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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Data Work: How Energy Advisors and Clients Make IoT Data Accountable.

Joel E Fischer¹, Andy Crabtree¹, James A Colley¹

¹1Mixed Reality Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK.

Computer Supported Cooperative Work : CSCW : an International Journal

|February 7, 2020

Summary

This study examines an interactive sensing system used by UK energy advisors for low-income households. It details how advisors and clients collaborate with data, highlighting the costs and needs for integrating the Internet of Things (IoT) into advisory work.

Keywords:

CSCW Collaborative work Ethnomethodology Internet of things

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Published on: January 16, 2020

Area of Science:

Human-Computer Interaction
Sociology of Technology
Energy Policy

Background:

Energy advisors provide crucial face-to-face advice to low-income households in the UK.
Interactive sensing systems can potentially enhance the effectiveness of energy advisory services.
The integration of technology into social work requires understanding collaborative data practices.

Purpose of the Study:

To investigate the deployment and use of an interactive sensing system by energy advisors.
To analyze how energy advisors and clients interact with and utilize system-generated data.
To understand the collaborative 'data work' involved in using IoT systems in advisory contexts.

Main Methods:

Fieldwork deployment of an interactive sensing system.
Observational studies of energy advisor-client interactions.
Qualitative analysis of collaborative data work and system appropriation.

Main Results:

The interactive sensing system was appropriated into the daily work of energy advisors.
Collaborative data work, including anticipating, rehearsing, and performing data tasks, was observed between advisors and clients.
Significant 'overhead costs' were identified in building collaborative action into sensing systems.

Conclusions:

Integrating the Internet of Things (IoT) into collaborative advisory work necessitates support for discrete workflows and accountability systems.
The study elaborates on social and collaborative methods of data work crucial for designing and studying collaborative IoT systems.
Understanding the practicalities of data work is essential for the successful implementation of IoT in social and support services.