TY - JOUR
T1 - Sphygmopalpation using Tactile Robotic Fingers Reveals Fundamental Arterial Pulse Patterns
AU - KONG, Ka Wai
AU - CHAN, Ho-Yin
AU - HUANG, Qingyun
AU - LEE, Francis Chee Shuen
AU - LEUNG, Alice Yeuk Lan
AU - GUAN, Binghe
AU - SHEN, Jiangang
AU - WONG, Vivian Chi-Woon Taam
AU - LI, Wen Jung
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - alternative diagnosis
KW - arterial pulse patterns
KW - deep learning
KW - electronics health records
KW - Medical services
KW - non-invasive health monitoring
KW - personalized medicine
KW - Position measurement
KW - Pressure measurement
KW - Pulse measurements
KW - Robot sensing systems
KW - Sensors
KW - sphygmopalpation
KW - Traditional Chinese medicine (TCM)
KW - Wrist
UR - http://www.scopus.com/inward/record.url?scp=85123362932&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85123362932&origin=recordpage
U2 - 10.1109/ACCESS.2022.3144475
DO - 10.1109/ACCESS.2022.3144475
M3 - RGC 21 - Publication in refereed journal
SN - 2169-3536
VL - 10
SP - 12252
EP - 12261
JO - IEEE Access
JF - IEEE Access
ER -