# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 4 Supported Sensors
Static discretization methods perform discretization, as a preprocessing step, prior to the execution of the learning/mining task. Dynamic methods instead carry out discretization on the fly. * _Global_ vs. _Local_. Global methods, such as binning, are applied to the entire \(n\)-dimensional space. Local methods, as the C4.5 classifier, produce partitions that are applied to localized regions of the instance space. A local method is usually associated with a dynamic discretization method. * _Top-down_ vs. _Bottom-up_. Top-down methods start with an empty list of cut-points (or split-points) and keep on adding new ones to the list by splitting intervals as the discretization progresses. Bottom-up methods start with the complete list of all the continuous values of the feature as cut-points and remove some of them by merging intervals as the discretization progresses. * _Direct_ vs. _Incremental_. Direct methods directly divide the range of a quantitative attribute in \(k\) intervals, where the parameter \(k\) is provided as input by the user. Conversely, incremental methods start from a simple discretization and improve it step by step in order to find the best value of \(k\). An important aspect to take into account when evaluating the usage of a particular category of sensors is the final user acceptance. Wearable sensors or cameras usually provoke a certain level of embarrassment in the users that must be taken into account. Table 2 shows, for each of the selected papers, the answers to research questions A1, A2 and A3. A3 is always empty for methods only supporting discrete sensors. \begin{table}
**Approach** & **RQ-A1** & **RQ-A2** & **RQ-A3** \\ \hline \hline aug-eca[9] & Position & Discrete & - \\ \hline \hline aug-APub8[10] & \begin{tabular}{l} Object sensor, motion sensor, and \\ context sensor \\ \end{tabular} & Mixed & - \\ \hline \hline casas-discorec[11; 12; 13] & motion + interaction (RFID) & Discrete & - \\ \hline casas-hmm[14] & motion + interaction (RFID) & Discrete & - \\ \hline casas-hmmhcrf[16] & motion + interaction (RFID) & Discrete & - \\ \hline casas-hmm[15] & motion + interaction(RFID) & Discrete & - \\ \hline casas-svm[17] & Passive InfraRed & Discrete & - \\ \hline \hline \end{tabular} \begin{tabular}{l l l} \hline \hline \multirow{2}{*}{chen-ont[18]} & \begin{tabular}{l} Dense sensed environment: RFID, \\ PIR, context sensors (tilt, pressure) \\ \end{tabular} & Mixed & \begin{tabular}{l} Sensor measurements continuously \\ fed into the system. \\ \end{tabular} \\ \hline rib-PROB[20; 21] & RFID + body sensor & Discrete & - \\ \hline \hline wang-ep[19] & \begin{tabular}{l} RFID + user’s movement, \\ environmental information, user \\ location, human-object interaction \\ \end{tabular} & Mixed & \begin{tabular}{l} From analogical measurements, \\ like acceleration data and \\ temperature, statistical values are \\ computed (e.g., mean, variance) \\ \end{tabular} \\ \hline nug-effus[22] & PIR, contact, pressure mats & Discrete & - \\ \hline wang-hier[23] & RFID + body sensors (IMOTE2) & Discrete & - \\ \hline kros-crf[24] & \
\begin{tabular}{l} large variety of sensors, from \\ contact to humidity/temperature \\ \end{tabular} & Mixed &
\begin{tabular}{l} Temporal probabilistic model: \\ sampling measurements creating \\ sequences of observations \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Supported Sensors—RQ-A1: sensors type—RQ-A2: data produced type—RQ-A3: discretization strategy.