities give NaN values when run on the Jahmm code.
According to what I've read about HMMs so far, these results are too low. Should I do more preprocessing before I use the said dimension reduction techniques? Is there a particular dimension reduction technique that is compatible with HMMs? Am I overfitting it? or am I better off making it discrete instead of continuous? I really have to do Activity Recognition and HMMs for my project. I would be so glad to get suggestions/feedback from people who have already tried Jahmm and R for continuous HMMs. It would also be great if someone could suggest a package/library that uses log probabilities and gives off a viterbi sequence from a fitted HMM given a new set of test data.
