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• A new experiment that involves upper limb motions and muscle forces is designed. • The classification of upper limb motions and muscle forces based on mechanomyography is proposed. • This study provide a basis for further research in which rehabilitation training is evaluated based on MMG. • This study has potential advantages in development on the human-computer interaction system that assist rehabilitation training. 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. [ABSTRACT FROM AUTHOR]

Prototypes of a PZT-based ultrasound needle-actuating device have shown the ability to reduce needle penetration force and enhance needle visibility with color Doppler imaging during needle insertion for tissue biopsy and regional anesthesia. However, the demand for smaller, lighter devices and the need for high performance transducers have motivated investigation of a different configuration of needle-actuation transducer, utilizing the $d_{31}$ -mode of PZT4 piezoceramic, and exploration of further improvement in its performance using relaxor-type piezocrystal. This paper outlines the development of the $d_{31}$ -mode needle actuation transducer design from simulation to fabrication and demonstration. Full characterization was performed on transducers for performance comparison. The performance of the proposed smaller, lighter $d_{31}$ -mode transducer is comparable with that of previous $d_{33}$ -mode transducers. Furthermore, it has been found to be much more efficient when using PMN-PT piezocrystal rather than piezoceramic. [ABSTRACT FROM AUTHOR]

Parameters of blood flow measured by ultrasound in radial and ulnar arteries, such as flow velocity, flow rate and wall shear rate, are widely used in clinical practice and clinical research. Investigation of these measurements is useful for evaluating accuracy and providing knowledge of error sources. A method for simulating the spectral Doppler ultrasound measurement process was developed with computational fluid dynamics providing flow-field data. Specific scanning factors were adjusted to investigate their influence on estimation of the maximum velocity waveform, and flow rate and wall shear rate were derived using the Womersley equation. The overestimation in maximum velocity increases greatly (peak systolic from about 10% to 30%, time-averaged from about 30% to 50%) when the beam-vessel angle is changed from 30° to 70°. The Womersley equation was able to estimate flow rate in both arteries with less than 3% error, but performed better in the radial artery (2.3% overestimation) than the ulnar artery (15.4% underestimation) in estimating wall shear rate. It is concluded that measurements of flow parameters in the radial and ulnar arteries with clinical ultrasound scanners are prone to clinically significant errors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Disturbances that propagate throughout a plant can have an impact on product quality and running costs. There is thus a motivation for the automated detection of plant-wide disturbances and for the isolation of the sources. A new application of independent component analysis (ICA), multi-resolution spectral ICA, is proposed to detect and isolate the sources of multiple oscillations in a chemical process. Its key feature is that it extracts dominant spectrum-like independent components each of which has a narrow-band peak that captures the behaviour of one of the oscillation sources. Additionally, a significance index is presented that links the sources to specific plant measurements in order to facilitate the isolation of the sources of the oscillations. A case study is presented that demonstrates the ability of spectral ICA to detect and isolate multiple dominant oscillations in different frequency ranges in a large data set from an industrial chemical process. [Copyright &y& Elsevier]

Abstract: Constrained, spectral, independent component analysis of perturbed controller output data is proposed to isolate multiple sources of plant-wide oscillations. The technique is described and applied to data pertaining to a simulated case study and to real data obtained from an industrial chemical plant. Results demonstrate its ability to isolate the sources of multiple oscillations at the loop level. [Copyright &y& Elsevier]

Loop status monitoring involves the declaration of deterministic trends, such as oscillations and drifting, in loops that are in multi-loop plant configurations. By analysing various time domain statistics pertaining to PI/PID control loops, a trend can be recognised as one of seven categories. The scientific basis for working with the particular statistics is given and the categorisation process is described. These statistics can be combined to produce an Overall Loop Performance Index for each loop, which can be compared to localise a single fault in a multi-loop arrangement. Estimation methods for these time domain statistics are outlined and the performance of the approach is demonstrated on both simulated and real plant data sets. [Copyright &y& Elsevier]

Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%. [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) is an electrophysiological technique that uses alternating magnetic fields to deliver electric current and stimulate the cerebral cortex. When TMS is used for the evaluation of brain diseases, it is necessary to detect the contraction of the corresponding muscles in the cerebral cortex stimulated by TMS, and the muscle activity referred to as motor evoked potential (MEP). This study simultaneously recorded the mechanomyography (MMG) and electromyography (EMG) from the right abductor pollicis brevis muscle during TMS with different intensities in order to observe whether the MEP parameters from MMG signals showed similar trait of EMG recordings. Moreover, the subspace method (N4SID) and transfer function were used to identify the TMS–MMG system. In this system, the input was a pulse signal of TMS, and the output was the MMG signal detected from the target muscle. The TMS–MMG system was identified as a fourth-order model. This study also analyzed the internal features of the system and demonstrated that the poles of healthy subjects were distributed in a range, and the gain increased with the increase of the TMS intensity. It was found that MMG signals can be used as diagnostic indicators of TMS, and the TMS–MMG model can be used to further explore the details of how TMS generates responses measured with MMG. [ABSTRACT FROM AUTHOR]

• In MMG-based hand movement recognition, novel swarm intelligence algorithms (BES, GWO, SSA) were introduced to optimize SVM. • Based on hand-crafted feature extraction methods and a deep learning method, the performances of classification methods are compared in accuracy and time consumption. • Adopting GWO-SVM with TD + FD features can obtain good classification accuracy of eight classes of hand movements, up to 93.55 %, and take relatively less training time. On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG. [ABSTRACT FROM AUTHOR]

Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and K -nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric and sensors on acrylic fiber) were compared. When all TD features were selected and SVM was adopted as the classifier, the total recognition rates of hand movements were 94.0%, 93.9% and 93.6%, respectively, of three collection methods. Using ELM can obtain similar results as SVM, with the recognition rates of 94.3%, 94.3% and 94.1%, respectively, better than using LDA (88.5%, 88.6% and 88.0%) or KNN (88.9%, 89.4% and 89.0%). For each algorithm, using TD features can acquire the highest recognition rates. Once the feature set and the classifier were selected, the total recognition rates were almost equally among three collection methods (especially for some feature sets, the differences are smaller than 1%). The results confirmed that satisfactory effects could be acquired even when the MMG was collected from sensors on fabrics with specific material, thus indicating that MMG has a unique potential value for developing wearable devices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Automated generation of all feasible assembly sequences for a given product is highly desirable in manufacturing industry. Many research studies in the past decades described efforts to find more efficient algorithms for assembly sequence planning. Imperialist competitive algorithm for assembly sequence planning is presented in this paper. Population individuals called countries are in two types: colonies and imperialists that all together form some empires. Each assembly sequence is encoded into the country. The proposed algorithm is tested and compared with genetic algorithm and particle swarm optimization. Results show that imperialist competitive algorithm can improve the quality in solution searching and upgrade the opportunity to find optimal or near-optimal solution for assembly sequence planning. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a measurement method of parametric temperature model for industrial bioreactor is proposed. The system of industrial bioreactor is an integrating process with time delay. Obviously, it cannot hold a stable system response since the characteristic roots are located on the imaginary axis of the complex plane. In this paper, the temperature model of the bioreactor was obtained by maintaining the bioreactor continuous response to a monopulse signal. This proposed method has a powerful ability to steady system response. The bioreactor consists of 30L fermentor, two impellers and four baffles. By using the proposed method and Bierman algorithm, the parameters of the temperature model for bioreactor are successfully measured on-line. Simulation results are given to show the effectiveness of the measurement method. [Copyright &y& Elsevier]