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Sensors 2016, 16(1), 72;

Recognition of Activities of Daily Living with Egocentric Vision: A Review

Faculty of Science, Engineering and Computing, Kingston University, Kingston upon Thames, London KT1 2EE, UK
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 4 November 2015 / Revised: 18 December 2015 / Accepted: 26 December 2015 / Published: 7 January 2016
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [17045 KB, uploaded 8 January 2016]   |  


Video-based recognition of activities of daily living (ADLs) is being used in ambient assisted living systems in order to support the independent living of older people. However, current systems based on cameras located in the environment present a number of problems, such as occlusions and a limited field of view. Recently, wearable cameras have begun to be exploited. This paper presents a review of the state of the art of egocentric vision systems for the recognition of ADLs following a hierarchical structure: motion, action and activity levels, where each level provides higher semantic information and involves a longer time frame. The current egocentric vision literature suggests that ADLs recognition is mainly driven by the objects present in the scene, especially those associated with specific tasks. However, although object-based approaches have proven popular, object recognition remains a challenge due to the intra-class variations found in unconstrained scenarios. As a consequence, the performance of current systems is far from satisfactory. View Full-Text
Keywords: egocentric vision; wearable cameras; ambient assisted living; activity recognition egocentric vision; wearable cameras; ambient assisted living; activity recognition

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Nguyen, T.-H.-C.; Nebel, J.-C.; Florez-Revuelta, F. Recognition of Activities of Daily Living with Egocentric Vision: A Review. Sensors 2016, 16, 72.

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