# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 8 Discussion  
In this paper, we applied the SLR approach to classify most prominent approaches in the ambient intelligence area according to how they approach the different stages of a model life cycle. The kind of classification introduced in the paper allows to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment.  
The field of ambient intelligence has been very active in the last 10 years, and first products (such as NEST) have hit the market. Unfortunately, as of 2018, no examples of holistic approaches, such as those proposed in the vast majority of academic works, are available outside research labs. The reasons for this are multiple.  
First of all, the vast majority of approaches either require a big effort in terms of _(i)_ expert time to produce hand-made models in specification based approaches, or _(ii)_ user time to manually label training sets for supervised learning based approaches. Unsupervised methods represent a small percentage on the total amount of academic works.  
Secondarily, model update is often not taken into account by the proposed approaches. Human habits, the way of performing activities and preferences generally change over time. In the vast majority of cases, proposed solutions do not provide an explicit method to update models, thus, requiring modeling to start from scratch, with the drawbacks identified in the first step.  
As a third point, support for multiple users performing actions separately and multiple users collaborating in a single activity are often neglected by proposed approaches. Supporting multiple users poses serious limitations to the applicability of available solutions. This problem is even harder to solve when human guests must be taken into account.  
As a final point, the validity of the declared results is often difficult to confirm on datasets different from the one used for tests, as in the vast majority of cases source code is not made available. The lack of benchmark data, even though many datasets are made freely available by research groups, makes this situation even harder.  
[PERSON] contributed to methodology, conceptualization, writing and editing; [PERSON] contributed to methodology, writing and editing, supervision and funding acquisition; [PERSON] contributed to investigation, conceptualization, writing and editing; [PERSON] contributed to supervision and funding acquisition.  
The work of [PERSON] has been partly supported by the Lazio regional project _SAPERI & Co_ (FILAS-RU-2014-1113), the work of [PERSON] has been partly supported by the Lazio regional project _Sapientia_ (FILAS-RU-2014-1186), all the authors have been also partly supported by the Italian projects _Social Museum & Smart Tourism_ (CTN01-00034-23154), _NEPTIS_ (PON03 PE-00214-3) and _RoMA--Resilence of Metropolitan Areas_ (SCN-00064).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AAL & Ambient Assisted Living \\ ADB & Active DataBase \\ AI & Artificial Intelligence \\ AmI & Ambient Intelligence \\ (A)NN & (Artificial) Neural Network \\ BPM & Business Process Management \\ CRF & Conditional Random Field \\ DST & Dampster-Shafer Theory \\ ECA & Event-Condition-Action \\ EP & Emerging Pattern \\ HCI & Human-Computer Interaction \\ HMM & Hidden Markov Model \\ IMU & Inertial Measurements Unit \\ IoT & Internet-of-Things \\ MC & Markov Chain \\ NB & Naive Bayes \\ RFID & Radio-Frequency IDentification \\ RQ & Research Question \\ SLR & Systematic Literature Review \\ SVM & Support Vector Machine \\ UHF & Ultra-High Frequency \\ WSN & Wireless Sensor Network \\ \end{tabular}