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Cited by 4Social Robot Interaction and HRIProsthetics and Rehabilitation RoboticsRobot Manipulation and Learning

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A Massage Area Positioning Algorithm for Intelligent Massage System

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Zhou, Liran, Feng, Zhiquan, Cai, Zeyuan, Yang, Xiaohui, Ai, Changsheng, Shao, Haiyan·2022·Computational Intelligence and Neuroscience
DOI 10.1155/2022/7678516PMC PMC9371831
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Abstract

A growing number of studies have been conducted over the past few years on the positioning of daily massage robots. However, most methods used for research have low interactivity, and a systematic method should be designed for accurate and intelligent positioning, thus compromising usability and user experience. In this study, a massage positioning algorithm with online learning capabilities is presented. The algorithm has the following main innovations: (1) autonomous massage localization can be achieved by gaining insights into natural human-machine interaction behavior and (2) online learning of user massage habits can be achieved by integrating recursive Bayesian ideas. As revealed by the experimental results, combining natural human-computer interaction and online learning with massage positioning is capable of helping people get rid of positioning aids, reducing their psychological and cognitive load, and achieving a more desirable positioning effect. Furthermore, the results of the analysis of user evaluations further verify the effectiveness of the algorithm.

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Crossref · 10.1155/2022/7678516

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Figures

Figure 1 Interactive device diagram for intelligent massage positioning systems: (a) Kinect 2.0 devices; (b) xArm robotic arm devices.
Figure 1 Interactive device diagram for intelligent massage positioning systems: (a) Kinect 2.0 devices; (b) xArm robotic arm devices.CC BY · PubMed Central
Figure 2 Illustration of the structure of the massage positioning system.
Figure 2 Illustration of the structure of the massage positioning system.CC BY · PubMed Central
Figure 3 Schematic diagram of the speech recognition process.
Figure 3 Schematic diagram of the speech recognition process.CC BY · PubMed Central
Figure 4 Intersection line finding diagram with different pointing lines: (a) irregular pointing; (b) regular pointing.
Figure 4 Intersection line finding diagram with different pointing lines: (a) irregular pointing; (b) regular pointing.CC BY · PubMed Central
Figure 5 Schematic diagram of the massage center point determination process: (a) the area framed by the dotted line represents T; (b) the area, central probability, and selection probability corresponding to each small section contained in the area T is indicated; (c) the massage center point is found by moving the interrogation point.
Figure 5 Schematic diagram of the massage center point determination process: (a) the area framed by the dotted line represents T; (b) the area, central probability, and selection probability corresponding to each small section contained in the area T is indicated; (c) the massage center point is found by moving the interrogation point.CC BY · PubMed Central
Figure 6 Schematic diagram of the massage area determination.
Figure 6 Schematic diagram of the massage area determination.CC BY · PubMed Central
Figure 7 Collection of massage positioning charts.
Figure 7 Collection of massage positioning charts.CC BY · PubMed Central
Figure 8 The massage area determination process: (a) the candidate area T is determined by pointing lines; (b) the area, selection probability, and central probability of the small area contained in area T are found; (c) the robot arm moves to the preferred point determined by Equation (2) and initiates a query to the user; (d) a positive response is obtained from the user, which leads to the determination of the massage area and the massage point.
Figure 8 The massage area determination process: (a) the candidate area T is determined by pointing lines; (b) the area, selection probability, and central probability of the small area contained in area T are found; (c) the robot arm moves to the preferred point determined by Equation (2) and initiates a query to the user; (d) a positive response is obtained from the user, which leads to the determination of the massage area and the massage point.CC BY · PubMed Central
Figure 9 Distribution of correct rates.
Figure 9 Distribution of correct rates.CC BY · PubMed Central
Figure 10 Collection of experimental results plots for the online learning function: (a) statistical plots of the number of interactions for #1 experimenter for 10 back and lumbar massage orientations, respectively; (b) statistical plots of the number of interactions for #2 experimenter for 10 back and lumbar massage orientations, respectively; (c) change of the number of interrogative interactions required during online learning.
Figure 10 Collection of experimental results plots for the online learning function: (a) statistical plots of the number of interactions for #1 experimenter for 10 back and lumbar massage orientations, respectively; (b) statistical plots of the number of interactions for #2 experimenter for 10 back and lumbar massage orientations, respectively; (c) change of the number of interrogative interactions required during online learning.CC BY · PubMed Central
Figure 11 NASA-TLX user evaluation graphs: (a) statistical graph of user evaluations for OLMP algorithm positioning; (b) statistical graph of user evaluations for massage chair positioning.
Figure 11 NASA-TLX user evaluation graphs: (a) statistical graph of user evaluations for OLMP algorithm positioning; (b) statistical graph of user evaluations for massage chair positioning.CC BY · PubMed Central
Algorithm 1 Online learning massage positioning (OLMP).
Algorithm 1 Online learning massage positioning (OLMP).CC BY · PubMed Central

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Research works printed but not linked: 19

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Metadata

Authors
Zhou, L., Feng, Z., Cai, Z., Yang, X., Ai, C., & Shao, H.
Affiliations
University of Jinan (CN)
Corresponding author
Feng, Zhiquan
ORCID
Venue
Computational Intelligence and Neuroscience
Publisher
Wiley
Published
2022-08-04
Volume
2022
Pages
1-13
ISSN
  • 1687-5265 (ISSN-L)
  • 1687-5273
DOI
10.1155/2022/7678516
PMC
PMC9371831
OpenAlex
W4289794223
Cited by
4
References
0
Citations by year
2025: 2 · 2024: 1 · 2023: 1
FWCI
0.94
Type
journal article
Language
en
Open access
Licence
Locations
  • Computational Intelligence and Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Independent Innovation Team Project of Jinan City (2019GXRC013, ZR2020LZH004)
  • Natural Science Foundation of Shandong Province (2019GXRC013, ZR2020LZH004)
Topics
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
  • Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
Keywords
massage robot, robot localization, human-robot interaction, online learning, user behavior modeling, cognitive load, user experience
MeSH terms
Algorithms, Bayes Theorem, Computers, Humans, Massage

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CoChat. (2026). Citation check: A Massage Area Positioning Algorithm for Intelligent Massage System. https://app.cochat.ai/papers/10.1155/2022/7678516
The paper
Zhou, L., Feng, Z., Cai, Z., Yang, X., Ai, C., & Shao, H. (2022). A Massage Area Positioning Algorithm for Intelligent Massage System. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/7678516