On a deductive reasoning model and method for uncertainty

Suzuki, Makoto; Matsushima, Tatsuya; Hirasawa, Shoichi

doi:10.1109/tai.1999.809781

Cited by 4 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inductive frameworks such as machine learning have the potential to learn and generalize by example (Delgado et al, 2009), (Ros et al, 2011), while deductive methods can provide powerful means to encode semantic process knowledge (Suzuki et al, 1999). The proposed hierarchal approach leverages both, as the lower task recognition tier is based on an inductive framework, while the higher tier ADL recognition is based on a deductive framework.…”

Section: Related Workmentioning

confidence: 99%

Activities of daily life recognition using process representation modelling to support intention analysis

Naeem

Bashroush

Anthony

et al. 2015

International Journal of Pervasive Computing and Communications

View full text Add to dashboard Cite

Activity of Daily Life (ADL) recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer's disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. Current work has generally focused on applying a range of traditional classification and semantic reasoning based techniques in order to recognise ADLs. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. In this paper, we present an ADL recognition approach, which uses a hierarchal structure for the representation and modelling of the activities, its associated tasks and their relationships. We describe an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer's patients. A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Activities of daily life recognition using process representation modelling to support intention analysis

Naeem

Bashroush

Anthony

et al. 2015

International Journal of Pervasive Computing and Communications

View full text Add to dashboard Cite

show abstract

“…Inductive frameworks such as machine learning have the potential to learn and generalize by example [22][23] while deductive methods can provide powerful means to encode semantic process knowledge [24]. Both frameworks have their benefits and limitations and the ultimate solution would be the one bringing the best of both worlds.…”

Section: Related Workmentioning

confidence: 99%

Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities Within the Home

Naeem

Tawil

Semelis

et al. 2017

Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016

View full text Add to dashboard Cite

Abstract-One of the key objectives of an ambient assisted living environment is to enable elderly people to lead a healthy and independent life. These assisted environments have the capability to capture and infer activities performed by individuals, which can be useful for providing assistance and tracking functional decline among the elderly community. This paper presents an activity recognition engine based on a hierarchal structure, which allows modelling, representation and recognition of ADLs, their associated tasks, objects, relationships and dependencies. The structure of this contextual information plays a vital role in conducting accurate ADL recognition. The recognition performance of the inference engine has been validated with a series of experiments based on object usage data collected within the home environment.

show abstract

“…Activity recognition frameworks can be divided into two main categories, inductive and deductive. Inductive frameworks such as machine learning have the potential to learn and generalize by example [16] [17] while deductive methods can provide powerful means to encode semantic process knowledge [18]. Both frameworks have their benefits and limitations and the ultimate solution would be the one bringing the best of both worlds.…”

Section: Related Workmentioning

confidence: 99%

Inference of Hygiene Behaviours While Recognising Activities of Daily Living

Naeem¹,

Tawil²,

Semelis³

et al. 2015

Proceedings of the 3rd International Conference on Context-Aware Systems and Applications

View full text Add to dashboard Cite

Many health problems are generally caused by unhealthy behaviours that occur whilst conducting everyday Activities of Daily Living (ADL), such as poor use of sanitation and hygiene. This paper describes the development of an ADL inference engine, which is able to recognise natural hygiene behaviour patterns. As opposed to traditional ADL classification approaches, the developed inference engine employs a novel hierarchal structure for the modelling, representation and recognition of the ADLs, its associated tasks, objects, dependencies and their relationships. The organisation of this information in a contextual structure plays a key role in carrying out robust ADL recognition for the detection of hygiene behaviours. The proposed work also marks a shift in feature detection methodology, as it allows actual behaviour to be studied in its natural environment within actual households, with at least two individuals per household as opposed to a laboratory based controlled setting. This paper also presents experimental results that validate the performance of the inference engine.

show abstract

On a deductive reasoning model and method for uncertainty

Cited by 4 publications

References 5 publications

Activities of daily life recognition using process representation modelling to support intention analysis

Activities of daily life recognition using process representation modelling to support intention analysis

Inference Engine Based on a Hierarchical Structure for Detecting Everyday Activities Within the Home

Inference of Hygiene Behaviours While Recognising Activities of Daily Living

Contact Info

Product

Resources

About