Tag Archives: sensors

Adapting the Interactive Activation Model for Context Recognition and Identification

Maya Sappelli, Suzan Verberne, and Wessel Kraaij. 2016. Adapting the Interactive Activation Model for Context Recognition and Identification. ACM Trans. Interact. Intell. Syst. 6, 3, Article 22 (September 2016), 30 pages. DOI: http://dx.doi.org/10.1145/2873067


In this article, we propose and implement a new model for context recognition and identification. Our work is motivated by the importance of “working in context” for knowledge workers to stay focused and productive.

A computer application that can identify the current context in which the knowledge worker is working can (among other things) provide the worker with contextual support, for example, by suggesting relevant information sources, or give an overview of how he or she spent his or her time during the day.

We present a descriptive model for the context of a knowledge worker. This model describes the contextual elements in the work environment of the knowledge worker and how these elements relate to each other. This model is operationalized in an algorithm, the contextual interactive activation model (CIA), which is based on the interactive activation model by Rumelhart and McClelland. It consists of a layered connected network through which activation flows. We have tested CIA in a context identification setting. In this case, the data that we use as input is low-level computer interaction logging data.

We found that topical information and entities were the most relevant types of information for context identification. Overall the proposed CIA model is more effective than traditional supervised methods in identifying the active context from sparse input data, with less labelled training data.

Deriving Requirements for Pervasive Well-Being Technology From Work Stress and Intervention Theory: Framework and Case Study

Deriving Requirements for Pervasive Well-Being Technology From Work Stress and Intervention Theory: Framework and Case Study

1Radboud University, Institute for Computing and Information Sciences, Nijmegen, Netherlands

2TNO Netherlands Organisation for Applied Scientific Research, The Hague, Netherlands

3Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands

4Delft University of Technology, Interactive Intelligence, Delft, Netherlands


Background: Stress in office environments is a big concern, often leading to burn-out. New technologies are emerging, such as easily available sensors, contextual reasoning, and electronic coaching (e-coaching) apps. In the Smart Reasoning for Well-being at Home and at Work (SWELL) project, we explore the potential of using such new pervasive technologies to provide support for the self-management of well-being, with a focus on individuals’ stress-coping. Ideally, these new pervasive systems should be grounded in existing work stress and intervention theory. However, there is a large diversity of theories and they hardly provide explicit directions for technology design.

Objective: The aim of this paper is to present a comprehensive and concise framework that can be used to design pervasive technologies that support knowledge workers to decrease stress.

Methods: Based on a literature study we identify concepts relevant to well-being at work and select different work stress models to find causes of work stress that can be addressed. From a technical perspective, we then describe how sensors can be used to infer stress and the context in which it appears, and use intervention theory to further specify interventions that can be provided by means of pervasive technology.

Results: The resulting general framework relates several relevant theories: we relate “engagement and burn-out” to “stress”, and describe how relevant aspects can be quantified by means of sensors. We also outline underlying causes of work stress and how these can be addressed with interventions, in particular utilizing new technologies integrating behavioral change theory. Based upon this framework we were able to derive requirements for our case study, the pervasive SWELL system, and we implemented two prototypes. Small-scale user studies proved the value of the derived technology-supported interventions.

Conclusions: The presented framework can be used to systematically develop theory-based technology-supported interventions to address work stress. In the area of pervasive systems for well-being, we identified the following six key research challenges and opportunities: (1) performing multi-disciplinary research, (2) interpreting personal sensor data, (3) relating measurable aspects to burn-out, (4) combining strengths of human and technology, (5) privacy, and (6) ethics.

JMIR Mhealth Uhealth 2016;4(3):e79