Deciphering 3D Human Sphygmopalpation Pulse Patterns using “X-ray” Images Acquired from Tactile Robotic Fingers

ABSTRACT

Sphygmopalpation at specific locations of human wrists has been used as a medical measurement technique in China since the Han Dynasty (202 BC – 220 AD); it is now generally accepted that traditional Chinese medicine (TCM) doctors are able to decipher 28 types of basic pulse patterns using their fingertips. This TCM technique of examining individual arterial pulses by palpation has undergone an upsurge recently in popularity as a low-cost and non-invasive diagnostic technique for monitoring patient health status. 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 fingers for pulse measurement and an artificial neural network (ANN) together with pulse signal preprocessing for pulse pattern recognition. The platforms previously reported by other research groups or marketed commercially exhibit one or more of the following imperfections: a single channel for data acquisition, low sensitivity and rigid sensors, lack of control of the applied pressure, and in many reported works, lack of an intelligent data analysis system. The platform presented here features up to three-dimensional (3D) tactile sensing channels for recording data and uses highly sensitive capacitive MEMS (microelectromechanical systems) flexible sensing arrays, pressure-feedback-controlled robotic fingers, and machine learning algorithms. We also proposed a methodology of obtaining “X-ray” image of pulse information constructed based on the sensing data from 3 locations and 3 applied pressures (i.e., mimicking TCM doctors), which contains all arterial pulse information in both spatial and temporal spans, and which could be used as an input to a deep learning algorithm. By applying our developed platform and algorithms, 3 types of consistent pulse patterns, i.e., “HUA” , “XI” , and “CHEN” , as described by TCM doctors, could be identified in a selected group of 3 subjects who were diagnosed by TCM practitioners. We have shown the classification rates is 98.7% in training process and 84.2% in testing result for these 3 basic pulse patterns. The high classification rate of the developed platform could 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