Providing effective memory retrieval cues through automatic structuring and augmentation of a lifelog of images
Year: 2008
Type: dissertation
Abstract: Almost everything we do these days is in some way monitored or logged by computers. We've come to accept - or maybe just ignore – this massive surveillance because it brings us benefits. In the past we have been easily able to monitor activities on our PC's (e.g. documents we worked\non, e-mails received, etc.), however recently with the advent of numerous types of wearable sensors we have been able to log many more aspects of our activities. While we have the technology to log these activities, we're not really sure what applications we can develop to usefully use this data. One application of lifelogging is as a memory aid to sufferers of neurodegenerative diseases as Alzheimer’s. Indeed in studies on the human mind it has been shown that images are strongly encoded in the brain, and in this seminar we talk about our work in using visual lifelog images to provide effective memory cues. To passively capture these lifelog images we use a wearable camera called the SenseCam. This device captures an average of 2,000 images per day, which equates to over 600,000 images per year! This is too much information for any individual to sift through, therefore we firstly exploit various aspects of the human memory system to break\nthis data down into manageable chunks or events. Finally in order to provide additional memory cues to an individual (on say a tourist trip), we automatically augment their own lifelog images with images taken from web 2.0 sites such as Flickr. Through analysing the tags of geotagged images on Flickr, we are also automatically able to provide additional augmented material from other sites such as the Yahoo search engine for example. The talk will conclude by talking about future directions that lifelogging may take and the data sources that may drive these changes.
Author: Aiden Doherty
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2173602695