Authors:
Daniele Liciotti
1
;
Emanuele Frontoni
1
;
Primo Zingaretti
1
;
Nicola Bellotto
2
and
Tom Duckett
2
Affiliations:
1
Università Politecnica delle Marche, Italy
;
2
University of Lincoln, United Kingdom
Keyword(s):
ADLs, Human Activity Recognition, HMMs.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Image and Video Analysis
;
Learning of Action Patterns
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Software Engineering
;
Video Analysis
Abstract:
Automated recognition of Activities of Daily Living allows to identify possible health problems and apply corrective strategies in Ambient Assisted Living (AAL). Activities of Daily Living analysis can provide very useful information for elder care and long-term care services. This paper presents an automated RGB-D video analysis system that recognises human ADLs activities, related to classical daily actions. The main goal is to predict the probability of an analysed subject action. Thus, abnormal behaviour can be detected. The activity detection and recognition is performed using an affordable RGB-D camera. Human activities, despite their unstructured nature, tend to have a natural hierarchical structure; for instance, generally making a coffee involves a three-step process of turning on the coffee machine, putting sugar in cup and opening the fridge for milk. Action sequence recognition is then handled using a discriminative Hidden Markov Model (HMM). RADiaL, a dataset with RGB-D
images and 3D position of each person for training as well as evaluating the HMM, has been built and made publicly available.
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