The task is to classify the activities of humans based on time series obtained by a Wireless Sensor Network.

The Activity Recognition system based on Multisensor data fusion (AReM) Dataset contains 7 folders that represent seven types of activities. In each folder, there are multiple files each of which represents an instant of a human performing an activity. Each file contain 6 time series collected from activities of the same person, which are called avg rss12, var rss12, avg rss13, var rss13, vg rss23, and ar rss23. There are 88 instances in the dataset, each of which contains 6 time series and each time series has 480 consecutive values.
The datasets 1 and 2 in folders bending1 and bending 2, as well as datasets 1, dataset 2, and dataset 3 in other folders are considered as testing data and other datasets are considered as training data.

Classification of time series usually needs extracting features from them. So, extract the time-domain features such as minimum, maximum, mean, median, standard deviation, first quartile, and third quartile for all the 6 time series in each instance. Estimate the standard deviation of each of the time-domain features, 90% bootsrap confidence interval for the standard deviation of each feature to select the three most important time-domain features.

The task is to classify bending activities (bending1 and bending2) from other activities using logistic regression. Break each time series in training set into l=1 to 20 time series of approximately equal length and use logistic regression to solve the binary classification problem using time-domain features. Calculate the p-values for logistic regression parameters in each model corresponding to each value of l. Use 5-fold stratified cross-validation to determine the best value of the pair (l, p), where p is the number of features used in recursive feature elimination.

The task is to classify bending activities (bending1 and bending2) from other activities using L1-penalized logistic regression. Break each time series in training set into l=1 to 20 time series of approximately equal length and use logistic regression to solve the problem. Calculate the p-values for logistic regression parameters in each model corresponding to each value of l. Use 5-fold stratified cross-validation to cross-validate for both l, the number of time series into which you break each of your instances, and the weight of L1 penalty in logistic regression objective function.

The task is to classify seven types of activities using Gaussian Naive Bayes classifier and Multinomial Naive Bayes classifier and compare the results.