Modeling Context Aware Interaction for Wayfinding using Mobile Devices Chao Li
Katharine Willis
CASA, University College London 1-19 Torrington Place London, UK + 44 20 7679 1807
SFB/TR8, University of Bremen Bibliothekstrasse 1 Bremen, Germany + 49 421 218 9043
[email protected]
[email protected]
ABSTRACT In this paper, we introduce and implement a model for contextaware interaction, and demonstrate its usefulness through an empirical study of interaction in a wayfinding task. Firstly, we outline the challenge of modeling context-awareness in dynamic interaction arising from wayfinding and navigation assistance applications. The conceptual model is developed and explained with emphasis on the context-aware interaction, and the three dynamic aspects in such interactions. Secondly, a wayfinding experiment is used to implement the conceptual model. We finish by concluding that the model proposed enables a dynamically inter-relational concept of context to be considered, and that the experiment described provides a valuable method for evaluating context-aware interaction in wayfinding.
Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interface - Interaction styles, User-centered design, Theory and methods
General Terms Experimentation, Human Factors, Performance, Theory, Design
Keywords Context-aware interaction, Mobile devices, Wayfinding
1. INTRODUCTION Human-computer interaction research, to date, has focused upon understanding the ways in which humans interact with computers, aiming to have a system to satisfy user needs and requirements in terms of system functionality and operation [11, 13]. With an increasing number of applications using mobile devices, mobile HCI research places more emphasis to human-device interaction in terms of developing mobile context-aware applications [1]. Kjeldskov and Graham [8] have reviewed a number of mobile HCI research methods, noting a focus towards building systems and a lack of emphasis on understanding design and usage. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MobileHCI'06, September 12–15, 2006, Helsinki, Finland. Copyright 2006 ACM 1-59593-390-5/06/0009...$5.00.
In general, the interaction between individuals and mobile devices can be viewed as a new kind of human-computer interaction. In conventional desktop-based human-computer interaction, the surrounding environment is under-represented in such interaction. For mobile HCI, the surrounding environment has started to be brought into consideration, for example by directly observing the phenomena and people [12, 7]. However, to date the dynamics of the surrounding environment has not had a significant role in the majority of the research and approaches in mobile HCI.
2. CONTEXT IN MOBILE HCI In the field of Mobile HCI there are many definitions and surveys of context have been presented ([4], [5], [3]), but they are often broad-ranging and vague so that ‘context has emerged as a general word and has a loose definition’ [3]. Further, the general approach is mainly limited to a definition of context as data or information which describes the state of a system. For instance Dey’s commonly accepted definition that ‘context is any information that can be used to characterize the situation of an entity. An entity can be a person, a place or an object that is considered relevant to the interaction between a user and an application’ [4]. In this case it appears that the environment, whether a location or other characteristic of the physical space, is not considered a dynamic part of the interaction itself, as this occurs exclusively between the user and the device. Location is critical to such an extent that Chen and Kotz observed in their study of context awareness that ‘few contexts other than location have been used in actual applications’ [3]. Nevertheless, in context-aware applications, location is often considered an index from which to infer the overall context influencing the mobile application [5]. The consequence of this is that the surrounding environment has not been regarded as a mutable information source with which people interact. Therefore, there has been no explicit focus upon the dynamic interactions between individuals, mobile devices and environments.
3. WAYFINDING USING MOBILE DEVICE 3.1 Challenges of Wayfinding Tasks Wayfinding is a complex, purposive and motivated spatial activity [6], and it is vital for the individual to be engaged with the real world environment, both spatially and temporally, during a task. Many different forms of assistance have been developed to support and guide individuals in spatial tasks, and today a proliferating number of commercially available mobile devices have been developed specifically to offer navigation assistance.
Although many forms of assistance are available, individuals completing spatial tasks often make errors, such as being disoriented and getting lost. Wayfinding involves a sequence of plans and decisions as a dynamic process. Individuals plans often change during the course of a task due to changes in the environment (e.g. it rains), changes in individual goal (e.g. I’m hungry), changes in device (e.g. poor GPS signal). Due to the sequential nature of the process any errors or changes that are made in the task are com-pounded over time, with the result that a small change can have significant consequences in the task. Additionally the wayfinding assistance itself may also exhibit spatial and temporal variation, which can include breaks in functionality or connectivity, imprecise positioning and errors in recording and representation. These give rise to breaks in the interaction between technology, individual and the environment which are often not made explicit in the interaction, but simply result in misinterpretation [14], and thus poor performance in a wayfinding task.
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3.2 Context-awareness in Wayfinding Assistance Contextual data is typically gathered from multiple distributed heterogeneous sources [4]. The integration of various data sets from fundamentally different sources can generate ambiguity. In wayfinding tasks even minor ambiguity can have quite significant effects on the outcome of the task. Both the integration and presentation of ambiguous context information can result in problems in interpretation with the outcome that the user makes errors in the interaction in a wayfinding task. Therefore, mobile wayfinding applications pose important challenges to reconsider the commonly accepted models of context-aware interaction. To date, much research in this area mainly focuses on adapting content to user needs, and obtaining and interpreting the context information from the environment and from individuals [2, 4, 15].
4. MODEL OF CONTEXT-AWARE INTERACTION The interactive conceptual model in Li’s work [9, 10] has considered environments, individuals and devices in its totality, with explicit focus on the real time interaction and information transactions between them. The model [10] regards both environments and devices as dynamic sources of information during the interaction. Individuals, as another facet of the model, can access and acquire spatial information through a mobile device whilst completing spatial activities; and they can also gain information directly from the environment. In wayfinding tasks, the contexts regarding individuals, environments and devices can be dynamic, as discussed in 3.1 and 3.2. Mobile devices, as assistance in wayfinding, should be able to offer applications which adapt to these dynamic contexts. Therefore, there is a need for a model to address these dynamic aspects. Based on the interactive model given above, we further propose a conceptual context-aware interaction model, which places emphasis on the dynamic contexts during the interaction.
4.1 Extending the Model The context-aware interaction model shown in Figure 1 comprises of two parts. Part 1 of the model focuses on the context-aware interaction, whilst Part 2 emphasizes the dynamic nature and connectivity of three dynamic aspects in the interactions.
A C
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Figure 1. A conceptual model of context-aware interaction In order to study the context-aware interactions between environments, individuals and devices in wayfinding tasks, we have identified three key dynamic aspects of the interaction (see Part 1 in Figure 1): location, interface and representation. The first aspect, ‘location’, is to model the context aspect during the course of the interaction between environments and individuals. The term ‘location’ is in a broad meaning of location-related situations, and not limited to the concept of (x, y) position. The second aspect in the model is the context in the interaction between individuals and devices, which is denoted as ‘interface’, characterizing the degree of automation within the device for resolving queries. The third aspect, ‘representation’ describes the degree of specificity or generalization that environments are represented through mobile devices. It is important to note that these three aspects of context should be considered in its totality in the interaction, although they have to be defined individually. Therefore, the emphasis in this model is the intersection of all three aspects, capturing the interaction which can be influenced by location, interface and representation. During wayfinding tasks, how individuals interact with their mobile devices and the types of information they requested from devices can be influenced by the spatial context of the environment such as the complexities of the environment. Part 2 of the model, shown as a three dimensional cube, addresses the dynamic nature of the context-aware interaction. The three axes represent location, interface and representation respectively; and the space in the 3D cube represents the intersection of the three aspects. The labels ‘A’ and ‘C’ on the central axis refer to attributes of Ambiguity and Certainty respectively. In all three identified aspects of the interaction, the nature of the data can
vary from very exact or precise (i.e. towards ‘C’ on each axis), to vague or uncertain (i.e. away from ‘C’ on each axis). During wayfinding tasks, the level of certainty in individuals’ location can vary, for instance from certainty ‘C’ towards ambiguity ‘A’ which could be caused by users getting disorientated or system having poor GPS signal. This would influence the aspect ‘interface’, and the degree of automation would vary (moving away from ‘C’ on the ‘interface’ axis) which may require a reduced level of automation and give users more control to input queries. Meanwhile, the level of representation on the mobile devices required by users could also change (moving along the ‘representation’ axis), such as changing from one type of map to another as suits the need. Point ‘C’ illustrates the situation where individuals are certain of their locations, with well perceived representations and higher automation provided by the device. In most real world situations, the certainty of individuals’ location, the frequency of interacting with devices and the types of information chosen can vary. Such change of contexts is incorporated into the model. This model will not cover all aspects of context-aware interaction; however, it provides a framework for studying the dynamic nature of context in Mobile HCI applications.
In the experiment, there were 27 participants (14 male, 13 female) who were required to find five destinations in sequence and then return to the start point. All participants were unfamiliar with the area. During the experiment, participants could access available information from the PDA at any location to assist them in completing the pre-described wayfinding tasks. The area where the wayfinding experiment took place is shown in Figure 3 with start/finish point and five destinations as D1 to D5. Participants took roads of their choice to reach each destination in turn. The design for each successive destination of the wayfinding tasks was intended to have different levels of complexity in terms of length of route, numbers of turnings and number of choice points passed. During the wayfinding experiment, individuals’ (x, y) positions in the environment were recorded automatically. Also captured was the usage of PDA information, including the types of information used and the number of times they were accessed.
D1 D2
5. APPLYING THE MODEL A wayfinding scenario has been used to apply the conceptual model shown in Figure 1. The three dynamic elements – devices, individuals and environments - have been included in the empirical setting for the wayfinding experiment. The interaction was recorded and studied using the model with focus on the three specific aspects: location, interface and representation.
5.1 Experiment Design The experiment setting included three main components: Virtual Reality (VR) urban models, a mobile device and data recording software [9]. The VR model was created based on both the layout and the characteristics of a residential area in UK. An immersive virtual environment has been used. This setting provided an environment in which individuals could ‘walk around’ with realistic street level views; whilst the mobile device simulated information services for wayfinding. Another component was a set of software to record individuals’ interactions. A PDA was used as the mobile device in this study, through which individuals could access wayfinding assistance information whenever and wherever they desired. This information was structured in web page style; and could be accessed via relevant icons and text on the PDA screen. The information provided includes text / voice route instructions, schematized maps with landmarks and detailed maps. Figure 2 illustrates some parts of this empirical setting.
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5.2 Experiment Methods
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Figure 2. PDA screen: (a) schematic map; (b) a detailed map.
Start/Finish
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Figure 3. The test environment.
5.3 Experiment Results and Discussion The level of automation in the ‘interface’ can be measured as the frequency and time used in accessing the PDA. In this paper, only the frequency is analyzed as shown in Figure 4. Each route represents the wayfinding journey between successive destinations, and reflects different levels of complexity of the urban environment, and therefore the changing context of location. As the figures show, in completing each wayfinding task, individuals had different levels of interaction with the mobile device to obtain information. The change in frequency for each route therefore reflects how the contexts influence the interaction between the individuals and the mobile device. The level of representation in the interaction can be studied from the types of information participants required and accessed in completing the wayfinding tasks (Figure 5). These different representations were chosen by participants in response to changes in the environment on different routes. The three types of information available were: schematized maps, detailed maps and route instructions. For the schematized maps, overview layout types of information are provided to individuals, whilst detailed maps provided more comprehensive information, but covered a smaller area. In route 3 where individual’s location uncertainty tended to be greater, and in route 4 where the environment was
more complex characterized by more junctions/ decision points, the use of schematized maps increased as they provided configurational information of the area. At the same time, the use of route instructions, which are procedural representations, declined.
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7. ACKNOWLEDGMENTS The authors wish to thank: the Economic and Social Research Council, UK; the support of the DAAD, and the Spatial Cognition Program at University of Bremen.
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provide a method for the evaluating the dynamic characteristics of the of environment, individual and device contexts in wayfinding tasks.
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Figure 4. Interaction with the PDA on each route Representation: percentage of 60
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Figure 5. Change in representation used on each route During wayfinding tasks, the changing context of location (e.g. route) altered the level of individual interaction with the device. This has implications for the degree of automation the device provides. The inverse relation was noted for the representation aspect in the dynamic interaction. When the environment was considered more complicated such as uncertainty in the location, the schematic map representation provided overall layout information and was accessed more often (see route 3 and 4). The implication of these to system and application design in Mobile HCI is that it is important to consider the three dynamic interlinked aspects of context aware interaction; individual, device and environment. For instance, the level of automation of device might need to be considered and varied according to the context related to location and representation.
6. CONCLUSIONS In this paper we have identified some issues with the current definition of context and context-aware applications. We have highlighted that the environment is under represented particularly with its dynamic nature in the interaction with individuals and devices in mobile HCI research. Consequently, we proposed a conceptual model, which is a framework for context awareness in the interaction between individuals, devices and environments. An experiment implemented the conceptual model, and demonstrated that the concept of context-aware interaction can
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