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Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions

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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: 25 July 2024 | Viewed by 31454

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Special Issue Editors

Dr. Paolo Barsocchi

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Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient-assisted living; indoor localization
Special Issues, Collections and Topics in MDPI journals

Dr. Filippo Palumbo

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Institute of Information Science and Technologies, National Research Council, 1-56124 Pisa, Italy
Interests: pervasive computing; ambient intelligence; ambient assisted living; indoor localization; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Victor Hugo C. De Albuquerque

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Instituto Federal de Educação, Federal University of Ceará, Fortaleza, 60020-181 Fortaleza/CE, Brazil
Interests: Artificial intelligence; image data science; internet of things; pattern recognition; information security
Special Issues, Collections and Topics in MDPI journals

Dr. Akash Kumar Bhoi

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Guest Editor

Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim 737136, India
Interests: Biomedical technologies; Internet of Things; artificial intelligence; biomedical signal processing; soft computing

Special Issue Information

Dear Colleagues,

The scientific and technological breakthroughs have helped in the area of the population's longevity in recent years. However, living a long and healthy life brings new challenges to governments and society. The increasing pressure on medical services for older adults is one significant consequence of longevity within any society. In this context, ambient assisted living (AAL) has a prominent role in improving scalability in healthcare services, making them reachable to older people, and keeping the user safe in their home environments. AAL can be applied as both a technical approach, related to the instruments used and how they are implemented in a system, and as an intelligent approach to data processing that models and incorporate a system architecture capable of gathering context high-level data from sensor data. Hence, artificial intelligence (AI) plays a significant role in AAL implementation. AAL systems based on artificial intelligence play an important role in healthcare systems by enhancing the overall quality of life of older people.

Healthcare solutions are in desperate need of technology for decision-making processes able to tackle typical problems of medical systems such as providing timely feedback to prevent disease transmission.

Data science analysis using AI is newly evolving, intending to empower healthcare systems and organizations to connect to harness information and convert it to usable knowledge and preferably personalized clinical decision-making. Utilizing deep learning, the implementation of AI in infectious diseases has implemented a range of improvements in the modeling of knowledge generation. Big data can be interpreted, stored, and collected in healthcare through the constantly emerging AI models, thereby allowing the understanding, rationalization, and use of data for various reasons for healthcare solutions. The hope of using AI in healthcare solutions will greatly impact the quality of disease diagnosis, prediction, and treatment, thus delivering quality care to patients across socioeconomic and geographic boundaries. During this global health emergency, the healthcare profession is pursuing technological innovations to monitoring elderly populaces from contacting or spreading infectious diseases. AI is one of those tools that can quickly monitor the rapid spreading of any disease, classify high-risk patients, and is important for real-time monitoring of elderly patients. It can also forecast mortality risk by an appropriate review of the clinicians' previous results.

This Special Issue addresses different solution strategies using Artificial Intelligence for Ambient Assisted Living (AAL) and Healthcare Solutions.

Topics of interest

Artificial intelligence
Neural networks
Machine learning
Ambient assisted living (AAL)
Biomedical signals
AI in health
Medical image processing
Ambient intelligence applications
Cognitive assistants
Smart systems
Connected devices-home automation
Connected healthcare
m-Health
User personalization and adaptation
Ubiquitous computing
Mobility and behavioral analysis
Physiological signal monitoring and analysis

Dr. Paolo Barsocchi
Dr. Filippo Palumbo
Dr. Victor Hugo C. De Albuquerque
Dr. Akash Kumar Bhoi
Guest Editors

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Published Papers (9 papers)

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Research

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12 pages, 6428 KiB

Open AccessArticle

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

by Xikang Jiang, Jinhui Zhang, Wenyao Mu, Kun Wang, Lei Li and Lin Zhang

Sensors 2023, 23(24), 9680; https://0-doi-org.brum.beds.ac.uk/10.3390/s23249680 - 07 Dec 2023

Viewed by 783

Abstract

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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18 pages, 672 KiB

Open AccessArticle

Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction

by Elias Dritsas and Maria Trigka

Sensors 2023, 23(3), 1161; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031161 - 19 Jan 2023

Cited by 16 | Viewed by 3947

Abstract

Cardiovascular diseases (CVDs) are now the leading cause of death, as the quality of life and human habits have changed significantly. CVDs are accompanied by various complications, including all pathological changes involving the heart and/or blood vessels. The list of pathological changes includes hypertension, coronary heart disease, heart failure, angina, myocardial infarction and stroke. Hence, prevention and early diagnosis could limit the onset or progression of the disease. Nowadays, machine learning (ML) techniques have gained a significant role in disease prediction and are an essential tool in medicine. In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique’s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synthetic Minority Oversampling Technique (SMOTE), and comparing them in terms of Accuracy, Recall, Precision and an Area Under the Curve (AUC). The experiment results showed that the Stacking ensemble model after SMOTE with 10-fold cross-validation prevailed over the other ones achieving an Accuracy of 87.8%, Recall of 88.3%, Precision of 88% and an AUC equal to 98.2%. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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18 pages, 2437 KiB

Open AccessArticle

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images

by Pravat Kumar Sahoo, Sushruta Mishra, Ranjit Panigrahi, Akash Kumar Bhoi and Paolo Barsocchi

Sensors 2022, 22(22), 8834; https://0-doi-org.brum.beds.ac.uk/10.3390/s22228834 - 15 Nov 2022

Cited by 75 | Viewed by 2761

Abstract

Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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21 pages, 5423 KiB

Open AccessArticle

Modified U-NET Architecture for Segmentation of Skin Lesion

by Vatsala Anand, Sheifali Gupta, Deepika Koundal, Soumya Ranjan Nayak, Paolo Barsocchi and Akash Kumar Bhoi

Sensors 2022, 22(3), 867; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030867 - 24 Jan 2022

Cited by 77 | Viewed by 5557

Abstract

Dermoscopy images can be classified more accurately if skin lesions or nodules are segmented. Because of their fuzzy borders, irregular boundaries, inter- and intra-class variances, and so on, nodule segmentation is a difficult task. For the segmentation of skin lesions from dermoscopic pictures, several algorithms have been developed. However, their accuracy lags well behind the industry standard. In this paper, a modified U-Net architecture is proposed by modifying the feature map’s dimension for an accurate and automatic segmentation of dermoscopic images. Apart from this, more kernels to the feature map allowed for a more precise extraction of the nodule. We evaluated the effectiveness of the proposed model by considering several hyper parameters such as epochs, batch size, and the types of optimizers, testing it with augmentation techniques implemented to enhance the amount of photos available in the PH2 dataset. The best performance achieved by the proposed model is with an Adam optimizer using a batch size of 8 and 75 epochs. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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16 pages, 5223 KiB

Open AccessArticle

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

by Umer Saeed, Syed Yaseen Shah, Syed Aziz Shah, Haipeng Liu, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Sana Ullah Jan, Jawad Ahmad and Qammer H. Abbasi

Sensors 2022, 22(3), 809; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030809 - 21 Jan 2022

Cited by 8 | Viewed by 2437

Abstract

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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12 pages, 242 KiB

Open AccessArticle

Factors Influencing Nursing Students’ Immersive Virtual Reality Media Technology-Based Learning

by Young-Ju Kim and Sung-Yun Ahn

Sensors 2021, 21(23), 8088; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238088 - 03 Dec 2021

Cited by 8 | Viewed by 2964

Abstract

Background/objectives: This study aims to identify the effects of cognitive and emotional variables related to immersive virtual reality media technology on learning for nursing students. Methods/Statistical analysis: The subjects of this study were 121 nursing students from a university in area D. After experiential learning with virtual reality from 6–8 June 2019, data was collected through questionnaires. For virtual reality learning, VIVE’s hTC VIVE ECO CE model was used. The collected data was analyzed using the IBM SPSS 26.0 program. Multiple Regression Analysis was used to analyze the factors influencing the subject’s virtual reality learning effects. Findings: The learning effects of the virtual reality medium had a statistically significant positive correlation with the virtual reality technology recognition, sensory immersion, realism, learning satisfaction, learning necessity, and continuous use intention (p < 0.001) scores. In personality traits, only Openness, Extraversion (p < 0.01), and Conscientiousness (p < 0.05) had a statistically significant positive correlation. As a result of regression analysis, the explanatory power of the learning effect of the virtual reality medium was 63.9% (F = 53.61, p < 0.001), with learning satisfaction, sensory immersion, continuous use intention, and Extraversion being significant influencing factors (p < 0.05). Improvements/Applications: This study is meaningful in the sense that it provided strategic implications for the teaching and learning method of virtual reality technology-based learning by considering the insights necessary to develop a learning program using virtual reality technology, according to the characteristics of virtual reality technology, and the learner’s cognitive and psychological variables. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

16 pages, 2584 KiB

Open AccessArticle

AI-Enabled Framework for Fog Computing Driven E-Healthcare Applications

by Ali Hassan Sodhro and Noman Zahid

Sensors 2021, 21(23), 8039; https://0-doi-org.brum.beds.ac.uk/10.3390/s21238039 - 01 Dec 2021

Cited by 24 | Viewed by 3971

Abstract

Artificial Intelligence (AI) is the revolutionary paradigm to empower sixth generation (6G) edge computing based e-healthcare for everyone. Thus, this research aims to promote an AI-based cost-effective and efficient healthcare application. The cyber physical system (CPS) is a key player in the internet world where humans and their personal devices such as cell phones, laptops, wearables, etc., facilitate the healthcare environment. The data extracting, examining and monitoring strategies from sensors and actuators in the entire medical landscape are facilitated by cloud-enabled technologies for absorbing and accepting the entire emerging wave of revolution. The efficient and accurate examination of voluminous data from the sensor devices poses restrictions in terms of bandwidth, delay and energy. Due to the heterogeneous nature of the Internet of Medical Things (IoMT), the driven healthcare system must be smart, interoperable, convergent, and reliable to provide pervasive and cost-effective healthcare platforms. Unfortunately, because of higher power consumption and lesser packet delivery rate, achieving interoperable, convergent, and reliable transmission is challenging in connected healthcare. In such a scenario, this paper has fourfold major contributions. The first contribution is the development of a single chip wearable electrocardiogram (ECG) with the support of an analog front end (AFE) chip model (i.e., ADS1292R) for gathering the ECG data to examine the health status of elderly or chronic patients with the IoT-based cyber physical system (CPS). The second proposes a fuzzy-based sustainable, interoperable, and reliable algorithm (FSIRA), which is an intelligent and self-adaptive decision-making approach to prioritize emergency and critical patients in association with the selected parameters for improving healthcare quality at reasonable costs. The third is the proposal of a specific cloud-based architecture for mobile and connected healthcare. The fourth is the identification of the right balance between reliability, packet loss ratio, convergence, latency, interoperability, and throughput to support an adaptive IoMT driven connected healthcare. It is examined and observed that our proposed approaches outperform the conventional techniques by providing high reliability, high convergence, interoperability, and a better foundation to analyze and interpret the accuracy in systems from a medical health aspect. As for the IoMT, an enabled healthcare cloud is the key ingredient on which to focus, as it also faces the big hurdle of less bandwidth, more delay and energy drain. Thus, we propose the mathematical trade-offs between bandwidth, interoperability, reliability, delay, and energy dissipation for IoMT-oriented smart healthcare over a 6G platform. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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15 pages, 2030 KiB

Open AccessArticle

Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

by William Taylor, Kia Dashtipour, Syed Aziz Shah, Amir Hussain, Qammer H. Abbasi and Muhammad A. Imran

Sensors 2021, 21(11), 3881; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113881 - 04 Jun 2021

Cited by 33 | Viewed by 5178

Abstract

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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Review

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33 pages, 6706 KiB

Open AccessReview

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare—A Systematic Literature Review

by Muhammad Irfan Younas, Muhammad Jawed Iqbal, Abdul Aziz and Ali Hassan Sodhro

Sensors 2023, 23(21), 8885; https://0-doi-org.brum.beds.ac.uk/10.3390/s23218885 - 01 Nov 2023

Viewed by 1365

Abstract

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the “quality of service” (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ambient Assistive Living and Healthcare Solutions)

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