# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 5 Model Types
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **RQ-A1** & **RQ-A2** & **RQ-A3** \\ \hline \multirow{2}{*}{reig-situation [25]} & \multirow{2}{*}{Video and audio records} & \multirow{2}{*}{Analogical} & Extraction of user position and \\  & & & speed (\(|\vec{v}|\)) \\ \hline \multirow{2}{*}{yang-nn [26]} & triaxial accelerometer on a wearable & \multirow{2}{*}{Discrete} & \multirow{2}{*}{-} \\  & board & & \\ \hline \multirow{3}{*}{stik-misvm [27]} & Audio-video, object interaction, & \multirow{3}{*}{Mixed} & Statistical features computed over \\  & environmental conditions, & & 30 s sliding window shifted in \\  & appliances’ use, 3D accelerometers & & increments of 15 s. \\ \hline \multirow{3}{*}{fleury-mcsvm [28]} & Many: position, microphones, & \multirow{3}{*}{Mixed} & Extraction of statistical features \\  & contact, wearable & & (e.g., number of events per \\  & & microphone) \\ \hline \multirow{2}{*}{palmes-objrel [29]} & RFID & Discrete & - \\ \hline \multirow{2}{*}{les-phi [31]} & Accelerometers + audio + & \multirow{2}{*}{Mixed} & \multirow{2}{*}{} \\  & barometric pressure & & \\ \hline \multirow{2}{*}{bue-wisps [32]} & WISPS: UHF RFID + accelerometers & Discrete & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._observation sequence X without violating the independence assumptions of the model. The graphical model representations of a HMM (a directed graph, Figure 2a) and a CRF (an undirected graph, Figure 2b) makes this difference explicit. In [24], a comparison between HMM and CRF is shown, where CRF outperforms HMM in terms of timeslice accuracy, while HMM outperforms CRF in terms of class accuracy. Another statistical tool often employed is represented by Markov Chains (MCs), which are based on the assumption that the probability of an event is only conditional to the previous event. Even if they are very effective for some applications like capacity planning, in the smart spaces context, they are quite limited because they deal with deterministic transactions and modeling an intelligent environment with this formalism results in a very complicated model. Support Vector Machines (SVMs) allow to classify both linear and non-linear data. A SVM uses a non-linear mapping to transform the original training data into an higher dimension. Within this new dimension, it searches for the linear optimal separating hyperplane that separates the training data of one class from another. With an appropriate non-linear mapping to a sufficiently high dimension, data from two classes can always be separated. SVMs are good at handling large feature spaces since they employ overfitting protection, which does not necessarily depend on the number of features. Binary Classifiers are built to distinguish activities. Due to their characteristics, SVMs are better in generating other kind of models with a machine learning approach than modeling directly the smart  
Figure 2: Examples of HMM and CRF models. Ellipses represent states (i.e., activities). Rectangles represent sensors. Arrows between states are state transition probabilities (i.e., the probability of moving from a state to another), whereas those from states to sensors are emission probabilities (i.e., the probability that in a specific state a sensor has a specific value). (**a**) HMM model example. Picture inspired by casas-hmm[14] and casas-ham[15]. (**b**) CRF model example. Picture inspired by kros-crf[24]. environment. For instance in [35] authors uses them combined with Naive Bayes Classifiers to learn the activity model built on hierarchical taxonomy formalism shown in Figure 3.  
Artificial Neural Networks (ANNs) are a sub-symbolic technique, originally inspired by biological neuron networks. They can automatically learn complex mappings and extract a non-linear combination of features. A neural network is composed of many artificial neurons that are linked together according to a specific network architecture. A neural classifier consists of an input layer, a hidden layer, and an output layer.