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Intelligent Surveillance and Smart Home

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The smart home field has been studied intensively in the last decade, but recent developments in the field of ICT (e.g. wearable devices, wireless local area network, artificial intelligence techniques, human machine interfaces) have enabled the implementation of new services in the home environment such as monitoring the health status of frail people, energy management, advice and support for rehabilitation, home monitoring of disabled people. Advanced artificial intelligence techniques enable the implementation of wearable devices offering a high decision-making capacity to operate actuators or trigger alerts or to choose the right intervention solution without users’ intervention. Numerous emerging applications become possible based on data fusion and processing algorithms and self-adaptive systems. Indeed, the mass of heterogeneous data are very large and require new approaches to make quick and local decisions with low power consumption. Therefore, innovative and flexible hardware and software architectures are expected, as well as adapted and easily usable user interfaces.

While this Special Issue invites topics broadly across the tools, methodology, infrastructure, devices, and use cases in real conditions, the topics of interest include but are not limited to:

Wearable electronic device design for health monitoring;
People’s daily activities data learning;
Human activity recognition;
Assisted living solutions, assistive technologies;
Living labs for health and autonomy;
Self-configurable networks for smart homes;
Smart homes/home networks/residential gateways;
Adaptive interaction for home control by users;
Processing and data fusion algorithms based on artificial intelligence;
Smart dust and buried sensors for home environments;
Smart control for energy management;
Heterogeneous data learning;
Internet of Things for smart environments and e-Health;
Middleware support for smart home and health telematic services;
Context awareness/autonomous computing;
Human–machine interface/ambient intelligence;
Epidemic disease prediction systems;
Human factors in autonomous smart systems.

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17 pages, 1011 KiB

Open AccessArticle

OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes

by Hubert Kenfack Ngankam, Hélène Pigot and Sylvain Giroux

Electronics 2022, 11(7), 1143; https://doi.org/10.3390/electronics11071143 - 5 Apr 2022

Cited by 16 | Viewed by 3384

Abstract

Ambient assisted living (AAL) makes it possible to build assistance for older adults according to the person’s context. Understanding the person’s context sometimes involves transforming one’s home into a smart home. Typically, this is carried out using nonintrusively distributed sensors and calm technologies. Older adults often have difficulty performing activities of daily living, such as taking medication, drinking coffee, watching television, using certain electronic devices, and dressing. This difficulty is even greater when these older adults suffer from cognitive impairments. Defining an assistance solution requires a multidisciplinary and iterative collaborative approach. It is necessary, therefore, to reason about the imperatives and solutions of this multidisciplinary collaboration (e.g., clinical), as well as the adaptation of technical constraints (e.g., technologies). A common approach to reasoning is to represent knowledge using logic-based formalisms, such as ontologies. However, there is not yet an established ontology that defines concepts such as multidisciplinary collaboration in successive stages of the assistance process. This article presents OntoDomus, an ontology that describes, at several levels, the semantic interactions between ambient assisted living, context awareness, smart home, and Internet of Things, based on multidisciplinarity. It revolves around two main notions: multidisciplinarity, based on specific sub-ontologies and the ambient feedback loop. OntoDomus combines SPARQL queries and OWL 2 models to improve the reusability of domain terminology, allowing stakeholders to represent their knowledge in different collaborative and adaptive situations. The ontological model is validated, first by its reuse in more specific works—specific to an aspect of ambient assistance. Second, it is validated by the structuring of ambient knowledge and inferences of the formalization in a case study that includes instances for a particular activity of daily living. It places the ambient feedback loop at the center of the ontology by focusing on highly expressive domain ontology formalisms with a low level of expressiveness between them. Full article

(This article belongs to the Special Issue Intelligent Surveillance and Smart Home)

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24 pages, 3976 KiB

Open AccessArticle

Using Language Model to Bootstrap Human Activity Recognition Ambient Sensors Based in Smart Homes

by Damien Bouchabou, Sao Mai Nguyen, Christophe Lohr, Benoit LeDuc and Ioannis Kanellos

Electronics 2021, 10(20), 2498; https://doi.org/10.3390/electronics10202498 - 14 Oct 2021

Cited by 26 | Viewed by 3451

Abstract

Long Short Term Memory (LSTM)-based structures have demonstrated their efficiency for daily living recognition activities in smart homes by capturing the order of sensor activations and their temporal dependencies. Nevertheless, they still fail in dealing with the semantics and the context of the sensors. More than isolated id and their ordered activation values, sensors also carry meaning. Indeed, their nature and type of activation can translate various activities. Their logs are correlated with each other, creating a global context. We propose to use and compare two Natural Language Processing embedding methods to enhance LSTM-based structures in activity-sequences classification tasks: Word2Vec, a static semantic embedding, and ELMo, a contextualized embedding. Results, on real smart homes datasets, indicate that this approach provides useful information, such as a sensor organization map, and makes less confusion between daily activity classes. It helps to better perform on datasets with competing activities of other residents or pets. Our tests show also that the embeddings can be pretrained on different datasets than the target one, enabling transfer learning. We thus demonstrate that taking into account the context of the sensors and their semantics increases the classification performances and enables transfer learning. Full article

(This article belongs to the Special Issue Intelligent Surveillance and Smart Home)

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