Uncertainty and Error Handling in Pervasive Computing: A User's Perspective
Marie-Luce Bourguet · Ubiquitous Computing · 2011
From this chapter's perspective, pervasive computing is a new class of multimodal systems, which employs passive types of interaction modalities, based on perception, context, environment and ambience (Abowd & Mynatt, 2000;Feki, 2004;Oikonomopoulos et al., 2006).By contrast, early multimodal systems were mostly based on the recognition of active modes of interaction, for example speech, handwriting and direct manipulation.The emergence of novel pervasive computing applications, which combine active interaction modes with passive modality channels raises new challenges for the handling of uncertainty and errors.For example, context-aware pervasive systems can sense and incorporate data about lighting, noise level, location, time, people other than the user, as well as many other pieces of information to adjust their model of the user's environment.In affective computing, sensors that can capture data about the user's physical state or behaviour, are used to gather cues which can help the system perceive the user's emotions (Kapoor & Picard, 2005;Pantic, 2005).In the absence of recognition or perception error, more robust interaction is then obtained by fusing explicit user inputs (the active modes) and implicit contextual information (the passive modes).However, in the presence of errors, the invisibility of the devices that make up the pervasive environment and the general lack of user's awareness of the devices and collected data properties render error handling very difficult, if not impossible.Despite recent advances in computer vision techniques and multi-sensor systems, designing and implementing successful multimodal and ubiquitous computing applications remain difficult.This is mainly because our lack of understanding of how these technologies can be best used and combined in the user interface often leads to interface designs with poor usability and low robustness.Moreover, even in more traditional multimodal interfaces (such as speech and pen interfaces) technical issues remain.Speech recognition systems, for example, are still error-prone.Their accuracy and robustness depends on the size of the application's vocabulary, the quality of the audio signal and the variability of the voice parameters.Signal and noise separation also remains a major challenge in speech recognition technology.Recognition-based multimodal interaction is thus still error prone, but in pervasive computing applications, where the capture and the analysis of passive modes are key, the possibilities of errors and misinterpretations are even greater.Furthermore, in pervasive www.intechopen.comUbiquitous Computing 50 computing applications, the computing devices have become invisible and the users may not be aware of their behaviour that is captured by the system.They may also have a wrong understanding of what data is captured by the various devices, and how it is used.In most cases, they do not receive any feedback about the system's status and beliefs.As a result, many traditional methods of multimodal error correction become ill adapted to pervasive computing applications.When faced with errors, users encounter a number of new challenges: understanding the computer's responses or change of behaviour; analysing the cause of the system's changed behaviour; and devising ways to correct the system's wrong beliefs.This chapter addresses these problems.It exposes the new challenges raised by novel pervasive computing applications for the handling of uncertainty and errors, and it discusses the inadequacies of known multimodal error handling strategies for this type of applications.It is organised as follows.In the next section, we explain our usage of the words multimodal and pervasive computing and we propose our own definitions, which are based on the notions of active and passive modes of interaction.We also describe a number of pervasive computing applications, which will serve in the remainder of the chapter to illustrate the new challenges raised by this type of applications.In section 3 of the chapter, we briefly review the various recognition error handling strategies that can be found in the multimodal interaction literature, then in section 4, we show that many of these multimodal error handling strategies, where active modes only are used, are ill adapted to pervasive computing applications.We also discuss the new challenges arising from the deployment of novel pervasive computing applications for error correction.In section 5, we suggest that promoting users' correct mental models of the devices and data properties that make up a pervasive computing environment can render error handling more effective.Section 6, finally, concludes the chapter.