Detecting Elderly Behaviors Based on Deep Learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

被引:12
|
作者
Almutairi, Mubarak [1 ]
Gabralla, Lubna A. [2 ]
Abubakar, Saidu [3 ]
Chiroma, Haruna [1 ]
机构
[1] Univ Hafr Al Batin, Coll Comp Sci & Engn, Hafar al Batin 31991, Saudi Arabia
[2] Princess Nourah Bint Abdulrahman Univ, Appl Coll, Dept Comp Sci & Informat Technol, Riyadh 11671, Saudi Arabia
[3] Abubakar Tafawa Balewa Univ, Dept Math Sci, Bauchi 740004, Nigeria
关键词
Older adults; Medical services; Deep learning; Smart devices; Databases; Search engines; Machine learning algorithms; Machine learning; deep learning algorithms; convolutional neural network; elderly person behaviour; VGG; Internet of Things; smart nursing home; FALL; FRAMEWORK; INTERNET;
D O I
10.1109/ACCESS.2022.3186701
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author's knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person's healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for detecting elderly behavior for elderly healthcare support.
引用
收藏
页码:69802 / 69821
页数:20
相关论文
共 50 条
  • [1] Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities
    Rahman, Anichur
    Debnath, Tanoy
    Kundu, Dipanjali
    Khan, Md. Saikat Islam
    Aishi, Airin Afroj
    Sazzad, Sadia
    Sayduzzaman, Mohammad
    Band, Shahab S.
    [J]. AIMS PUBLIC HEALTH, 2024, 11 (01): : 58 - 109
  • [2] Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications
    Elhanashi, Abdussalam
    Dini, Pierpaolo
    Saponara, Sergio
    Zheng, Qinghe
    [J]. ELECTRONICS, 2023, 12 (24)
  • [3] Reinforcement Learning in Robotics: Applications and Real-World Challenges
    Kormushev, Petar
    Calinon, Sylvain
    Caldwell, Darwin G.
    [J]. ROBOTICS, 2013, 2 (03): : 122 - 148
  • [4] Recent advances and applications of deep learning methods in materials science
    Kamal Choudhary
    Brian DeCost
    Chi Chen
    Anubhav Jain
    Francesca Tavazza
    Ryan Cohn
    Cheol Woo Park
    Alok Choudhary
    Ankit Agrawal
    Simon J. L. Billinge
    Elizabeth Holm
    Shyue Ping Ong
    Chris Wolverton
    [J]. npj Computational Materials, 8
  • [5] Recent advances and applications of deep learning methods in materials science
    Choudhary, Kamal
    DeCost, Brian
    Chen, Chi
    Jain, Anubhav
    Tavazza, Francesca
    Cohn, Ryan
    Park, Cheol Woo
    Choudhary, Alok
    Agrawal, Ankit
    Billinge, Simon J. L.
    Holm, Elizabeth
    Ong, Shyue Ping
    Wolverton, Chris
    [J]. NPJ COMPUTATIONAL MATERIALS, 2022, 8 (01)
  • [6] Real-world data: a brief review of the methods, applications, challenges and opportunities
    Liu, Fang
    Demosthenes, Panagiotakos
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2022, 22 (01)
  • [7] Real-world data: a brief review of the methods, applications, challenges and opportunities
    Fang Liu
    Demosthenes Panagiotakos
    [J]. BMC Medical Research Methodology, 22
  • [8] Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
    Nambiar, Mila
    Ghosh, Supriyo
    Ong, Priscilla
    Chan, Yu En
    Bee, Yong Mong
    Krishnaswamy, Pavitra
    [J]. PROCEEDINGS OF THE 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2023, 2023, : 4673 - 4684
  • [9] Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning
    Liu, Naijun
    Lu, Tao
    Cai, Yinghao
    Wang, Rui
    Wang, Shuo
    [J]. PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 4780 - 4784
  • [10] Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review
    Pandey, Saroj Kumar
    Janghel, Rekh Ram
    [J]. NEURAL PROCESSING LETTERS, 2019, 50 (02) : 1907 - 1935