This paper gives an overview of previous studies on photovoltaic (PV) devices, grid-connected PV inverters, control systems, maximum power point tracking (MPPT) control strategies, switching devices and transformer-less inverters. The literature is classified based on types of PV systems, DC/DC boost converters and DC/AC inverters, and types of controllers that control the circuit to ensure maximum power tracking and stabilization of load and input voltage. This is followed by the theoretical background of PV devices, an overview of MPPT controllers and common mode leakage current, and a detailed investigation of different inverter topologies regarding the ground leakage current. Furthermore, design principles of power converters, such as DC/DC boost converters, and single-phase inverters are discussed. The paper also discusses limitations and benefits in addition to the basic operating principles of several topologies. Finally, the proposed system is derived and its simulation results are discussed to offer the next generation of grid connected PV systems.

In this paper, we designed a methodology to classify facial nerve function after head and neck surgery. It is important to be able to observe the rehabilitation process objectively after a specific brain surgery, when patients are often affected by face palsy. The dataset that is used for classification problems in this study only contains 236 measurements of 127 patients of complex observations using the most commonly used House–Brackmann (HB) scale, which is based on the subjective opinion of the physician. Although there are several traditional evaluation methods for measuring facial paralysis, they still suffer from ignoring facial movement information. This plays an important role in the analysis of facial paralysis and limits the selection of useful facial features for the evaluation of facial paralysis. In this paper, we present a triple-path convolutional neural network (TPCNN) to evaluate the problem of mimetic muscle rehabilitation, which is observed by a Kinect stereovision camera. A system consisting of three modules for facial landmark measure computation and facial paralysis classification based on a parallel convolutional neural network structure is used to quantitatively assess the classification of facial nerve paralysis by considering facial features based on the region and the temporal variation of facial landmark sequences. The proposed deep network analyzes both the global and local facial movement features of a patient’s face. These extracted high-level representations are then fused for the final evaluation of facial paralysis. The experimental results have verified the better performance of TPCNN compared to state-of-the-art deep learning networks.

Gait and motion stability analysis in gait dysfunction problems is a very interesting research area. Usually, patients who undergo vestibular deafferentation are affected by changes in their dynamic balance. Therefore, it is important both patients and physicians are able to monitor the progress of the so-called vestibular compensation to observe the rehabilitation process objectively. Currently, the quantification of their progress is highly dependent on the physician’s opinion. In this article, we designed a novel methodology to classify the gait disorders associated with unilateral vestibular deafferentation in patients undergoing vestibular schwannoma surgery (model of complete vestibular loss associated with imbalance due to vestibular nerve section and eventual labyrinthectomy). We present a dual-head attentional transformer-LSTM (DHAT-LSTM) to evaluate the problem of rehabilitation from gait dysfunction, which is observed by a Kinect. A system consisting of a key-point-RCNN detector is used to compute body landmark measures and evaluate gait dysfunction based on a DHAT-LSTM network. This structure is used to quantitatively assess gait classification by tracking skeletal features based on the temporal variation of feature sequences. The proposed deep network analyses the features of the patient’s movement. These extracted high-level representations are then fed to the final evaluation of gait dysfunction. The result analytically demonstrates its effectiveness in classification evaluation when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. An accuracy greater than 81% was achieved for given sets of individuals using velocity-based, angle-based, and position features for both the whole body and the symmetric features of the body.

Recently, systems for classifying gait disorders have been of great interest. However, quantifying the progress of these disorders has been highly dependent on a physician’s judgement in classifying sick and healthy subjects. We examine the effects of gait stability analysis on gait dysfunction problems, which are impacted by the patient’s dynamic balance. The dataset in this study was collected and labelled based on the opinions of physicians at Prague Hospital; it included 84 measurements of 37 patients. A keypoint detector was applied to detect the skeletal keypoints of patients. We have prepared two different datasets from the detection and tracking results. For the proposed feature selection method, we have used statistical measurements such as the x and y coordinates for each keypoint, the distance, and the angle between two selected keypoints. Using these statistical measurements, we have prepared different subgroups with different numbers of features to examine. We have also applied ten different feature selection algorithms to obtain data from different numbers of features automatically. Then, these datasets with high-level features were used to train well-known networks, such as the long short-term memory (LSTM), gated recurrent unit (GRU), and multiple layer perceptron (MLP) networks. The study results showed that the 30 features selected by the analysis of variance (ANOVA) algorithm and used to train the GRU network ranked among the best features and resulted in a classification F -score of 85%. The results also prove that the data generated by the detector method are more effective than the data generated by the tracking method due to the format of the exercises in our dataset, which were designed by physicians. Moreover, the best feature selection approaches have considerably improved the classification F -score compared to manual feature generation.

In gait stability analysis, patients suffering from dysfunction problems are impacted by shifts in their dynamic balance. Monitoring the patients' progress is important for allowing physicians and patients to observe the rehabilitation process accurately. In this study, we designed a new methodology for classifying gait disorders to quantify patients' progress. The dataset in this study includes 84 measurements of 37 patients based on a physician's opinion. In this study, the system, which includes a Kinect camera to observe and store the frames of patients walking down a hallway, a key -point detector to detect the skeletal key points, and an encoder transformer classifier network integrated with generator-discriminator networks (ET -GD), is designed to evaluate the classification of gait dysfunction. The detector extracts the skeletal key points of patients. After feature engineering, the selected high -level features are fed into the proposed neural network to analyse patient movement and perform the final evaluation of gait dysfunction. The proposed network is inspired by the 1D encoder transformer, which is integrated with two main networks: a network for classification and a network to generate fake output data similar to the input data. Furthermore, we used a discriminator structure to distinguish between the actual data (input) and fake data (generated data). Due to the multi-structural networks in the proposed method, multi -loss functions need to be optimised; this increases the accuracy of the encoder transformer classifier.