KOA Massage Robot: A Study on the Reduction of TCM Manipulation Based on PSO-BP Algorithm

被引:0
|
作者
Shen, Yichun [1 ]
Wang, Shuyi [1 ]
Shen, Yuhan [1 ]
Xing, Hua [2 ]
Gong, Li [2 ]
Hu, Jingyi [1 ]
机构
[1] Univ Shanghai Sci & Technol, Sch Hlth Sci & Engn, Shanghai 200093, Peoples R China
[2] Yueyang Hosp Integrated Tradit Chinese & Western M, Shanghai 201203, Peoples R China
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Neural networks; Force; Robot sensing systems; Older adults; Accuracy; Medical treatment; Medical services; Control systems; Particle swarm optimization; Degenerative diseases; Improved particle swarm optimization; optimization BP neural network; TCM massage; KNEE OSTEOARTHRITIS;
D O I
10.1109/ACCESS.2024.3471889
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The aging population in China is increasing the prevalence of degenerative diseases such as knee osteoarthritis (KOA), which significantly impacts the elderly's quality of life. Traditional Chinese Medicine (TCM) massage has been effective in alleviating KOA symptoms; however, the physician-patient ratio and the physical demands on practitioners pose challenges. This study introduces the KOA massage robot, designed to replicate the seated knee adjustment manipulation, a specific TCM technique. The robot's structural design and a Particle Swarm Optimization-Back Propagation (PSO-BP) algorithm are integrated to reduce the manipulation required by physicians and to assist in KOA massage treatment. The robot's force prediction accuracy was determined to be 5.34N on average, and its therapeutic efficacy was supported by Surface Electromyography (sEMG) and Visual Analog Scale (VAS) assessments, demonstrating pain relief and improved quadriceps muscle activation in KOA patients. The experimental validation involved a comparison between traditional manual massage and the robotic intervention. The results showed that the robot could achieve an average increase in integrated EMG (iEMG) of 47%, closely mirroring the 45.37% increase observed in the manual treatment group. Similarly on VAS scores, the robotic intervention group obtained a decrease of 28.12%. This study's findings not only highlight the potential of integrating TCM principles with modern robotics but also pave the way for a new paradigm in elderly care, where personalized and efficient KOA management is within reach.
引用
收藏
页码:149367 / 149380
页数:14
相关论文
共 50 条
  • [31] Vegetable Price Prediction Based on PSO-BP Neural Network
    Ye Lu
    Li Yuping
    Liang Weihong
    Song Qidao
    Liu Yanqun
    Qin Xiaoli
    PROCEEDINGS OF 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2015), 2015, : 1093 - 1096
  • [32] Gold price prediction method based on improved PSO-BP
    Wang, Yan
    Zhang, Liguo
    Liu, Yongfu
    Guo, Jun
    International Journal of u- and e- Service, Science and Technology, 2015, 8 (11) : 253 - 260
  • [33] Classification of flour types based on PSO-BP neural network
    Chen, Maomao
    Liu, Mingliang
    PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC), 2018, : 2591 - 2595
  • [34] Chaotic secure communication system based on PSO-BP network
    Ou Qingli
    Zhao Pingrong
    Qiu Zhaoliang
    2012 2ND INTERNATIONAL CONFERENCE ON APPLIED ROBOTICS FOR THE POWER INDUSTRY (CARPI), 2012, : 792 - 795
  • [35] Phishing Detection Research Based on PSO-BP Neural Network
    Chen, Wenwu
    Wang, Xu An
    Zhang, Wei
    Xu, Chunfen
    ADVANCES IN INTERNET, DATA & WEB TECHNOLOGIES, 2018, 17 : 990 - 998
  • [36] Study on combined stress failure envelope of CMG based on PSO-BP neural network
    Huang, Shouqing
    Qin, Taichun
    Yang, Xiaoning
    Li, Fangyong
    Zhou, Yuan
    Yu, Yifang
    Wang, Hao
    AIP ADVANCES, 2023, 13 (08)
  • [37] RETRACTED: Information System Security Evaluation Algorithm Based on PSO-BP Neural Network (Retracted Article)
    Zheng, Qinghua
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2021, 2021
  • [38] Displacement Back Analysis Based on GA-BP and PSO-BP Neural Network
    Dongdong, Gu
    Yunliang, Tan
    PROCEEDINGS OF THE 8TH RUSSIAN-CHINESE SYMPOSIUM COAL IN THE 21ST CENTURY: MINING, PROCESSING, SAFETY, 2016, 92 : 169 - 174
  • [39] Prediction of Water Consumption Based on PSO-BP Model in Mining Face
    Wang, Pei
    2016 INTERNATIONAL CONFERENCE ON POWER, ENERGY ENGINEERING AND MANAGEMENT (PEEM 2016), 2016, : 408 - 414
  • [40] Fault diagnosis of cascaded inverter based on PSO-BP neural networks
    Wang Xin
    Sun He-nan
    Wang Dan-lu
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 3269 - 3273