Mobile-Based Experience Sampling for Behaviour Research
August 15, 2015 Β· Declared Dead Β· π Emotions and Personality in Personalized Services
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Veljko Pejovic, Neal Lathia, Cecilia Mascolo, Mirco Musolesi
arXiv ID
1508.03725
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY
Citations
64
Venue
Emotions and Personality in Personalized Services
Last Checked
3 months ago
Abstract
The Experience Sampling Method (ESM) introduces in-situ sampling of human behaviour, and provides researchers and behavioural therapists with ecologically valid and timely assessments of a person's psychological state. This, in turn, opens up new opportunities for understanding behaviour at a scale and granularity that was not possible just a few years ago. The practical applications are many, such as the delivery of personalised and agile behaviour interventions. Mobile computing devices represent a revolutionary platform for improving ESM. They are an inseparable part of our daily lives, context-aware, and can interact with people at suitable moments. Furthermore, these devices are equipped with sensors, and can thus take part of the reporting burden off the participant, and collect data automatically. The goal of this survey is to discuss recent advancements in using mobile technologies for ESM (mESM), and present our vision of the future of mobile experience sampling.
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