# Surveying Human Habit Modeling and Mining Techniques in Smart Spaces
## 1 Introduction  
The progress of information and communication technologies has many faces; while computing speed, reliability and level of miniaturization of electronic devices increase year after year, their costs decrease. This allows a widespread adoption of _embedded systems_ (e.g., appliances, sensors, actuators) and of powerful computing devices (e.g., laptops, smartphones), thus, turning _pervasive_ (or _ubiquitous_) computing into reality. Pervasive computing embodies a vision of computers seamlessly integrating into everyday life, responding to information provided by sensors in the environment, with little or no direct instruction from users [1]. At the same time, connecting all these computing devices together, as networked artefacts, using local and global network infrastructures has become easy. The rise of applications that exploit these technologies represents a major characteristic of the _Internet-of-Things_ (IoT) [2]. _Smart spaces_ represent an emerging class of IoT-based applications. Smart homes and offices are representative examples where pervasive computing could take advantage of _ambient intelligence_ (AmI) more easily than in other scenarios where artificial intelligence--AI problems soon become intractable [3]. A study about the current level of adoption of commercial smart home systems is provided in [4]. This study reveals how people understanding of the term \"smart\" has a more general meaning than what we presented here as AmI; in particular, it also includes non-technological aspects such as the spatial layout of the house. Additionally, an automated behavior is considered as smart, especially from people without a technical background, only if it performs a task quicker than the user could do by himself. The research also reveals that interest in smart home systems is subject to a virtuous circle such that people experiencing benefits from their services feel the need of upgrading them. The universeAAL specification [5] defines a smart space as \"an environment centered on its human users in which a set of embedded networked artefacts, both hardware and software, collectively realize the paradigm of _ambient intelligence_\" (AmI). Many different definitions of AmI are provided in the literature, e.g., [6] introduces a set of distinguishing keywords characterizing AmI systems, namely: sensitivity, responsiveness, adaptivity, ubiquity and transparency. The term _sensitivity_ refers to the ability of an AmI system to sense the environment and, more generally, to understand the _context_ it is interacting with. Strictly related to sensitivity are _responsiveness_ and _adaptivity_, which denote the capability to timely act, in a reactive or proactive manner, in response to changes in the context according to user preferences (_personalization_). Sensitivity, responsiveness and adaptivity all contribute to the concept of _context awareness_. Finally, the terms _ubiquity_ and _transparency_ directly refer to concept of pervasive computing. AmI is obtained by merging techniques from different research areas [5] including artificial intelligence (AI) and human-computer interaction (HCI). The different proposed approaches largely differs in terms of models taken as input for both space dynamics and human behavior in it, how these models are extracted, what kind of input sensors they need, which are the constraints under which they are supposed to effectively work. The performance evaluation of all these approaches, especially in the academic field, is often conducted in controlled situations where some of the features of a real environment are hid by the need for repeatability of the experiment. In this paper, we propose a comparative framework for techniques and approaches for modeling and extracting models to be employed with modern smart spaces. Differently from other surveys in the same area, this work focuses on giving an overview of the approaches by enforcing a taxonomy with dimensions that are chosen in order to understand the suitability of a specific technique to a specific setting.