# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 6 Model Construction
The individual classes have relations that describe dependencies. Moreover Description Logic is employed to support ontological reasoning, which allows also to check the knowledge base consistency. It also infers additional information from registered facts. In nug-evfus[22], the interrelationships between sensors, context and activities are represented as a hierarchical network of ontologies (see Figure 8). A particular activity can be performed or associated with a certain room in the house, this information is modeled with an ontology of the network. In casas-hmm[14], each activity is performed in a protected environment, and the resulting log is recorded and labeled. Then, HMM model is built upon this dataset in a supervised way. The resulting model is shown in Figure 2. Observations (squares) model the sensor triggering, the states (circles) model the activities that can generate the observations according to certain probabilities. The goal is to infer the activities by processing the observations. This recognition technique is supporting single user data, but the problem of modeling multiple users is introduced. The same team, in casas-svm[17], employs SVM. In this second work, authors propose an interesting analysis of the different windowing strategies to be employed to gather measurements into observation vectors. Finally, in casas-hmmbcrf[16], experiments are performed with the same methodology adding CRF and NB modeling techniques to the analysis. In wang-ep[19], from log of sequential activities, Emerging Patterns are mined and the resulting set composes the model. In kros-crf[24], the model is trained out from a labeled dataset. The log is divided in segments 60-s long and each segment is labeled. The dataset is composed by multi-days logs: one day is used for testing the approach, the remaining for training the models. The resulting model is an undirected graph as in Figure 2.  
In reig-situation[25], a SVM model, built on statistical values extracted from the measurements of a given user, is used for classifying the roles. Then, this information, combined with other statistically extracted ones, is involved into the training of the HMM that models the situations. Figure 8: Hierarchical ontology structure adopted in nug-evfus[22] to model activities in a smart space. In yang-nn[26], the input vector contains the features to consider, the output vector the classes (activities). The back-propagation learning algorithm is used for training the ANNs. Three neural networks are built on labeled logs: a pre-classifier and two classifiers; static activities and dynamic activities are modeled with separated ANNs. The structure of the neural classifier consists of an input layer, an hidden layer and an output layer. In les-phi[31], given the maximum number of features the activity recognition system can use, the system automatically chooses the most discriminative sub-set of features and uses them to learn an ensemble of discriminative static classifiers for the activities that need to be recognized. Then, the class probabilities estimated from the static classifiers are used as inputs into HMMs. In bue-wisps[32], the users are asked to perform activities. The resulting log is used for training an HMM. In fleury-mcsvm[28], the classes of the classifier model the activities. Binary classifiers are built to distinguish activities: pairwise combinations selection. The number of SVMs for \(n\) activities will be \(n-1\). The features used are statistics from measurements. The algorithm proposed in casas-discore[11; 12; 13] is to improve the performance of activity recognition algorithms by trying to reduce the part of the dataset that has not been labeled during data acquisition. In particular, for the unlabeled section of the log, the authors employ a pattern mining technique in order to discover, in an unsupervised manner, human activity patterns. A pattern is defined here as a set of events where order is not specified and events can be repeated multiple times.