Extended Body-Angles Algorithm to recognize activities within intelligent environments

Franca, Carlos Gutiérrez López de la; Hervás, Ramón; Johnson, Esperanza; Mondéjar, Tania; Bravo, José

doi:10.1007/s12652-017-0463-y

Cited by 8 publications

(13 citation statements)

References 14 publications

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“…We would also like to include a wider array of tools to help improve the interactive capabilities of our proposal, such as EEG headset, or include overall movement of the body [22].…”

Section: Discussionmentioning

confidence: 99%

Analyzing and Predicting Empathy in Neurotypical and Nonneurotypical Users with an Affective Avatar

Johnson

Hervás

Gutiérrez-López-Franca

et al. 2017

Mobile Information Systems

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In recent times, diagnosing and treating different health issues have improved greatly with the help of technology, with an example being cognitive health issues. Despite this, there is still a difference between how the technology is working towards it and the actual potential that can be achieved. In this paper, we propose a mobile application with an affective avatar, encompassed in the area of serious games, which will obtain information related to the interactions performed by the users. There are a total of 50 users, of neurotypical and nonneurotypical backgrounds, with the latter being people with Down syndrome and intellectual disability. Based on collected data from the different users interacting with the avatar in a mobile device, we analyzed the results to obtain a ground truth about prototypic empathic interactions and feed those interactions to a learning algorithm to support the diagnosis process and therapy treatment of empathy and socialization issues.

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“…We would also like to include a wider array of tools to help improve the interactive capabilities of our proposal, such as EEG headset, or include overall movement of the body [22].…”

Section: Discussionmentioning

confidence: 99%

Analyzing and Predicting Empathy in Neurotypical and Nonneurotypical Users with an Affective Avatar

Johnson

Hervás

Gutiérrez-López-Franca

et al. 2017

Mobile Information Systems

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“…A very important process at the core of smart environments is the sensor-based activity recognition [1][2][3]. This kind of activity recognition is based on recognizing the actions of one or more persons within an intelligent environment by using a flow of observed events that depend only on the current activity.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Salguero

Espinilla

2018

Computers & Electrical Engineering

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In recent years, many techniques have been proposed for automatic recognition of Activities of Daily Living from smart home sensor data. However, classifiers usually use features created ad hoc. In this work, the use of ontologies is proposed for the fully automatic generation of these features. The process consists of converting the original dataset into an ontology and then combine all the concepts and relations in that ontology to obtain relevant class expressions. The high formalization of ontologies allows us to reduce the search space by discarding many meaningless expressions, such as contradictory or unsatisfiable expressions. The relevant class expressions are then used as features by the classifiers to build the classification model. To validate our proposal, we have used as reference the results obtained by four different classification algorithms that use the most commonly used features.

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“…This kind of activity recognition is based on identifying the actions of one or more people within an intelligent environment, by using a stream of observed sensor events that depend only on the current activity Alemdar and Ersoy 2017). Common activities of interest are ADL such as "bathing","sleeping" or "dinning", for instance (Ferrández-Pastor et al 2017;Shewell et al 2017;Gutiérrez López de la Franca et al 2017). Usually, objects or furniture can generate sensor events indicating, for example, the use of a faucet, the opening of a door, or the use of a light switch (Korhonen et al 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Usually, objects or furniture can generate sensor events indicating, for example, the use of a faucet, the opening of a door, or the use of a light switch (Korhonen et al 2003). We can even use much more complex sensors which give us information such as the posture of the people performing the activities (Gutiérrez López de la Franca et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Methodology for improving classification accuracy using ontologies: application in the recognition of activities of daily living

Salguero

Medina

Delatorre

et al. 2018

J Ambient Intell Human Comput

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Feature construction and selection are two key factors in the field of machine learning (ML). Usually, these are very timeconsuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. In this paper, we propose a methodology that makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base, expressed in form of ontology. The proposed methodology has been evaluated with three different activities of a popular dataset, showing its effectiveness in the recognition of activities of daily living (ADL). The obtained successful results indicate that the use of extended feature vectors provided by the use of ontologies offers a better accuracy, regarding the original feature vectors of the classic approach, where each feature corresponds to the activation of a sensor. Although the classic approach produces classifiers with accuracies above 92%, the proposed methodology improves those results by 1.9%, on average, without adding more information to the dataset.

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Extended Body-Angles Algorithm to recognize activities within intelligent environments

Cited by 8 publications

References 14 publications

Analyzing and Predicting Empathy in Neurotypical and Nonneurotypical Users with an Affective Avatar

Analyzing and Predicting Empathy in Neurotypical and Nonneurotypical Users with an Affective Avatar

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Methodology for improving classification accuracy using ontologies: application in the recognition of activities of daily living

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