Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. Variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function components existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode decomposition number K and penalty factor α in VMD must be optimized to obtain the best signal decomposition accuracy. This paper proposes an improved VMD denoising algorithm that overcomes the shortcomings of slow parameter selection and poor generalization in the traditional VMD algorithm. The algorithm presented firstly adopts EMD algorithm to remove the low-frequency baseline drift noise, and then employs the adaptive particle swarm optimization algorithm to optimize the parameter pair ( K , α) for VMD. To validate the denoising performance of the improved VMD algorithm, the No.103 record from the MIT arrhythmia database is first selected as the pure ECG signal, then both 20dB Gaussian white noises and 0.3 Hz baseline drift are added to simulate the noisy ECG signal. Second, the ECG signals of nine subjects are collected by a customized ECG detection platform based on AD8232 and ADALM1000. The ECG denoising results in simulation and actual experiments show that the improved VMD algorithm achieves the highest signal-to-noise ratio, correlation coefficient and minimum mean square error compared with the traditional EMD and VMD algorithm, which demonstrates the proposed denoising algorithm has stronger denoising ability and better retains morphological characteristics of the original ECG signals.

As the most characteristic feature of Parkinson’s disease (PD), bradykinesia (slowness of movement) affects all patients with Parkinson’s disease and interferes with their daily activities. This study introduces a wearable bradykinesia assessment system whose core component is composed of an inertial measurement unit. The system diagram and assessment task were defined in accordance with clinical requirements from neurologists. Based on hand grasping actions, calculations of hand grasping ranges and statistical methods of quantitatively assessing parkinsonian bradykinesia were presented. Seven control subjects and eight patients were tested with this system. Experimental results show that a calculated bradykinesia parameter (modified mean range, instead of mean and standard deviation of the grasp ranges) correlated well with the evaluations of a neurologist (Pearson’s correlation coefficient r = −0.83, p < 0.001). The bradykinesia assessment system was tested on both health subjects and PD patients. The results show that this system has greater correlation with the evaluations by neurologists than other parkinsonian bradykinesia assessment systems. The modified mean range was verified as the major bradykinesia parameter (key indicator). This study is helpful to those who want to use consumer-grade inertial sensors for quantitative assessment of motor symptoms during treatment.

Water resources are an indispensable precious resource for human survival and development. Water quality prediction plays a vital role in protecting and enhancing water resources. Changes in water quality are influenced by many factors, both long-term and short-term. Therefore, according to water quality changes’ periodic and nonlinear characteristics, this paper considered dissolved oxygen as the research object and constructed a neural network model combining convolutional neural network (CNN) and long short-term memory network (LSTM) to predict dissolved oxygen index in water quality. Firstly, we preprocessed the water quality data set obtained from the water quality monitoring platform. Secondly, we used a CNN network to extract local features from the preprocessed water quality data and transferred time series with better expressive power than the original water quality information to the LSTM layer for prediction. We choose optimal parameters by setting the number of neurons in the LSTM network and the size and number of convolution kernels in the CNN network. Finally, LSTM and the proposed model were used to evaluate the water quality data. Experiments showed that the proposed model is more accurate than the conventional LSTM in the prediction effect of peak fitting. Compared with the conventional LSTM model, its root mean square error, Pearson correlation coefficient, mean absolute error and mean square error were respectively optimized by 5.99%, 2.80%, 2.24%, and 11.63%.

Voltage sag is one of the most prominent power quality problems in electric power system. Accurate estimation of voltage sag parameters is an important reference for electric utilities and users to evaluate power quality. In this paper, a novel algorithm based on modified S transform with digital prolate spheroidal window (DPSW) is proposed to achieve accurate detection of voltage sag. The DPSW and its time-frequency characteristics are firstly detailed. Then, the construction and performance of modified S transform with DPSW are given. Since DPSW has superior time-frequency distribution, characteristics of voltage sag signal, such as sag depth, duration, and phase jump, can be obtained by analyzing the two-dimensional complex matrix after the modified S transform with DPSW. Simulation and experimental results verify the accuracy and validity of the proposed algorithm.

The optical tracking system (OTS) plays a vital role in the computer-assisted surgical navigation process, whereas the performance of the commonly used binocular stereo vision is affected by the line-of-sight problem and limited workspace. Thus, this article proposed a prior knowledge-based multicamera reconstruction model (PKRM) to both expand the tracking workspace and improve the tracking robust and computational efficiency of OTS when working in unstructured clinical conditions. This reconstruction model inherits the advantages of the geometrical method, data-driven method, and gating technique (GT). First, we added the geometric principle as the prior knowledge to optimize the training of the multicamera OTS reconstruction model through the Lagrange multiplier method; hence, the prior knowledge feedforward NN (PKFNN) was built. Second, besides the training features, the state of camera (SOC) was extracted in advance to determine the NN structure using GT. According to the SOC feature, the OTS can be self-adaptive to the changing field of view (FOV) caused by optical occlusion, which is frequently occurred in surgery. Furthermore, experiments were carried out to verify the performance of the proposed model, whose accuracy and runtime performed 0.4627 mm and 0.0016 ms, respectively. Results demonstrate that the proposed reconstruction model can achieve higher accuracy and computational efficiency than both the geometrical model and the data-driven model. Especially, by considering SOC as the state prior knowledge, the tracking robustness is enhanced when one or two of the four cameras are not working properly.