Haiying Zhang

Acupuncture, a key therapeutic method in traditional Chinese medicine (TCM), has been widely adopted in various clinical fields. Quantitative research on acupuncture manipulation parameters is critical for achieving standardized techniques. However, quantitative mechanical detection of acupuncture parameters remains limited. In this study, a kinematic and dynamic model of acupuncture is established, and key parameters such as the lifting-thrusting force, acceleration, velocity, and displacement, as well as the twirling-rotating angular velocity and angle, are identified. To measure these critical parameters, we propose a quantitative system comprising a sensing needle equipped with a force sensor and an inertial measurement unit (IMU), as well as an external camera module to capture image information. By fusing visual and IMU data, we can accurately identify the stationary or motion states of the needle, enabling segmented computation of the lifting-thrusting velocity and displacement. The experimental results demonstrate that the sensing needle achieves comprehensive detection with high precision, featuring an accuracy of 0.45% in force measurement and an RMSE of 1.2 mm in displacement. The extracted parameters provide an objective description of the operational characteristics and motion patterns of the four basic acupuncture manipulations and two auxiliary manipulations. These findings provide valuable tools and methods for research on acupuncture standardization.

Acupuncture, one of the key therapeutic methods in Traditional Chinese Medicine (TCM), has been widely adopted in various clinical fields. Quantitative research on acupuncture manipulation parameters is critical to achieve standardized techniques. However, quantitative mechanical detection of acupuncture parameters remains limited. This study establishes a kinematic and dynamic model of acupuncture, identifying key parameters such as lifting-thrusting force, acceleration, velocity, displacement, as well as twirling-rotating angular velocity and angle. To measure these critical parameters, we propose a quantitative system comprising a sensing needle equipped with a force sensor and an inertial measurement unit (IMU), as well as an external camera module to capture image information. By fusing visual and IMU data, we accurately identify the stationary or motion states of the needle, enabling segmented computation of lifting-thrusting velocity and displacement. The experimental results demonstrate that the sensing needle achieves comprehensive detection with high precision, featuring a nonlinearity error of 0.45% in force measurement and an RMSE of 1.2 mm in displacement. The extracted parameters provide an objective description of the operational characteristics and motion patterns of the four basic acupuncture manipulations. These findings provide valuable tools and methods for research in acupuncture standardization.

Tongue diagnosis is a crucial method in traditional Chinese medicine (TCM) for obtaining information about a patient’s health condition. In this study, we propose a tongue image segmentation method based on deep learning and a pixel-level tongue color classification method utilizing machine learning techniques such as support vector machine (SVM) and ridge regression. These two approaches together form a comprehensive framework that spans from tongue image acquisition to segmentation and analysis. This framework provides an objective and visualized representation of pixel-wise classification and proportion distribution within tongue images, effectively assisting TCM practitioners in diagnosing tongue conditions. It mitigates the reliance on subjective observations in traditional tongue diagnosis, reducing human bias and enhancing the objectivity of TCM diagnosis. The proposed framework consists of three main components: tongue image segmentation, pixel-wise classification, and tongue color classification. In the segmentation stage, we integrate the Segment Anything Model (SAM) into the overall segmentation network. This approach not only achieves an intersection over union (IoU) score above 0.95 across three tongue image datasets but also significantly reduces the labor-intensive annotation process required for training traditional segmentation models while improving the generalization capability of the segmentation model. For pixel-wise classification, we propose a lightweight pixel classification model based on SVM, achieving a classification accuracy of 92%. In the tongue color classification stage, we introduce a ridge regression model that classifies tongue color based on the proportion of different pixel categories. Using this method, the classification accuracy reaches 91.80%. The proposed approach enables accurate and efficient tongue image segmentation, provides an intuitive visualization of tongue color distribution, and objectively analyzes and quantifies the proportion of different tongue color categories. In the future, this framework holds potential for validation and optimization in clinical practice.

The radial artery contains wealthy physiological information, and the pulse signal at the radial artery is critical for responding to the condition of the cardiovascular system. It is essential to consider their spatial and temporal dimensions to understand pulse signals comprehensively. However, most traditional pulse acquisition platforms are based on single-point sensors, leading to limited information and considerable errors. This paper proposes a pulse signal acquisition system equipped with a high-density sensor array capable of accurately and automatically locating arterial pulsation points. By focusing on the strongest pulse signals, the system enables precise monitoring of heart rate and respiratory rate. Additionally, an innovative bicubic interpolation method is employed to construct a high-precision three-dimensional (3D) pulse map, allowing for the extraction of pulse width and pulse length parameters. Using the flip-chip packaging, a 24-channel array of microelectromechanical systems (MEMS) piezoresistive sensors has been built. The area of a sensor is only 1.8 mm × 1.8 mm. Sensor experiments have demonstrated that sensors exhibit good linearity, fast response time, high sensitivity, high consistency, and good durability. This system provides a wealth of information for pulse signal research and holds potential applications for health diagnostics.

Objective: This study presents, for the first time, an ultrasound method combining focused and single plane wave transmissions to achieve single calibration, non-invasive, cuffless blood pressure monitoring. Methods: High-quality echo signals obtained from focused transmissions were first used to identify the optimal segment of the radial artery wall. Then, single plane wave signals were utilized to track this segment frame-by-frame and calculate local pulse wave velocity (PWV). Finally, the M-K equation was applied to estimate systolic and diastolic blood pressures. Results: To validate the effectiveness of the proposed algorithm, 25 volunteers participated in the study. Data were collected using both an oscillometric cuff-based blood pressure monitor and our ultrasound system in four states: morning, evening, pre-exercise, and post-exercise. A total of 300 sets of comparative data were obtained. The overall mean deviation for systolic blood pressure was 2.2 ± 2.1 mmHg, and for diastolic blood pressure, it was 2.1 ± 2.2 mmHg. Conclusion: To our knowledge, this is the first study to achieve real-time blood pressure calculation within seconds using a single-array ultrasound transducer. Compared to existing non-invasive continuous blood pressure monitoring methods, this approach is faster and provides more accurate vascular wall information. Significance: Combined with affordable hardware, this method holds significant potential for the development of wearable devices for real-time cardiovascular health monitoring.

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R² of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications.

Analog front-end (AFE) circuits play an important role in the acquisition of physiological signals with low-level amplitudes (from tens of μV to tens of mV) and broadband low-frequency ranges (from sub-Hz to several hundred Hz). Possessing a high input impedance, an instrumentation amplifier (IA) accurately amplifies signals with low amplitude and low frequency, making it suitable for AFE circuits. This work demonstrates a capacitively coupled IA whose feedback resistance is realized by the proposed hybrid resistor, consisting of a switched-capacitor low-pass filter, a switched-capacitor resistor, and a continuous-time low-pass filter. The capacitively coupled IA achieves tera-ohm (TΩ) resistance and is insensitive to process, voltage, and temperature (PVT) variations. The simulation results show that the proposed IA illustrates a high-pass corner of 73.9 mHz, and the change of its high-pass corner with temperature is 0.05 mHz/°C. With the variation in the PVT corners, the difference between the maximum and minimum values of the high-pass corner of the proposed capacitively coupled IA is only 0.06 Hz. The design was implemented in a 130 nm standard CMOS process. The AFE with the proposed capacitively coupled IA achieves a 53.9 dB signal-to-noise and distortion ratio (SNDR) and 69.5 dB total harmonic distortion (THD).

Abnormalities in cardiac function arise irregularly and typically involve multimodal electrical, mechanical vibrations, and acoustics alterations. This paper proposes an Electro-Mechano-Acoustic (EMA) activity model for mapping the complete macroscopic cardiac function to refine the systematic interpretation of cardiac multimodal assessment. We abstract this activity pattern and build the mapping system by analyzing the functional comparison of the heart pump and Electronic Fuel Injection (EFI) system from the multimodal characteristics of the heart. Electrocardiogram (ECG), seismocardiogram (SCG) & Ultra-Low Frequency seismocardiogram (ULF-SCG), and Phonocardiogram (PCG) are selected to implement the EMA mapping respectively. First, a novel low-frequency cardiograph compound sensor capable of extracting both SCG and ULF-SCG is proposed, which is integrated with ECG and PCG modules on a single hardware device for portable dynamic acquisition. Afterward, a multimodal signal processing chain further analyses the acquired synchronized signals, and the extracted ULF-SCG is shown to indicate changes in heart volume. In particular, the proposed method based on waveform curvature is used to extract 9 feature points of the SCG signal, and the overall recognition accuracy reaches over 90% in the data collected by EMA portable device. Ultimately, we integrate the portable device and signal processing chains to form the EMA cardiovascular mapping system (EMACMS). As a next-generation system solution for cardiac daily dynamic monitoring, which can map the mechanical coupling and electromechanical coupling process, extract multi-characteristic heart rate variability (HRV), and enable extraction of important time intervals of cardiac activity to assess cardiac function.