TU Darmstadt / ULB / TUbiblio

Getting the Residents' Attention: The Perception of Warning Channels in Smart Home Warning Systems

Haesler, Steffen ; Wendelborn, Marc ; Reuter, Christian (2023)
Getting the Residents' Attention: The Perception of Warning Channels in Smart Home Warning Systems.
2023 Designing Interactive Systems Conference. Pittsburgh, USA (10.-14.07.2023)
doi: 10.1145/3563657.3596076
Conference or Workshop Item, Bibliographie

Abstract

About half a billion households are expected to use smart home systems by 2025. Although many IoT sensors, such as smoke detectors or security cameras, are available and governmental crisis warning systems are in place, little is known about how to warn appropriately in smart home environments. We created a Raspberry Pi based prototype with a speaker, a display, and a connected smart light bulb. Together with a focus group, we developed a taxonomy for warning messages in smart home environments, dividing them into five classes with different stimuli. We evaluated the taxonomy using the Experience Sampling Method (ESM) in a field study at participants' (N = 13) homes testing 331 warnings. The results show that taxonomy-based warning stimuli are perceived to be appropriate and participants could imagine using such a warning system. We propose a deeper integration of warning capabilities into smart home environments to enhance the safety of citizens.

Item Type: Conference or Workshop Item
Erschienen: 2023
Creators: Haesler, Steffen ; Wendelborn, Marc ; Reuter, Christian
Type of entry: Bibliographie
Title: Getting the Residents' Attention: The Perception of Warning Channels in Smart Home Warning Systems
Language: English
Date: 10 July 2023
Publisher: ACM
Book Title: DIS'23: Proceedings of the 2023 ACM Designing Interactive Systems Conference
Event Title: 2023 Designing Interactive Systems Conference
Event Location: Pittsburgh, USA
Event Dates: 10.-14.07.2023
DOI: 10.1145/3563657.3596076
Abstract:

About half a billion households are expected to use smart home systems by 2025. Although many IoT sensors, such as smoke detectors or security cameras, are available and governmental crisis warning systems are in place, little is known about how to warn appropriately in smart home environments. We created a Raspberry Pi based prototype with a speaker, a display, and a connected smart light bulb. Together with a focus group, we developed a taxonomy for warning messages in smart home environments, dividing them into five classes with different stimuli. We evaluated the taxonomy using the Experience Sampling Method (ESM) in a field study at participants' (N = 13) homes testing 331 warnings. The results show that taxonomy-based warning stimuli are perceived to be appropriate and participants could imagine using such a warning system. We propose a deeper integration of warning capabilities into smart home environments to enhance the safety of citizens.

Uncontrolled Keywords: smart home warning system, public warning, crisis informatics, taxonomy, user perceptions, emergenCITY_INF, emergenCITY_SG, emergenCITY
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
Zentrale Einrichtungen
Zentrale Einrichtungen > Arbeitsgruppe Interdisziplinäre "Stadtforschung"
Date Deposited: 26 Oct 2023 07:44
Last Modified: 13 May 2024 17:00
PPN: 513018131
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details