|
1
|
Motwani A, Shukla PK, Pawar M. Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review. Artif Intell Med 2022; 134:102431. [PMID: 36462891 PMCID: PMC9595483 DOI: 10.1016/j.artmed.2022.102431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 02/04/2023]
Abstract
During the COVID-19 pandemic, the patient care delivery paradigm rapidly shifted to remote technological solutions. Rising rates of life expectancy of older people, and deaths due to chronic diseases (CDs) such as cancer, diabetes and respiratory disease pose many challenges to healthcare. While the feasibility of Remote Patient Monitoring (RPM) with a Smart Healthcare Monitoring (SHM) framework was somewhat questionable before the COVID-19 pandemic, it is now a proven commodity and is on its way to becoming ubiquitous. More health organizations are adopting RPM to enable CD management in the absence of individual monitoring. The current studies on SHM have reviewed the applications of IoT and/or Machine Learning (ML) in the domain, their architecture, security, privacy and other network related issues. However, no study has analyzed the AI and ubiquitous computing advances in SHM frameworks. The objective of this research is to identify and map key technical concepts in the SHM framework. In this context an interesting and meaningful classification of the research articles surveyed for this work is presented. The comprehensive and systematic review is based on the "Preferred Reporting Items for Systematic Review and Meta-Analysis" (PRISMA) approach. A total of 2540 papers were screened from leading research archives from 2016 to March 2021, and finally, 50 articles were selected for review. The major advantages, developments, distinctive architectural structure, components, technical challenges and possibilities in SHM are briefly discussed. A review of various recent cloud and fog computing based architectures, major ML implementation challenges, prospects and future trends is also presented. The survey primarily encourages the data driven predictive analytics aspects of healthcare and the development of ML models for health empowerment.
Collapse
Affiliation(s)
- Anand Motwani
- School of Computing Science & Engineering, VIT Bhopal University, Sehore, (MP) 466114, India; Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal, (MP) 462033, India.
| | - Piyush Kumar Shukla
- Department of Computer Science & Engineering, University Institute of Technology, RGPV, Bhopal, (MP) 462033, India.
| | - Mahesh Pawar
- Department of Information Technology, University Institute of Technology, RGPV, Bhopal, (MP) 462033, India.
| |
Collapse
|
|
2
|
Binyamin SS, Ben Slama S. Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid. SENSORS (BASEL, SWITZERLAND) 2022; 22:8099. [PMID: 36365795 PMCID: PMC9656614 DOI: 10.3390/s22218099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/10/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
Multi-Agent Systems (MAS) have been seen as an attractive area of research for civil engineering professionals to subdivide complex issues. Based on the assignment's history, nearby agents, and objective, the agent intended to take the appropriate action to complete the task. MAS models complex systems, smart grids, and computer networks. MAS has problems with agent coordination, security, and work distribution despite its use. This paper reviews MAS definitions, attributes, applications, issues, and communications. For this reason, MASs have drawn interest from computer science and civil engineering experts to solve complex difficulties by subdividing them into smaller assignments. Agents have individual responsibilities. Each agent selects the best action based on its activity history, interactions with neighbors, and purpose. MAS uses the modeling of complex systems, smart grids, and computer networks. Despite their extensive use, MAS still confronts agent coordination, security, and work distribution challenges. This study examines MAS's definitions, characteristics, applications, issues, communications, and evaluation, as well as the classification of MAS applications and difficulties, plus research references. This paper should be a helpful resource for MAS researchers and practitioners. MAS in controlling smart grids, including energy management, energy marketing, pricing, energy scheduling, reliability, network security, fault handling capability, agent-to-agent communication, SG-electrical cars, SG-building energy systems, and soft grids, have been examined. More than 100 MAS-based smart grid control publications have been reviewed, categorized, and compiled.
Collapse
Affiliation(s)
| | - Sami Ben Slama
- The Applied College, King Abdelaziz University, Jeddah 21589, Saudi Arabia
- Analysis and Processing of Electrical and Energy Systems Unit, Faculty of Sciences of Tunis El Manar, Tunis 2092, Tunisia
| |
Collapse
|
|
3
|
Morita PP, Sahu KS, Oetomo A. Health Monitoring Using Smart Home Technologies: A Scoping Review (Preprint). JMIR Mhealth Uhealth 2022; 11:e37347. [PMID: 37052984 PMCID: PMC10141305 DOI: 10.2196/37347] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 07/29/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. OBJECTIVE This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. METHODS Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. RESULTS Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. CONCLUSIONS This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.
Collapse
Affiliation(s)
- Plinio P Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
- Research Institute of Aging, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Centre for Digital Therapeutics, University Health Network, Toronto, ON, Canada
| | - Kirti Sundar Sahu
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Arlene Oetomo
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| |
Collapse
|
|
4
|
Huang H, Chen Z, Cao S, Xiao M, Xie L, Zhao Q. Adoption Intention and Factors Influencing the Use of Gerontechnology in Chinese Community-Dwelling Older Adults: A Mixed-Methods Study. Front Public Health 2021; 9:687048. [PMID: 34604153 PMCID: PMC8484701 DOI: 10.3389/fpubh.2021.687048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 08/11/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: To explore the Chinese community-dwelling intention of older adults to adopt gerontechnology and its influencing factors. Design: A mixed-methods sequential explanatory design with an inductive approach was employed. In phase 1, a self-made questionnaire was administered from August 2018 to December 2019. Multifactor logistic regression was used to analyze the adoption intention and factors influencing the use of gerontechnology. In phase 2, participants completed a semistructured interview to explore the adoption intention of a specific form of gerontechnology, Smart Aged Care Platform, from May to July 2020. Setting: Twelve communities in three districts of Chongqing, China. Participants: Community-dwelling older adults were included. Results: A total of 1,180 older adults completed the quantitative study; two-thirds of them (68.7%) showed adoption intention toward gerontechnology. Nineteen participants (10 users and nine nonusers) completed the qualitative study and four themes were explored. Through a summarized understanding of the qualitative and quantitative data, a conceptual model of influencing factors, namely, predictive, enabling, and need factors, was constructed. Conclusions: This study reveals that most Chinese community-dwelling older adults welcome the emergence of new technologies. However, there was a significant difference in the adoption intention of gerontechnology in Chinese community-dwelling older adults based on their sociodemographic and psychographic characteristics. Our findings extend previous technology acceptance models and theories and contribute to the existing resource base.
Collapse
Affiliation(s)
- Huanhuan Huang
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhiyu Chen
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Orthopedic, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Songmei Cao
- First Clinical College, Chongqing Medical University, Chongqing, China.,Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liling Xie
- Department of Nursing, The First Branch of First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qinghua Zhao
- Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
|
5
|
Yuan Y, Wu H, Bu X, Wu Q, Wang X, Han C, Li X, Wang X, Liu W. Improving Ammonia Detecting Performance of Polyaniline Decorated rGO Composite Membrane with GO Doping. MATERIALS 2021; 14:ma14112829. [PMID: 34070649 PMCID: PMC8198450 DOI: 10.3390/ma14112829] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/24/2022]
Abstract
Gas-sensing performance of graphene-based material has been investigated widely in recent years. Polyaniline (PANI) has been reported as an effective method to improve ammonia gas sensors’ response. A gas sensor based on a composite of rGO film and protic acid doped polyaniline (PA-PANI) with GO doping is reported in this work. GO mainly provides NH3 adsorption sites, and PA-PANI is responsible for charge transfer during the gas-sensing response process. The experimental results indicate that the NH3 gas response of rGO is enhanced significantly by decorating with PA-PANI. Moreover, a small amount of GO mixed with PA-PANI is beneficial to increase the gas response, which showed an improvement of 262.5% at 25 ppm comparing to no GO mixing in PA-PANI.
Collapse
Affiliation(s)
- Yubin Yuan
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Haiyang Wu
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Xiangrui Bu
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Qiang Wu
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Xuming Wang
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Chuanyu Han
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
| | - Xin Li
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
- Guangdong Shunde Xi’an Jiaotong University Academy, Xi’an Jiaotong University, NO.3 Deshengdong Road, Daliang, Shunde District, Foshan 528300, China
| | - Xiaoli Wang
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
- School of Science, Xi’an Jiaotong University, Xi’an 710049, China
| | - Weihua Liu
- School of Microelectronics, School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (Y.Y.); (H.W.); (X.B.); (Q.W.); (X.W.); (C.H.); (X.L.); (X.W.)
- The Key Lab of Micro-nano Electronics and System Integration of Xi’an City, Xi’an 710049, China
- Research Institute of Xi’an Jiaotong University, Hangzhou 311215, China
- Correspondence: ; Tel.: +86-29-8266-3343
| |
Collapse
|