Traditional Chinese Medicine (TCM) pulse diagnosis, a key diagnostic method for over 2000 years, lacks a standardized approach and relies heavily on the subjective experience of practitioners. Integrating TCM's nuanced pulse readings with modern data platforms has been a significant challenge. This paper addresses this challenge by developing a wearable multichannel pulse monitoring system. Existing pulse monitoring technologies often utilize single-point sensors or lack the spatial resolution needed to capture the complexity of TCM pulse diagnosis. The authors aimed to overcome these limitations by creating a system that captures multi-dimensional pulse signals from multiple points simultaneously, offering a more comprehensive and objective assessment of pulse characteristics.

Previous research has explored various sensor technologies for pulse wave detection, including optical, acoustic, and pressure sensors. While wearable pressure sensors show promise, most existing systems are limited by the size and rigidity of their sensors, hindering their integration into multichannel arrays for spatially resolved pulse monitoring. Studies using sensor arrays to analyze pulse conditions have shown progress, but often lack the temporal and spatial resolution needed to fully capture the subtleties of TCM pulse diagnosis. The lack of a method for detecting and analyzing multi-dimensional weak pulse signals has been a significant hurdle in advancing this area. This research builds upon these previous efforts by developing a flexible, wearable system with a higher density of sensors capable of providing detailed spatial and temporal pulse information.

The researchers developed a wearable multichannel pulse monitoring platform using flexible pressure sensor arrays. The system consists of 3x3 pressure sensor arrays positioned at the Cun, Guan, and Chi pulse points on the wrist. These ionogel-based sensors offer flexibility and high sensitivity. The fabrication process involved printing silver electrodes onto a flexible PET substrate, depositing ionogel films, and encapsulating them with PDMS for stability. The system acquires signals from the nine sensors, amplifies and denoises them, and uses surface fitting (cubic spline interpolation) to create a 3D pulse map. Data analysis included time domain analysis to extract parameters such as pulse frequency, ΔT_DVP (time interval between systolic and dicrotic peaks), and AI (augmentation index, P₂/P₁) from the pulse waveform. These parameters were then correlated with physiological conditions and TCM pulse classifications. Experiments were conducted on multiple volunteers under different conditions (before and after meals) to assess the system's ability to differentiate pulse characteristics.

The developed pulse sensing platform successfully captured 2D and 3D pulse signals, providing detailed temporal and spatial information. The 3x3 sensor arrays showed consistent and stable performance, allowing for accurate pulse waveform acquisition. Time domain analysis of the pulse waveforms revealed characteristic features such as P₁ (early systolic peak), P₂ (inflection point), and P₃ (dicrotic peak). Parameters such as ΔT_DVP and AI effectively differentiated between healthy individuals and pulse conditions before and after meals. The 3D pulse mapping visually represented pulse strength distribution across the three pulse positions, mirroring the sensory experience of a TCM practitioner. The system distinguished between different pulse frequencies and detected changes in pulse characteristics related to physiological states (e.g., postprandial changes). The parameter K, calculated from the pulse wave, allowed for classification of pulses according to TCM principles.

The findings demonstrate the feasibility and effectiveness of a flexible, wearable multichannel pulse monitoring system for TCM pulse diagnosis. The system's ability to capture 3D pulse information provides a more comprehensive and objective assessment of pulse characteristics than traditional single-point methods. The correlation between the extracted pulse parameters and physiological states validates the system's potential for applications in health monitoring and diagnostics. The 3D pulse map visualization improves the understanding of pulse dynamics and allows for more refined interpretation within the framework of TCM. This research bridges the gap between traditional TCM pulse diagnosis and modern quantitative analysis, offering a potential tool for improving the accuracy and efficiency of TCM diagnosis.

This study successfully developed and validated a novel wearable multichannel pulse monitoring system for TCM pulse diagnosis. The use of flexible pressure sensor arrays and 3D pulse mapping allows for more comprehensive and objective evaluation of pulse conditions than traditional methods. The system’s ability to differentiate between healthy individuals and to detect changes in physiological states underscores its potential for applications in healthcare. Future research could focus on expanding the dataset to include a wider range of subjects and conditions, integrating machine learning algorithms for automated pulse diagnosis, and developing more sophisticated data analysis techniques to further refine the interpretation of pulse characteristics.

The study's sample size was relatively small, and further research with a larger, more diverse population is needed to validate the generalizability of the findings. While the system successfully captured pulse signals, the long-term stability and durability of the sensors could be investigated more extensively. Additionally, further research is needed to establish stronger correlations between the extracted pulse parameters and specific disease conditions.