A context-sensitive conceptual framework for activity modeling
JOURNAL OF SPATIAL INFORMATION SCIENCE
Number
A context-sensitive conceptual framework for activity modeling
Rahul Deb Das 0
Stephan Winter 0
0 Department of Infrastructure Engineering, The University of Melbourne , Australia
Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place “along the way,” such as “reading on the bus,” are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., “holidaying in a country,” could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts.
activity; action; context; ontology; travel demand modeling; trajectory
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c by the author(s)
1 Introduction
With the emergence of pervasive and mobile computing, and especially location-based
services, there has been a growing interest in a theoretical framework facilitating the
processing and sharing of activity information more effectively between human and computer
[
12, 42, 56, 71, 72
]. While individual disciplines have worked towards their own
frameworks, we will demonstrate that they are incompatible, and an overarching framework is
still lacking.
In principle, activity, and synonymously action, requires agency, or a purposeful,
goaldirected performance that is available to awareness [89]. Based on the usage of the word
activity in natural language, WordNet [17] defines activity as any specific behavior or an
action or bodily function. In this view, travel, which is understood here as any
purposeful, goal-directed change of location, is an activity (or action). Furthermore, any complex
travel can be composed of simpler activities, some of them forming travel activities
themselves (such as “taking the bus on the way to work”), and others are non-travel or
stationary activities (such as “reading the papers on the bus”). In contrast to many other
disciplines, human-computer interaction research (HCI) actually applies this understanding by
modeling activity from a purely motivational, goal-oriented, and operational perspective
[41, 42, 43] where the activity is motivational and oriented towards an objective. Actions
(which are subsumed by activity) required to perform the activity are oriented towards
goals and realization of the entire phenomena happens through operations. HCI assumes
activity as interaction of a subject with an object to fulfill certain needs through mediation
which may involve the process of externalization and internalization [42]. Thus in HCI
activity is characterized mostly by the why, what, and how, but less so by the where
(location) and when (time), despite most activities being generally constrained by space or time
or both. Obviously this understanding can cope with travel as well as non-travel activities.
In pervasive computing, the common subject of research into activity recognition are
human motion trajectories [82], whether captured, for example, from diaries [84], social
networks [
13
], checkpoints or cordons [15], GPS [
92, 95
], or CCTV cameras [76].
Accordingly, a variety of disciplines, from geography over data mining [
29, 61, 78, 81, 93
] to
computer vision [
3
], provide tools for motion analysis. But across these disciplines activity
remains an ambiguous term, to the point of direct contradiction. For example, in (urban)
transport research activity generally bears semantics related to times spent at home, work,
restaurants, or shops [
40, 52, 91
], and is the cause for travel between the locations of these
activities [ (...truncated)