Algorithms in Ambient Intelligence

Aarts, Ehl Emile; Korst, Jan; Verhaegh, Wfj Wim

doi:10.1007/3-540-27139-2_16

Cited by 11 publications

(18 citation statements)

References 21 publications

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“…19 Weiser refers to Heidegger and Polyani here. 20 Examples of profiling technologies used in Ambient Intelligence are recommender systems based on both collaborative filtering (webbased group profiling) and personal filtering (individual user profiling) (Aarts et al 2005). 21 There are many different kinds of profiling practices: credit scoring, fraud prevention, customer and consumer profiling, profiling of employees, profiling of web users, profiling for attention support in education, location based services (mobile marketing), behavioral and biometric profiling (Hildebrandt and Gutwirth 2008). this way whole segments of the population are withheld these important material resources without having awareness or access to information about the grounds of exclusion.…”

Section: Persons In Intelligent Environmentsmentioning

confidence: 99%

Property, privacy and personhood in a world of ambient intelligence

Dijk

2009

Ethics Inf Technol

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Profiling technologies are the facilitating force behind the vision of Ambient Intelligence in which everyday devices are connected and embedded with all kinds of smart characteristics enabling them to take decisions in order to serve our preferences without us being aware of it. These technological practices have considerable impact on the process by which our personhood takes shape and pose threats like discrimination and normalisation. The legal response to these developments should move away from a focus on entitlements to personal data, towards making transparent and controlling the profiling process by which knowledge is produced from these data. The tendency in intellectual property law to commodify information embedded in software and profiles could counteract this shift to transparency and control. These rights obstruct the access and contestation of the design of the code that impacts one's personhood. This triggers a political discussion about the public nature of this code and forces us to rethink the relations between property, privacy and personhood in the digital age.

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Section: Persons In Intelligent Environmentsmentioning

confidence: 99%

Property, privacy and personhood in a world of ambient intelligence

Dijk

2009

Ethics Inf Technol

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“…Table 2. ese 34 extracted features are those that are commonly used in audio analysis and activity recognition activities through audio [38][39][40][41], especially the mel-frequency spectral coefficients, since many audio analysis works have been developed that make use of them [21,[42][43][44].…”

Section: Data Preprocessingmentioning

confidence: 99%

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

García-Domínguez

Galván-Tejada

Zanella-Calzada

et al. 2020

Mobile Information Systems

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In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.

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“…As illustrated in Figure 1, we structure "Kikubari" with five properties extended from a framework for service provisioning in Ambient Intelligence (AmI) [1], and define services as tasks for service provisioning. We redefine these properties as follows:…”

Section: A Model Of "Kikubari"mentioning

confidence: 99%

Kikubari: A model for provisioning dynamic context-aware services based on the intentions

Maki

Aoyama

2012

The 1st IEEE Global Conference on Consumer Electronics 2012

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Due to the ubiquity of mobile computing, the service provisioning systems are required to provide appropriate services dynamically under the changing context. Based on a model of Kikubari, traditional Japanese manner of hospitality with delicate attention to other's intentions, we propose a context-aware service provisioning system for coordinating multiple users' intentions along with changing context. We evaluate the proposed method with multiple scenarios for the users of a car navigation system and demonstrate the effectiveness of proposed method.

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Algorithms in Ambient Intelligence

Cited by 11 publications

References 21 publications

Property, privacy and personhood in a world of ambient intelligence

Property, privacy and personhood in a world of ambient intelligence

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

Kikubari: A model for provisioning dynamic context-aware services based on the intentions

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