Finding the Inner Clock: A Chronobiology-based Calendar
April 14, 2020 Β· Declared Dead Β· π CHI Extended Abstracts
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sarah Janboecke, Alina Gawlitta, Judith Doerrenbaecher, Marc Hassenzahl
arXiv ID
2004.06594
Category
cs.HC: Human-Computer Interaction
Citations
10
Venue
CHI Extended Abstracts
Last Checked
4 months ago
Abstract
Time and its lack of play a central role in our everyday lives. Despite increasing productivity, many people experience time stress, exhaustion and a longing for time affluence, and at the same time, a fear of not being busy enough. All this leads to a neglect of natural time, especially the patterns and rhythms created by physiological processes, subsumed under the heading of chronobiology. The present paper presents and evaluates a calendar application, which uses chronobiological knowledge to support people s planning activities. Participants found our calendar to be interesting and engaging. It especially made them think more about their bodies and appropriate times for particular activities. All in all, it supported participants in negotiating. external demands and personal health and wellbeing. This shows that technology does not necessarily has to be neutral or even further current (mal-)practices. Our calendar cares about changing perspectives and thus about enhancing users wellbeing.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted