TCM Pulse Detection Using Raspberry Pi and Pulse Sensor

Abstract

Traditional Chinese medicine (TCM) pulse diagnosis has long depended on practitioners’ subjective judgment, making standardization and objective analysis challenging. This study presents a TCM pulse detection system that integrates Raspberry Pi with pulse-sensing technology to digitize and analyze pulse data. The system captures pulse signals reliably and uses Python to extract pulse rate and waveform features, which are then matched with classical pulse pattern classifications. Results show that the platform consistently acquires stable data and accurately recognizes basic pulse types. This research contributes a reproducible and low-cost digital tool to enhance diagnostic accuracy in TCM. Future improvements in algorithms may further expand its capacity to identify more complex patterns, supporting the modernization of TCM practices.