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.

Using monitoring devices could help avoid injuries and even death. Currently, wearable sensors such as motion sensors and other sensors are used to detect when the patient is having a seizure and alarm their caregivers. However, the development phase of these devices requires labor-intensive work on labeling the collected data, resulting in difficulties in developing wearable monitoring devices. Thus, a more automated auxiliary method of labeling seizure data and a wearable device to detect seizures for daily monitoring use are necessary. We collected data from epileptics outside the hospital with our proposed bracelet. The subjects were asked to press the mark button after they had seizures. We also presented an automatically extraction and annotation of moving segments (EAMS) algorithm to exclude nonmoving segments. Then, we used a two-layer ensemble model (TLEM) using machine learning methods to classify seizures and non-seizure moving segments, which was designed to deal with imbalanced dataset. Then, we build two individual TLEM models separately for the overall (all day and night) seizure detection case and the night seizure detection case, owing to different imbalance of these datasets. The EAMS algorithm exclude 93.9% raw inactive data. The TLEM model achieved 76.84% sensitivity (SEN) and 97.28% accuracy (ACU) for the overall case and achieved 94.57% SEN and 91.37% ACU for the night case. These results indicate that this bracelet can capture seizures efficiently, and our proposed TLEM has higher SEN and ACU than single-layer machine learning models.

The respiratory signal is a critical index of cardiopulmonary function. In this paper, we implement the polyvinylidene fluoride (PVDF) sensor to collect the data of pulse waves and reference respiration signals. The correlations between the major feature values and breaths are investigated and presented. As a result, several feature values exhibit relatively good correlations with reference respiratory amplitude. The improvements of Kalman Filter for the respiratory signals extracted from feature value variances are also introduced. Moreover, we display the comparisons with low-pass filtering, Wavelet filtering, and ensemble empirical mode decomposition (EEMD) method. Using the method of identifying error breaths, the error rate of the new method is about 4:146%. The new method is feasible for real-time applications at quiet state.

The radial artery reflects the largest amount of physiological and pathological information about the human body. However, ultrasound signal processing involves a large number of complex functions, and traditional digital signal processing can hardly meet the requirements of real-time processing of ultrasound data. The research aims to improve computational accuracy and reduce the hardware complexity of ultrasound signal processing systems. Firstly, this paper proposes to apply the coordinate rotation digital computer (CORDIC) algorithm to the whole radial artery ultrasound signal processing, combines the signal processing characteristics of each sub-module, and designs the dynamic filtering module based on the radix-4 CORDIC algorithm, the quadrature demodulation module based on the partitioned-hybrid CORDIC algorithm, and the dynamic range transformation module based on the improved scale-free CORDIC algorithm. A digital radial artery ultrasound imaging system was then built to verify the accuracy of the three sub-modules. The simulation results show that the use of the high-performance CORDIC algorithm can improve the accuracy of data processing. This provides a new idea for the real-time processing of ultrasound signals. Finally, radial artery ultrasound data were collected from 20 volunteers using different probe scanning modes at three reference positions. The vessel diameter measurements were averaged to verify the reliability of the CORDIC algorithm for radial artery ultrasound imaging, which has practical application value for computer-aided clinical diagnosis.

Pulse signals carry comprehensive information regarding human cardiovascular physiology and pathology, providing a noninvasive and continuous method to assess cardiovascular health status in blood pressure monitoring. The blood pressure measurement method based on the pulse signal needs to extract the features of the single-cycle pulse signal, while the pulse signal pertains to the weak physiological signal of body surface. The acquisition process is susceptible to various factors leading to abnormal cycles, especially adjacent channel interference, affecting the subsequent feature extraction. To address this problem, this paper conducts an analysis of the formation mechanism of adjacent channel interference and proposes a single-cycle pulse signal recognition algorithm based on a one-dimensional deep convolutional neural network (1D-CNN) model. Radial pulse signals were collected from 150 subjects by pulse bracelet, and a dataset comprising 3446 single-cycle signals was extracted in total after denoising, single-cycle segmentation, and standardized preprocessing. The 1D-CNN model is trained to classify input signals into three categories: effective pulse signals, distortion, and interference signals. This classification is achieved by evaluating the waveform morphology of the signals within a single cycle. The results show that the overall classification accuracy of the algorithm on the test set is 98.26%, in which the classification accuracy of pulse waves is 99.8%, indicating that it can effectively recognize single-cycle pulse waves, which lays the foundation for subsequent continuous blood pressure measurement.