Application of automatized 3D moiré monitoring system in pulse measurement

Abstract

This paper presents a quick monitor method to measure micro height variations directly. Here we apply optical moiré technology with a program designed by ourselves to Traditional Chinese Medical Pulse diagnosis. We analyze the moiré pattern which records the information of pulse, and then examine the conditions of pulse, the conditions of pulse including the location of pulse (by image processing the moiré pattern), the rhythm of pulse (via the frequency), the shape of pulse (via moiré pattern) and the strength of pulse (via amplitude intensity). Therefore, we can quantify the conditions of pulse by the system.

A Wearable and Highly Sensitive Graphene Strain Sensor for Precise Home-Based Pulse Wave Monitoring

“…Currently, there are several types of sensors that are used to acquire the pulse signal such as piezoelectricity sensor, infrared sensor, acoustic sensor, liquid sensor, photoelectric sensor, and image sensor. − In traditional Chinese pulse diagnosis, physicians pick up the human pulse information by means of fingertips palpating patients’ pulses shown in the superficial arteries. Hence, a pressure or strain sensor may be the best option to imitate the physician’s tactile sensation of the pulse in the practice of traditional Chinese medicine. − Since the pulse wave belongs to weak human motions, it is difficult for sensors to differentiate all features precisely in a characteristic pulse waveform shape.…”

A comprehensive review on technological advancements for sensor-based Nadi Pariksha: An ancient Indian science for human health diagnosis

“…While we discuss the future of the automated nadi pariksha, it can be used for patient monitoring systems in which the sensor-based device can be used to monitor patients round the clock [ 28 , 33 ]. These devices can be integrated with mobile to activate alert system.…”

“… Pressure sensor 12 X 3 Sub sensor pulse width period of the pulse depth of the valley Entropy 398 Diabetes SVM 10-fold cross-validation 92.6% sensitivity 87.6% specificity 90.2% accuracy [ 64 ] 5. Image sensors Rhythm, shape Amplitude Image processing FFT, moire patterns – Validate the pulse features Matching patterns [ 28 ] 6. Pulse sensors Peak Difference Mean for VPK 120 VPK Dominance Statistical Analysis Verified with daytime [ 33 ] 7.…”