# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 4 Supported Sensors  
Sensing technologies have made significant progress on designing sensors with smaller size, lighter weight, lower cost, and longer battery life. Sensors can, thus, be embedded in an environment and integrated into everyday objects and onto human bodies without affecting users' comfort. Nowadays, sensors do not only include those traditionally employed for home and building automation (e.g., presence detectors, smoke detectors, contact switches for doors and windows, network-attached and close circuit cameras) but also more modern units (e.g., IMU--Inertial Measurements Units such as accelerometer and gyroscopes, WSN nodes), which are growingly available as part of off-the-shelf products. Measured values are usually affected by a certain degree of uncertainty. Sensors have indeed their own technical limitations as they are prone to breakdowns, disconnections from the system and environmental noise (e.g., electromagnetic noise). As a consequence, measured values can be out of date, incomplete, imprecise, and contradictory with each other. Techniques for cleaning sensor data do exist [33], but uncertainty of sensor data may still lead to wrong conclusions about the current context, which in turn potentially lead to incorrect behaviors of the system. Formalisms employed for representing knowledge in AmI systems often need environmental variables to be _binary_ or _categorical_. A wide category of sensors (e.g., temperature sensors) produce instead numerical values, making it necessary to discretize sensor data before they can be used for reasoning. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Acronym** & **Study Title** \\ \hline rib-prob [20; 21] & [PERSON], [PERSON], and [PERSON] [PERSON] A probabilistic ontological framework for the recognition of multilevel human activities. \\ \cline{2-3}  & [PERSON], [PERSON], [PERSON], and [PERSON] Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning. \\ \hline nug-e\#fus [22] & [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] Evidential fusion of sensor data for activity recognition in smart homes. \\ \hline wang-hier [23] & [PERSON] et al. Fast effective rule induction. \\ \hline kros-crf [24] & [PERSON] [PERSON], [PERSON], [PERSON], and [PERSON] Accurate activity recognition in a home setting. \\ \hline reig-situation [25] & [PERSON], [PERSON], and [PERSON] Learning situation models in a smart home. \\ \hline yang-nn [26] & [PERSON], [PERSON], and [PERSON] Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers. \\ \hline stik-misvm [27] & [PERSON], [PERSON], [PERSON], and [PERSON] Weakly supervised recognition of daily life activities with wearable sensors. \\ \hline Fleury-mcsvm [28] & [PERSON], [PERSON], and [PERSON] Svm-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. \\ \hline palmes-objrel [29] & [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] Object relevance weight pattern mining for activity recognition and segmentation. \\ \hline moore-strawman [30] & [PERSON], [PERSON], and [PERSON] Activity recognition using cell phone accelerometers. \\ \hline les-phi [31] & [PERSON], [PERSON], and [PERSON] A practical approach to recognizing physical activities. \\ \hline bue-wisps [32] & [PERSON], [PERSON], [PERSON], and [PERSON] Recognizing daily activities with rfid-based sensors. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Discretization methods in machine learning and data mining are usually classified according to the following dimensions [34]:  
* _Supervised_ vs. _Unsupervised_. Unsupervised methods do not make use of class information in order to select cut-points. Classic unsupervised methods are equal-width and equal-frequency binning, and clustering. Supervised methods employ instead class labels in order to improve discretization results. * _Static_ vs. _Dynamic_.