These notes are a summary of concepts presented in “A Conceptual framework for Adaptive User Interfaces for older adults.”
Machado, Eduardo & Singh, Deepika & Cruciani, Federico & Chen, Liming & Hanke, Sten & Salvago, Fernando & Kropf, Johannes & Holzinger, Andreas. (2018). A Conceptual framework for Adaptive User Interfaces for older adults. 782-787. 10.1109/PERCOMW.2018.8480407.
- The Relationships Between Social Interaction and Intelligent Systems
- Integration of intelligent systems to improve daily quality of life
- Promotion of new social interaction forms
- Digital Assistive Technologies for Older Adults
- Scaling assistive technologies
- Data-driven modification of interface layouts based on user goals and needs
- Scaling assistive technologies
- Cognitive Load and Measurement Techniques
- Types of Cognitive Load
- Extraneous
- Intrinsic
- Measurement techniques:
- Subjective: Self-reported assessments
- Dual-task: Performance on primary and secondary tasks
- Physiological: Includes muscle tension, pupil dilation, heart rate, blood pressure, and neuronal activity
- Eyetracking as a tool for pupil data collection
- Types of Cognitive Load
- Intelligent Systems and Task-Evoked Responses
- Using task-evoked pupillary responses as a feedback loop to interactions
- Exploration of other physiological responses to interactions with intelligent systems
- Peripheral Devices for Interaction and Data Collection
- Integration of peripherals such as:
- Eyetracking systems
- Intelligent computer mouse with sensors skin conductance, pulse, and pressure
- Customizing interaction mechanisms based on user preferences and skills
- Integration of peripherals such as:
- Localized Data Collection and Storage
- Collecting data locally on user devices for context-specific processing
- Utilizing barcodes and RFID for system decision-making
- Longitudinal Data Collection and User Profiles
- Creation of dynamic user profiles through continuous data gathering
- Challenges in measuring face-to-screen distance for effective data collection
- User Profile Modeling
- Building user profiles based on dynamic data repositories
- Enabling adaptive, personalized responses by intelligent systems