Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Continuous lifelong capture of personal experience with EyeTap
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Validating the Detection of Everyday Concepts in Visual Lifelogs
SAMT '08 Proceedings of the 3rd International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
SenseCam Image Localisation Using Hierarchical SURF Trees
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
Multimodal segmentation of lifelog data
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
SenseCam: a retrospective memory aid
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Handbook of Ambient Assisted Living: Technology for Healthcare, Rehabilitation and Well-being - Volume 11 of Ambient Intelligence and Smart Environments
Evaluation of an inexpensive depth camera for in-home gait assessment
Journal of Ambient Intelligence and Smart Environments
Personal informatics & context: Using context to reveal factors that affect behavior
Journal of Ambient Intelligence and Smart Environments
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Falls are a major cause of injury and fatality among the older population and the use of wearable sensors to quantify key metrics relating to gait and motor function in order to evaluate potential falls risk are increasing in use. However, one of the problems with current use of quantitative gait data to evaluate falls risk is that it is still tied to a relatively controlled deployment model due to lack of effective methods to label and segment gait data that could be acquired over a long term basis in the uncontrolled home and community setting. This means that we cannot evaluate the potentially powerful impact of environmental factors on gait and motor function in the home and community. In this paper we present a conceptual approach to solving this problem by combining inertial sensing methods for quantitative gait analysis with life logging, using other wearable sensors and a wearable camera that automatically record a wearer's contexts, at all times. Using the system, a clinician can use both gait data and lifelog data to mutually index each other to enable a more thorough exploration of data and a greater understanding of the impact of environment on gait function and subsequent falls risk. We also present a single case study of a long term deployment using a prototype of the system, with feedback from an experienced clinician, to illustrate its potential clinical utility.