The pulse measurement instrument is based on traditional Chinese medicine (TCM) and is used to collect the pulse of patients to assist in diagnosis and treatment. In the existing pulse measurement system, desktop devices have large volumes, complex pressure adjusting operations, and unstable pressurization. Wearable devices tend to have no pressurization function or the function to pressurize three channels separately, which are not consistent with the diagnostic method in TCM. This study constructs a wearable pulse measurement system using airbags for pressurization. This system uses guide plates, guide grooves, and positioning screws to adjust the relative position of the wristband and locate Cun, Guan and Chi regions. The pulse signal measured by the sensor is collected and sent to a computer by microcontroller unit. In experiments, this system successfully obtains the best pulse-taking pressure, its pulse waveform under continuous decompression, and the pulse waveform of three regions under light, medium, and heavy pressure. Compared with the existing technology, the system has the advantages of supporting single-region and three-region pulse acquisition, independent pressure adjustment, and position adjustment. It meets the needs of home, medical, and experimental research, and it is convenient and comfortable to wear and easy to carry.

Rehabilitation training is essential for patients who have a history of certain illnesses, such as stroke. As a crucial part of rehabilitation training, upper limb training involves such key factors as upper limb motions and forces. This study investigated three upper limb motions (elbow flexion of 135°, Motion 1; shoulder flexion of 90°, Motion 2; and shoulder abduction of 90°, Motion 3) and various forces (muscle Force 0, no force; holding one 1.4 kg dumbbell, muscle Force 1; holding one 2.4 kg dumbbell, muscle Force 2) in combination to evaluate nine motion patterns. These patterns were completed by twelve healthy volunteers. Mechanomyography (MMG) measurements of the biceps brachii (Channel 1), triceps (Channel 2), and deltoid (Channel 3) muscles were collected. These were subsequently divided into signal segments corresponding to each of the motions using a segmentation method based on average energy. After extracting time-domain features and wavelet packet energy features, support vector machine analysis (SVM) was used for the classification of the upper limb motions and forces based on the MMG measurements. Channel 2 and Channel 3 were shown to play an important role in the classification of upper limb motions, and Channel 1 played a role in the classification of the forces. These results demonstrate that collection of MMG measurements from the three muscles is feasible and suggest a foundation for further studies in which rehabilitation training is evaluated based on MMG measurements.

This study investigated classification of six types of head motions using mechanomyography (MMG) signals. An unequal segmenting algorithm was adopted to segment the MMG signals generated by head motions. Three types of features (time domain, time-frequency domain and nonlinear dynamics) were extracted to construct five feature sets as candidate datasets for classification analysis. Genetic algorithm optimized support vector machine (GA-SVM) was used to classify the MMG signals. Three different kernel functions, different combinations of feature sets, different number of signal channels and training samples were selected for comparative analysis to evaluate the classification accuracy. Experimental results showed that the classifier had the best overall classification accuracy when using the radial basis function (RBF). Any combination of three different types of feature sets guaranteed an average accuracy of over 80%. In the case of the best combination (feature set 2 + 3 + 5), the classification accuracy was up to 88.2%. Using four channels to acquire MMG signal and no less than 60 training samples can assure a satisfactory classification accuracy.

The aim of this study was to evaluate the errors in measurement of volumetric flow rate and wall shear rate measured in radial and ulnar arteries using a commercial ultrasound scanning system. The Womersley equations were used to estimate the flow rate and wall shear rate waveforms, based on the measured vessel diameter and centerline velocity waveform. In the experiments, each variable (vessel depth, diameter, flow rate, beam-vessel angle and different waveform) in the phantom was investigated in turn, and its value was varied within a normal range while others were fixed at their typical values. The outcomes revealed that flow rate and wall shear rate were overestimated in all cases, from around 13% to nearly 50%. It is concluded that measurements of flow rate and wall shear rate in radial and ulnar arteries with a clinical ultrasound scanner are vulnerable to overestimation.

Wall shear stress (WSS) plays an important role in the formation, growth, and rupture of atherosclerotic plaques in arteries. This study measured WSS in diseased carotid artery phantoms with degrees of stenosis varying from 0 to 60% with both steady and pulsatile flow. Experiments were performed using in silico and real flow phantoms. Blood velocities were estimated using plane wave (PW) vector Doppler. Wall shear stress was then estimated from the velocity gradient near the wall multiplied by the viscosity of a blood-mimicking fluid. The estimated WSS using the in silico phantom agreed within 10% of the ground-truth values (root-mean-square error). The phantom experiment showed that the mean WSS and maximum WSS increased with the increasing degree of stenosis. The simulation and experiment results provide the necessary validation data to give confidence in WSS measurements in patients using the PW vector Doppler technique.