Internet of Things for enabling smart environments: A technology-centric perspective

Gómez, Carles; Chessa, Stefano; Fleury, Anthony; Roussos, George; Preuveneers, Davy

doi:10.3233/ais-180509

Cited by 122 publications

(68 citation statements)

References 84 publications

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“…Among most relevant AmI areas are the ones described in this paper: semantic web and agents, ambient assisted living, electronic and mobile healthcare, taking care for elderly, medical applications, elearning, smart home and smart cities. The last two fields are related to IoT papers in this anniversary issue [20,33], while medical issues in this paper are complementing the e-medicine related anniversary paper [55].…”

Section: Discussion and Summarymentioning

confidence: 99%

Artificial intelligence and ambient intelligence

Gams

Härmä

et al. 2019

AIS

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Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore's law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

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Section: Discussion and Summarymentioning

confidence: 99%

Artificial intelligence and ambient intelligence

Gams

Härmä

et al. 2019

AIS

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“…Information from sensors, which is often considered high-level, cannot be, evidently, obtained by a direct observation of their raw data. It must be processed by suitable algorithms usually based on machine learning, signal processing and data analysis [3].…”

Section: The Role Of Artificial Intelligence (Ai) On Smart Tailored Ementioning

confidence: 99%

“…Such services consist of common automated mechanisms and functions that give the ability to monitor and control home appliances, windows, air conditioning system, doors and so forth. However, there are a wide variety of services that classify smart homes differently and that provide different kinds of support and assistance to the users in their daily life activities and actions [3]. Assistive services, which are specifically tailored to the special needs of the user, are part of an assisted living environment.…”

Section: Introductionmentioning

confidence: 99%

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Rodrigues

Postolache

Cercas

2020

Sensors

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Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

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“…Many researchers used microphones as an input to complete interactions at smart homes and have added many available solutions for sending speech commands to control smart home devices [3,4]. There are some challenges in developing a speech recognition system that must be addressed involving distance, security, and noise [5]. This paper presents a proposed speech recognition system to control appliances at smart homes or smart hospitals.…”

Section: Introductionmentioning

confidence: 99%

Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

2020

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This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients. Author Contributions: Conceptualization, A.I.; Formal analysis, S.A.; Project administration, I.M.E.; Software, A.I. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest:The authors declare that there is no conflict of interest.

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Internet of Things for enabling smart environments: A technology-centric perspective

Cited by 122 publications

References 84 publications

Artificial intelligence and ambient intelligence

Artificial intelligence and ambient intelligence

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review

Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

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