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Abstract The susceptibility of mechanomyography (MMG) signals acquisition to sensor donning and doffing, and the apparent time-varying characteristics of biomedical signals collected over different periods, inevitably lead to a reduction in model recognition accuracy. To investigate the adverse effects on the recognition results of hand actions, a 12-day cross-time MMG data collection experiment with eight subjects was conducted by an armband, then a novel MMG-based hand action recognition framework with densely connected convolutional networks (DenseNet) was proposed. In this study, data from 10 days were selected as a training subset, and the remaining data from another 2 days were used as a test set to evaluate the model’s performance. As the number of days in the training set increases, the recognition accuracy increases and becomes more stable, peaking when the training set includes 10 days and achieving an average recognition rate of 99.57% (± 0.37%). In addition, part of the training subset is extracted and recombined into a new dataset and the better classification performances of models can be achieved from the test set. The method proposed effectively mitigates the adverse effects of sensor donning and doffing on recognition results.

The rake angles on both sides of the cutting edges of the hourglass worm gear hob significantly influence its cutting performance, which, in turn, plays a decisive role in the surface quality of the machined worm wheel. To balance the rake angles along the tooth height direction of each hob tooth and enhance the overall cutting performance of the hob, this paper proposes a method that utilizes a rotating paraboloid surface to generate the helical rake face of the hourglass worm gear hob. First, the conjugate condition equations for the rake face generated by the rotating paraboloid surface are derived. A mathematical model for the helical rake face of planar double-enveloping hourglass worm gear hob is established. This study explores the influence of two machining parameters on the rake angle, specifically the milling drive ratio coefficient k and the geometric parameter of a parabolic milling cutter p. Through a systematic analysis of the variations in rake angle at the dividing toroidal surface and along the tooth height direction, the optimal parameter values were identified as k = 0.9115 and p = 0.6834. The results show that, after optimization, the hob rake angle range is around ±4.7°, with a maximum rake angle difference of 6.3072° along the tooth height direction, and the rake angles on both sides of the teeth are more balanced. The structure of the rake face is more reasonable, reflecting the feasibility of rotating paraboloid tools for forming tools in the machining of complex surfaces.

Tight sandstone reservoirs are widely developed in the Mesozoic Yanchang Formation of the Ordos Basin, China. There is a lack of understanding on the sedimentary setting, source-reservoir relationship and oil accumulation conditions in this area. In this study, through the comprehensive analysis of the distribution of tight oil, we evaluated the properties and petrological features of reservoir, geochemical characteristics of source rocks, the source-reservoir relationship, as well as the trapping, preservation and accumulation conditions of tight oil in the Chang 7 Member, and predicted the sweet spots of tight oil in the study area. The results show that the Chang 7 Member is a typical low-porosity and ultra-low permeability reservoir with great tightness, small pore throat and high capillary pressure, and must have been of near-source accumulation. The source rocks are mainly developed in the Chang 73 submember, and the reservoirs mainly occur in the Chang 71 and Chang 72 submembers, forming a combination mode of “lower source rock and upper reservoir”. Sandbodies with good connectivity and fractures being well developed in local areas are the main hydrocarbon transport systems. The abnormal high pressure caused by hydrocarbon generation and pressurization is the main driving force of tight oil accumulation. The mode of hydrocarbon transportation is dominated by the vertical or lateral migration from underlying source rocks or adjacent source rocks to reservoirs within a short distance. Following the integrated evaluation of lithology, physical properties and oil saturation of reservoirs and geochemical characteristics of source rocks, we grouped the sweet spots of Chang 7 Member into three types: Type I, Type II and Type III. Among others, the Type I sweet spots are the best in terms of porosity, permeability and source rock thickness and hydrocarbon enrichment which should be the focus of oilfield development. This study lays an important foundation for the economic and efficient development of tight oil in the Chang 7 Member of Heshui area, and has important implications on tight sandstone reservoirs in other regions of Ordos Basin in China.

Aiming at the existing problems of rehabilitation robots in passive rehabilitation training for patients with severe upper limb hemiplegia， a novel 7-DOF upper limb exoskeleton robot is proposed， and a new hand fixator is designed to replace the grip of the traditional exoskeleton to better achieve the effective wrist training for patients with severe upper limb hemiplegia. According to the exoskeleton model， the Denavit-Hartenberg （D-H） coordinate system is established by using Matlab， and the workspace is simulated to verify that the workspace met the design requirements. Dynamics simulation of exoskeleton is performed by using Adams. In view of the problem that the simulation results do not conform to the reality due to the lack of the injured limb in simulation， a new design that adds the weight of the patient's upper limb to the center of mass of the exoskeleton is adopted. According to the analysis of the loading characteristics， the rationality of the selection of the robot driver is verified， which provides a theoretical basis for the development of the physical prototype.

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements.

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.

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 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.