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Connecting Data, People, and Artificial Intelligence toward Smart and Personalized...
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Connecting Data, People, and Artificial Intelligence toward Smart and Personalized Healthcare

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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 7908

Special Issue Editors

Dr. Lei Shi

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: machine learning; human-computer interaction
Dr. Ioannis Ivrissimtzis

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: machine learning; graphics
Dr. Sunčica Hadžidedić

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: human-computer interaction; recommender systems; interactive intelligent systems; affective computing; responsible AI
Dr. Xingjie Wei

E-Mail Website
Guest Editor
Centre for Decision Research, Leeds University Business School, University of Leeds, Leeds LS2 9JT, UK
Interests: social computing; social data mining
Dr. Frederick W. B. Li

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: computer graphics; geometric modelling and processing; collaborative virtual environments; visual aesthetics; educational techno
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The healthcare industry is being transformed and revolutionized by the latest advances in the Internet of Things, intelligent sensors, machine learning, privacy and security technologies, and human-centered artificial intelligence design. The widespread applications of various intelligent terminals and wearable sensing devices support the evolution of the pervasive health paradigm towards preventative, predictive, personalized, and participatory healthcare. Technological advances in wearables and sensors allow unobtrusive continuous sensing to be combined with multiple technologies such as artificial intelligence, making it possible to monitor health in an unobtrusive and seamless manner, transforming episodic, largely manual sampling processes to continuous, context-aware monitoring and intelligent intervention.

This Special Issue calls for state-of-the-art research on various issues and solutions that leverage artificial intelligence technology or focus on the impact of smart and personalized healthcare on people, medical data, and technology. Technical contribution papers, industrial case studies, and review papers are welcome. Topics of interest include, but are not limited to, the following:

  • Protecting patient privacy in data mining;
  • Multimodal sensor data fusion;
  • Intelligent sensors that facilitate the exchange of health data and knowledge;
  • Human-centered design of sensor-based applications;
  • Artificial intelligence in healthcare;
  • Personalized clinical decision support.

Dr. Lei Shi
Dr. Ioannis Ivrissimtzis
Dr. Sunčica Hadžidedić
Dr. Xingjie Wei
Dr. Frederick Li
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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Research

16 pages, 1628 KiB  
Article
Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug–Food Interactions from Chemical Structures
by Quang-Hien Kha, Viet-Huan Le, Truong Nguyen Khanh Hung, Ngan Thi Kim Nguyen and Nguyen Quoc Khanh Le
Sensors 2023, 23(8), 3962; https://doi.org/10.3390/s23083962 - 13 Apr 2023
Cited by 24 | Viewed by 3200
Abstract
Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in [...] Read more.
Possible drug–food constituent interactions (DFIs) could change the intended efficiency of particular therapeutics in medical practice. The increasing number of multiple-drug prescriptions leads to the rise of drug–drug interactions (DDIs) and DFIs. These adverse interactions lead to other implications, e.g., the decline in medicament’s effect, the withdrawals of various medications, and harmful impacts on the patients’ health. However, the importance of DFIs remains underestimated, as the number of studies on these topics is constrained. Recently, scientists have applied artificial intelligence-based models to study DFIs. However, there were still some limitations in data mining, input, and detailed annotations. This study proposed a novel prediction model to address the limitations of previous studies. In detail, we extracted 70,477 food compounds from the FooDB database and 13,580 drugs from the DrugBank database. We extracted 3780 features from each drug–food compound pair. The optimal model was eXtreme Gradient Boosting (XGBoost). We also validated the performance of our model on one external test set from a previous study which contained 1922 DFIs. Finally, we applied our model to recommend whether a drug should or should not be taken with some food compounds based on their interactions. The model can provide highly accurate and clinically relevant recommendations, especially for DFIs that may cause severe adverse events and even death. Our proposed model can contribute to developing more robust predictive models to help patients, under the supervision and consultants of physicians, avoid DFI adverse effects in combining drugs and foods for therapy. Full article
(This article belongs to the Special Issue Connecting Data, People, and Artificial Intelligence toward Smart and Personalized Healthcare)
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13 pages, 3400 KiB  
Article
Three-Dimensional Encoding Approach for Wearable Tactile Communication Devices
by Yan-Ni Lin, Yan-Cheng Li, Song Ge, Jing-Jing Xu, Lian-Lin Li and Sheng-Yong Xu
Sensors 2022, 22(24), 9568; https://doi.org/10.3390/s22249568 - 7 Dec 2022
Cited by 1 | Viewed by 1683
Abstract
In this work, we presented a novel encoding method for tactile communication. This approach was based on several tactile sensory characteristics of human skin at different body parts, such as the head and neck, where location coordinates in the three-dimensional (3D) space were [...] Read more.
In this work, we presented a novel encoding method for tactile communication. This approach was based on several tactile sensory characteristics of human skin at different body parts, such as the head and neck, where location coordinates in the three-dimensional (3D) space were clearly mapped in the brain cortex, and gentle stimulations of vibrational touching with varied strengths were received instantly and precisely. For certain applications, such as playing cards or navigating walk paths for blinded people, we demonstrated specifically designed code lists with different patterns of tactile points in varied temporal sequences. By optimizing these codes, we achieved excellent efficiency and accuracy in our test experiments. As this method matched well with the natural habits of tactile sensory, it was easy to learn in a short training period. The results of the present work have offered a silent, efficient and accurate communication solution for visually impaired people or other users. Full article
(This article belongs to the Special Issue Connecting Data, People, and Artificial Intelligence toward Smart and Personalized Healthcare)
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13 pages, 14091 KiB  
Article
Feasibility of Tracking Human Kinematics with Simultaneous Localization and Mapping (SLAM)
by Sepehr Laal, Paul Vasilyev, Sean Pearson, Mateo Aboy and James McNames
Sensors 2022, 22(23), 9378; https://doi.org/10.3390/s22239378 - 1 Dec 2022
Cited by 1 | Viewed by 2011
Abstract
We evaluated a new wearable technology that fuses inertial sensors and cameras for tracking human kinematics. These devices use on-board simultaneous localization and mapping (SLAM) algorithms to localize the camera within the environment. Significance of this technology is in its potential to overcome [...] Read more.
We evaluated a new wearable technology that fuses inertial sensors and cameras for tracking human kinematics. These devices use on-board simultaneous localization and mapping (SLAM) algorithms to localize the camera within the environment. Significance of this technology is in its potential to overcome many of the limitations of the other dominant technologies. Our results demonstrate this system often attains an estimated orientation error of less than 1° and a position error of less than 4 cm as compared to a robotic arm. This demonstrates that SLAM’s accuracy is adequate for many practical applications for tracking human kinematics. Full article
(This article belongs to the Special Issue Connecting Data, People, and Artificial Intelligence toward Smart and Personalized Healthcare)
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