User activity recognition for energy saving in smart home environment

Lima, Wesllen Sousa; Souto, Eduardo; Rocha, Thiago Schmitz Marques da; Pazzi, Richard W.; Pramudianto, Ferry

doi:10.1109/iscc.2015.7405604

Cited by 31 publications

(16 citation statements)

References 15 publications

(22 reference statements)

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“…The availability of sensors in mobile platforms has enabled the development of a variety of practical applications for several areas of knowledge [1,2] such as:Health—through fall detection systems [3], elderly monitoring [4], and disease prevention [5]. Internet of Things and Smart Cities—through solutions used to recognize and monitor domestic activities [6] and electrical energy saving [7]. Security—through individual activity monitoring solutions [8], crowd anomaly detection [9], and object tracking [10].…”

Section: Introductionmentioning

confidence: 99%

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Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Lima

Souto

El-Khatib

et al. 2019

Sensors

Self Cite

184

100

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The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people’s lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users’ physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

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Section: Introductionmentioning

confidence: 99%

“…Internet of Things and Smart Cities—through solutions used to recognize and monitor domestic activities [6] and electrical energy saving [7].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Lima

Souto

El-Khatib

et al. 2019

Sensors

Self Cite

184

100

View full text Add to dashboard Cite

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“…In the future, the main objective will be to improve the system by adding a context-awareness approach to automate the HVAC (heating, ventilation, and air conditioning) equipment [72][73][74][75]. Improvements to the system hardware and software are planned to make it much more appropriate for specific purposes, such as hospitals, schools, and offices.…”

Section: Resultsmentioning

confidence: 99%

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

Marques

Pitarma

2019

JSAN

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Mobile health research field aims to provide access to healthcare anytime and anywhere through mobile computing technologies while using a cost-effective approach. Mobile health is closely related to ambient assisted living as both research fields address independence in elderly adults. Aging has become a relevant challenge, as it is anticipated that 20% of world population will be aged 60 years and older in 2050. Most people spend more than 90% of their time indoors, therefore the indoor environmental quality has a relevant impact on occupant's health and well-being. We intended to provide real-time indoor quality monitoring for enhanced living environments and occupational health. This paper presents the AirPlus real-time indoor environmental quality monitoring system, which incorporates several advantages when compared to other systems, such as scalability, flexibility, modularity, easy installation, and configuration, as well as mobile computing software for data consulting and notifications. The results that were obtained are promising and present a significant contribution to the monitoring solutions available in the literature. AirPlus provides a rich dataset to plan interventions for enhanced indoor quality, but also to support clinical diagnostics and correlate occupant's health problems with their living environment conditions. Keywords: ambient assisted living; enhanced living environments; health monitoring; indoor air quality; indoor environmental quality; Internet of Things; mobile health; occupational health IntroductionMobile health (mHealth) is a multifaceted research field to deliver healthcare services anytime and anywhere through mobile computing technologies while using a cost-effective approach. The mHealth services and applications are enhanced personalized healthcare systems that improve the communication between patient and doctor [1]. The development of mobile computing solutions for health improvement and well-being has significantly increased in the recent past. There is relevant interest in the study and development of reliable solutions based on mobile computing for enhanced healthcare systems [2]. However, most people do not use mobile health applications, and the individuals that use them tend to stop using it in a short period of time. Therefore, the software developers need to study new pervasive and ubiquitous methods and architectures to better address the consumer needs in order to promote the adoption of these mobile applications for enhanced living environments [3]. The proliferation of computer processing power and the evolution of wireless communication technologies have promoted mHealth and lead to the development of solutions and technologies that substantially contribute to the evolution of clinical research and healthcare [4].It is anticipated that 20% of the world population will be aged 60 years and older in 2050 [5], which will cause a significant increase of health disorders, medical services costs, and caregivers demands. Furthermore, Ambient Assisted Living (AAL)...

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“…Vector space model [ 34 ], commonly used in the information retrieval area, has been the target of several adaptations to the context of HAR in several literature works [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. This approach has been widely used because of the ease for manipulating texts.…”

Section: Classification Approachesmentioning

confidence: 99%

Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors

Lima

Bragança

Quispe

et al. 2018

Sensors

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Mobile sensing has allowed the emergence of a variety of solutions related to the monitoring and recognition of human activities (HAR). Such solutions have been implemented in smartphones for the purpose of better understanding human behavior. However, such solutions still suffer from the limitations of the computing resources found on smartphones. In this sense, the HAR area has focused on the development of solutions of low computational cost. In general, the strategies used in the solutions are based on shallow and deep learning algorithms. The problem is that not all of these strategies are feasible for implementation in smartphones due to the high computational cost required, mainly, by the steps of data preparation and the training of classification models. In this context, this article evaluates a new set of alternative strategies based on Symbolic Aggregate Approximation (SAX) and Symbolic Fourier Approximation (SFA) algorithms with the purpose of developing solutions with low computational cost in terms of memory and processing. In addition, this article also evaluates some classification algorithms adapted to manipulate symbolic data, such as SAX-VSM, BOSS, BOSS-VS and WEASEL. Experiments were performed on the UCI-HAR, SHOAIB and WISDM databases commonly used in the literature to validate HAR solutions based on smartphones. The results show that the symbolic representation algorithms are faster in the feature extraction phase, on average, by 84.81%, and reduce the consumption of memory space, on average, by 94.48%, and they have accuracy rates equivalent to conventional algorithms.

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User activity recognition for energy saving in smart home environment

Cited by 31 publications

References 15 publications

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview

mHealth: Indoor Environmental Quality Measuring System for Enhanced Health and Well-Being Based on Internet of Things

Human Activity Recognition Based on Symbolic Representation Algorithms for Inertial Sensors

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