Title :
Activity recognition using a hierarchical framework
Author :
Naeem, Usman ; Bigham, John
Author_Institution :
Dept. of Electron. Eng., Queen Mary Univ. of London, London
fDate :
Jan. 30 2008-Feb. 1 2008
Abstract :
This paper describes an approach for modelling and detecting activities of daily life based on a hierarchy of plans that contain a range of precedence relationships, representations of concurrency and other temporal relationships. Identification of activities of daily life is achieved by episode recovery models supported by using relationships expressed in the plans. The motivation is to allow people with Alzheimerpsilas disease to have additional years of independent living before the Alzheimerpsilas disease reaches the moderate and severe stages.
Keywords :
patient care; sensor fusion; Alzheimer´s disease; activity recognition; concurrency representations; daily life activities; episode recovery models; hierarchical framework; precedence relationships; temporal relationships; Alzheimer´s disease; Computer vision; Concurrent computing; Feature extraction; Hidden Markov models; Ontologies; Radiofrequency identification; Sensor phenomena and characterization; Sensor systems; Wearable sensors; Activities of Daily Life; Alzheimer’s Disease; Episode Recovery; Task Identification;
Conference_Titel :
Pervasive Computing Technologies for Healthcare, 2008. PervasiveHealth 2008. Second International Conference on
Conference_Location :
Tampere
Print_ISBN :
978-963-9799-15-8
DOI :
10.1109/PCTHEALTH.2008.4571018
