Sphygmopalpation using Tactile Robotic Fingers Reveals Fundamental Arterial Pulse Patterns

Ka Wai KONG, Ho-Yin CHAN*, Qingyun HUANG, Francis Chee Shuen LEE, Alice Yeuk Lan LEUNG, Binghe GUAN, Jiangang SHEN*, Vivian Chi-Woon Taam WONG, Wen Jung LI*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

10 Citations (Scopus)
132 Downloads (CityUHK Scholars)

Abstract

Sphygmopalpation at specific locations of human wrists has been used as a medical diagnostics technique in China since the Han Dynasty (202 BC - 220 AD) and it is now generally accepted that traditional Chinese medicine (TCM) doctors are able to decipher at least 28 fundamental pulse patterns among all patients using their fingertips. However, unlike collecting EEG (electroencephalography), ECG (electrocardiography), and EMG (electromyography) signals, there is no standardization on how the arterial pulse waves from the TCM sphygmopalpation methods should be digitalized and analyzed. We have developed a pulse sensing platform for studying and digitalizing arterial pulse patterns via a TCM approach. This platform consists of a robotic hand with three pressure-feedback-controlled robotic fingers (each with 4×6 sensing pixel arrays) for pulse measurement and an artificial neural network (ANN) for pulse pattern recognition. Data analyses reveal that 3 types of consistent pulse patterns, i.e., “HUA” (滑), “XI” (細), and “CHEN” (沉) – key fundamental pulse patterns described by TCM doctors – could be identified in a selected group of subjects. The classification rates are 99.1% in the training process and 97.4% in testing result for these 3 basic pulse patterns. The results will lead to further development of a high-level artificial intelligence system incorporating knowledge from TCM – the robotics finger system could become a standard clinical equipment for digitalizing and visualizing human arterial pulses.
Original languageEnglish
Pages (from-to)12252-12261
Number of pages10
JournalIEEE Access
Volume10
Online published18 Jan 2022
DOIs
Publication statusPublished - 2022

Funding

This work was supported in part by the Research Grants Council (JLFS—RGC-Joint Laboratory Funding Scheme) under Project JLFS/E-104/18, and in part by the Health and Medical Research Fund [HMRF—Health and Health Services (former HHSRF)] under Project 17181811.

Research Keywords

  • alternative diagnosis
  • arterial pulse patterns
  • deep learning
  • electronics health records
  • Medical services
  • non-invasive health monitoring
  • personalized medicine
  • Position measurement
  • Pressure measurement
  • Pulse measurements
  • Robot sensing systems
  • Sensors
  • sphygmopalpation
  • Traditional Chinese medicine (TCM)
  • Wrist

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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