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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 22577

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Special Issue Editor

Dr. Hemant Ghayvat

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Computer Science and Media Technology Department, Linnaeus University, 392 34 Växjö, Sweden
Interests: ambient assisted living; preventive healthcare monitoring; smart home and health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of the journal Sensors entitled “Smart Assisted Living” will focus on all aspects of research and development related to this area. Ambient Assisted Living (AAL) aims to assist people with health emergencies, physical or cognitive disabilities, and health diseases such as dementia and Parkinson’s. Their operational areas include sophisticated platforms that include sensor fusion, smart sensors, smart healthcare systems, sensor conception, and smart home acoustics.

Although fellow researchers are working in all these areas and have provided some solutions to help caregivers or healthcare experts, the customization element has not been widely addressed. It is essential to design and develop cost-effective next-generation healthcare solutions to assist people with a variety of disabilities and provide real-time updates to household members and caregivers. In recent times, it has also become essential to provide all the emergency healthcare facilities in smart cars, smart offices, and elderly homes.

This Special Issue aims to collect the most recent advances in the area of Ambient Assisted Living and Activities of Daily Living. We are inviting the submission of original and unpublished work addressing several research topics of interest, including but not limited to the following issues:

health informatics;

assisted automation;

digitally enhanced living;

intelligent virtual assistants (IVAs);

enhanced living environments;

next generation healthcare monitoring;

smart sensors;

smart home;

mobile healthcare monitoring;

ambient intelligence

Dr. Hemant Ghayvat
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Published Papers (4 papers)

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Research

27 pages, 19242 KiB

Open AccessArticle

Design of a Kitchen-Monitoring and Decision-Making System to Support AAL Applications

by Nikola Žarić, Milutin Radonjić, Nikola Pavlićević and Sanja Paunović Žarić

Sensors 2021, 21(13), 4449; https://doi.org/10.3390/s21134449 - 29 Jun 2021

Cited by 9 | Viewed by 3977

Abstract

Numerous researchers are working on Ambient Assisted Living systems to enable more comfortable and safer living for senior people in their homes. Due to modern lifestyles and an aging population, this has become a very important issue. According to the available literature, it is obvious that the kitchen is one of the most important and most dangerous rooms in the home. However, there is still evident lack of monitoring systems suitable for specific kitchen activities. In this paper, we propose a monitoring system capable of identifying activities related to the cooking process, and a decision-making system capable of identifying some unwanted and possibly critical conditions. The proposed systems are designed to satisfy the requirements of the modern Ambient Assisted Living systems dedicated to older adults. The proposed monitoring system consists of ultrasound, temperature, and humidity sensors. The acquired results from these sensors are the inputs for the decision-making system, which generate warnings or alarms intended for the senior users and/or formal or informal caregivers. This system is designed to improve home safety related to kitchen activities, as well as to provide information about the lifestyle and daily activities of senior users. Experimental validation of the proposed system confirms its functionality and accurate design approach. Full article

(This article belongs to the Special Issue Smart Assisted Living)

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32 pages, 3204 KiB

Open AccessArticle

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

by Chirag I. Patel, Dileep Labana, Sharnil Pandya, Kirit Modi, Hemant Ghayvat and Muhammad Awais

Sensors 2020, 20(24), 7299; https://doi.org/10.3390/s20247299 - 18 Dec 2020

Cited by 59 | Viewed by 5424

Abstract

Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors. Full article

(This article belongs to the Special Issue Smart Assisted Living)

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25 pages, 9969 KiB

Open AccessArticle

Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging

by Muhammad Awais, Hemant Ghayvat, Anitha Krishnan Pandarathodiyil, Wan Maria Nabillah Ghani, Anand Ramanathan, Sharnil Pandya, Nicolas Walter, Mohamad Naufal Saad, Rosnah Binti Zain and Ibrahima Faye

Sensors 2020, 20(20), 5780; https://doi.org/10.3390/s20205780 - 12 Oct 2020

Cited by 35 | Viewed by 5266

Abstract

Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia. Full article

(This article belongs to the Special Issue Smart Assisted Living)

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25 pages, 5266 KiB

Open AccessArticle

Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

by Sharnil Pandya, Hemant Ghayvat, Anirban Sur, Muhammad Awais, Ketan Kotecha, Santosh Saxena, Nandita Jassal and Gayatri Pingale

Sensors 2020, 20(18), 5448; https://doi.org/10.3390/s20185448 - 22 Sep 2020

Cited by 24 | Viewed by 6697

Abstract

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O₃). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO₂, and O₃, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants. Full article

(This article belongs to the Special Issue Smart Assisted Living)

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