Methodology For Diagnosis Research Articles

Methodology for the Detection and Classification of Power Quality Disturbances Using CWT and CNN

Energy generation through renewable processes has represented a suitable option for power supply; nevertheless, wind generators and photovoltaic systems can suddenly operate under undesired conditions, leading to power quality problems. In this regard, the development of condition-monitoring strategies applied to the detection of power quality disturbances becomes mandatory to ensure proper working conditions of electrical machinery. Therefore, in this work we propose a diagnosis methodology for detecting power quality disturbances by means of the continuous wavelet transform (CWT) and convolutional neural network (CNN). The novelty of this work lies in the image processing that allows us to precisely highlight the discriminant patterns through spectrograms into 2D images; the images are cropped and reduced to a standard size of 128x128 pixels to retain the most relevant information. Subsequently, the identification of six power quality disturbances is automatically performed by a convolutional neural network. The effectiveness of the proposed method is validated under a set of synthetic data as well as a real data set; the obtained results make the proposal suitable for being incorporated into the monitoring of power quality disturbances in renewable energy systems.

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

A novel methodology for transformer winding defects diagnosis based on its measured frequency response signal impedance morphology interpretation

This article proposes the characteristics study of the key parameters of power transformer winding (viz. the equivalent capacitance and the equivalent inductance) extracted from the frequency response analysis (FRA) of the winding. However, the FRA curve of the winding can be divided into three frequency ranges (low, medium, and high), where; the low and the high frequency response (LFR and HFR) ranges represent the inductive and capacitive effects of the winding respectively, which can establish best analysing and interpreting tool of the winding response behaviour to diagnose their failures. Furthermore, real axial deformations were carried out on a winding constructed for this study. In addition, the variation of the equivalent inductance and capacitance obtained from the impedance curve in the LFR and HFR ranges were analysed to locate and evaluate the faulty states with the reference state of the winding. Consequently, the proposed method provides a tool for locating and assessing the internal faults severity in power transformer windings.

Autism spectrum disorder - the fight of the public against a disease it fails to understand.

Madam, Autism is a heterogeneous set of neurodevelopmental conditions affecting around 1% of the population globally [1,2] and every 1 in 120 people in Pakistan [5]. It exhibits a higher incidence in males than females [1,2,5]; this may be because males have a lower threshold for brain dysfunction, and require a lower genetic burden to cause autism, than females. Individuals with autism commonly experience impaired social cognition and perception, atypical information processing, and restricted patterns of behaviour and interest. A study from The Lancet in 2014 determined that the autism goes hand in hand with multiple other intellectual, psychiatric, medical, and behavioural disorders, with a significant association between autism and motor abnormality, sleep disorders, and aggressive behaviours [1]. While multiple genetic and developmentally early environmental factors have been established as risk factors in the aetiology of autism, none were confirmed to be adequate alone for ASD to develop. With respect to familiarity with autism, while people are not completely unaware of the existence of the disease they do display poor knowledge about its symptoms and presentation. In a study conducted in Pakistan in 2012, 52 out of the 73 teachers who participated (71%) claimed to have knowledge regarding autism; substantial gaps were recognized in their perceived knowledge about the disease [3]. Another study conducted in Karachi also concluded that parents’ lack of awareness and insufficient knowledge among parents about the signs and symptoms as well as the treatment and diagnosis of autism [4]. Early diagnosis is crucial for timely intervention; the assessment of children needs to be developmental and multidisciplinary [1]. Even though multiple rating scales and checklists have been established, it is preferable to use more performance-based methodologies for assessment and diagnosis [2]. Thorough and focused behavioral interventions in early life can promote social communication and decrease aggression and anxiety in individuals diagnosed with ASD. Support should be personalized according to each individual and if feasible, should be multi-dimensional. Although drugs as a form of treatment diminish comorbid symptoms in autistic people, they alone display no direct effect on enhancing social communication. Awareness can be spread by donating or volunteering at fundraisers, through social media, and by participating in various autism programs. Formation of an encouraging environment that acknowledges and respects that the individual is different is imperative.

Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition

Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore, a digital twin-driven diesel engine fault diagnosis method based on the combination of the classification algorithm and the optimization algorithm is proposed and a case study of fuel injection system fault diagnosis is used to illustrate and verify the proposed method. This method closely links the physical system, virtual model, database, and diagnosis system through data transmission and the diagnostic process consists of three parts: classification, diagnosis, and decision. The fault classification part can preliminarily lock the possible types and degrees of faults, and point out the key classification features for each fault type by using classification algorithms such as Random Forest. The fault diagnosis part can diagnose and reproduce the diesel engine faults by using an optimization-simulation joint calculation model, where the virtual model variables and optimization algorithm are determined according to the possible fault types, and the optimization target depends on the importance of classification features. Then the maintenance decision can be made according to the fault detailed information. The proposed method reduces the requirement of covering the fault degree of the database, and the obtained fault model provides the possibility for subsequent online optimization and also facilitates the development of intelligent engine management.

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

Abstract New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.

The essence of students' vitality in the context of its reflexive determination.

Aim: Theoretical analysis of the problem of the individual's vitality in the context of its reflective determination, an empirical study of the formation of its components, and the proof of correlations with various types of reflection. Materials and Methods: The following psychodiagnostic methods were used in the empirical study: "Methodology of vitality diagnosis" by S. Maddi in an adaptation by D. Leontiev; test for determining the reflection of interpersonal interaction T. Pushkar in the modification of N. Senchyna; the method of research of reflexivity of thinking (T. Pashukova, A. Dopira, G. Dyakonov); the technique "Determining the level of reflection" (O. Anisimov); method "Determining the level of reflexivity" (A. Karpov, V. Ponomaryova). Results: It was established that the majority of students in both the first and fourth years have an average and low level of vitality. Vitality is directly connected to intellectual, personal, and situational reflection on a temporal basis; that is, the specified types of reflection are the leading determinants of vitality, which is statistically proven. Conclusions: The study showed the high importance of resilience for the full functioning of student youth in modern complex life conditions and its determination by the level of development of various types of reflection.

Ag2S quantum dot-based magnetic resonance/fluorescence dual-mode imaging nanoprobes for tumor diagnosis.

Accurate tumor detection is crucial for the early discovery and subsequent treatment of small neoplastic foci. Molecular imaging, which combines non-invasiveness, high specificity, and strong sensitivity, excels in diagnosing early tumors and stands out among tumor diagnosis methods. Here, we introduced a dual-modal imaging probe capable of actively targeting tumor cells, suitable for both near-infrared (NIR) fluorescence and magnetic resonance imaging (MRI). Dendritic mesoporous silica was used as a carrier for the probe, encapsulating Ag2S quantum dots (QDs) for NIR fluorescence imaging. Additionally, the probe conjugated the MRI contrast agent Gd-DOTA and cetuximab, which targeted EGFR on the tumor cell membrane surface, to achieve dual-modal imaging in the tumor area. This strategy provided a methodology for the accurate diagnosis of early-stage tumor lesions and guides precise lesion resection during surgery, offering significant potential for clinical application.

Prediction of microRNA target genes for diagnosis and prognosis of asthma development

Given the intricacy of diagnosis, there's an urgent necessity to establish objective and quantifiable biological indicators to enhance the accuracy of diagnosing asthma in its early stages. MicroRNAs emerge as promising candidates for asthma diagnosis. By leveraging the correlations between established target genes and microRNAs, a methodology for early asthma diagnosis can be formulated. A comprehensive database containing microRNAs and target genes implicated in asthma development has been compiled. The dynamics of microRNA interaction with target genes crucial for diagnosing and predicting the progression of asthma have been investigated. Specific microRNA binding sites with target genes involved in asthma development have been pinpointed. Using the DIANA-microTCDS program, features of microRNA interaction with target genes linked to asthma development have been analyzed. Notably, binding sites with significant occurrences have been identified in genes such as IL13, VEGFA, ADRB2, ALDH2, IL10, and DPP10. The conservativity of microRNA binding sites to mRNA genes has been demonstrated.

The methodology for diagnosing and managing stress developed by Grivtsova, as an independent method and as part of the preparation for inducing lucid dreams within the framework of the “Bancheko Extended Algorithm,” and its application in the “Global Forecasting System,” serves as a means of transferring transcendental states of consciousness within the “Bancheko-Technology Set”

According to the World Health Organization (WHO), the increase in stressful situations due to the accelerating pace of life, environmental pollution, urbanization, technological progress, and low socio-economic living standards of the population, underpins the rise in neurological diseases. Consequently, one of the crucial tasks in practical psychology and applied psychophysiology is the timely and comprehensive diagnosis and correction of borderline forms of psychological disorders. This study examines the application of stress diagnosis and correction methodologies. The foundation is the wave model of brain electrical activity, observed using electroencephalography, based on an algorithm developed during the experiment. The key hypothesis of the study suggests that by following the developed self-organization system, based on performing simple actions, individuals with disrupted brain electrical activity due to various stressors can avoid pharmacological interventions. It has been shown that the consequences of stress primarily include increased tension in the central nervous system (sympathetic component) and insufficient relaxation (parasympathetic component). During the exacerbation of chronic diseases, the following effects were observed in subjects: poor sleep in patients with gastritis, drowsiness and headaches in individuals with allergies, chronic migraines, and neurological pains in the head on the left side along the trigeminal nerve, meteorological dependence in hypertensive patients, and jaw locking and right-side pain in those with traumatic brain injuries. Correction of these disorders took into account their stress-induced nature. A method for rehabilitating emotional-affective disorders in individuals is proposed to overcome stressful situations. The obtained data suggest that the proposed method of restorative alpha rhythm through neurotraining has high potential in therapeutic work for overcoming and correcting stress in people from various social groups, ages, and professions.

PSYCHOSOMATIC MANIFESTATIONS OF MILITARY PSYCHOTRAUMA

Introduction. War is a super-powerful extreme event that causes adaptive capacity to decline. The need for a clearer understanding of the specifics of war psychotrauma and its clinical consequences for all components of human health determines the relevance of the topic of this study.
 The aim of the work is to study the peculiarities of psychosomatic relationships in persons with the consequences of war psychotrauma.
 Materials and methods. The experimental study group consisted of 32 patients from the number of temporarily displaced persons, the control group – 34 patients who did not change their place of residence during the entire period of the full-scale war. The diagnosis was aimed at identifying neurotic, stress-related and somatoform disorders, according to the rubric (F40-F48) of the ICD-10. Assessment of the somatic condition was carried out on the basis of analysis of anamnestic data, clinical examination of the patient and indicators of laboratory tests. Psychodiagnostic methods were used to assess the mental states of patients (Clinical Questionnaire for the Detection and Assessment of Neurotic States; Methodology for the express diagnosis of neurosis by K. Heck and H. Hess; Questionnaire SAN (Mood, Activity, Mood); Methodology for the diagnosis of Taylor's anxiety level; Self-assessment of mental states according to Eysenck; Ch. Spibleger's Reactive and Personal Anxiety Assessment Scale in the modification of Y. Hanin. Statistical processing of the research results was carried out in the Microsoft Excel program and with the help of the Social Science Statistics online calculator.
 Results. As a result of the analysis of anamnestic data, clinical examination and analysis of laboratory tests, psychosomatic disorders were found in patients of both research groups: in 29 (90.63%) patients of the experimental group and in 26 (74.47%) patients of the control group.
 Conclusions. It has been confirmed that a prolonged stressful extreme situation of a full-scale war causes the formation of negative mental states accompanied by psychosomatic manifestations, the formation of a closed vicious circle of a pathological psychosomatic process. A high close direct correlation was found between the level of reactive anxiety, autonomic disorders, well-being, activity, mood of patients and psychosomatic symptoms. The absence of a statistically significant difference in the risk of developing psychosomatic disorders as a result of war psychotrauma in temporarily displaced persons and those who did not change their place of residence was revealed.

Revolutionizing Breast Cancer Care: AI-Enhanced Diagnosis and Patient History

Breast cancer poses a significant global health challenge, demanding enhanced diagnostic accuracy and streamlined medical history documentation. This study presents a holistic approach that harnesses the power of artificial intelligence (AI) and machine learning (ML) to address these pressing needs. This study presents a comprehensive methodology for breast cancer diagnosis and medical history generation, integrating data collection, feature extraction, machine learning, and AI-driven history-taking. The research employs a systematic approach to ensure accurate diagnosis and efficient history collection. Data preprocessing merges similar attributes to streamline analysis. Three key algorithms, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Fuzzy Logic, are applied. Fuzzy Logic shows exceptional accuracy in handling uncertain data. Deep learning models enhance predictive accuracy, emphasizing the synergy between traditional and deep learning approaches. The AI-driven history collection simplifies the patient history-taking process, adapting questions dynamically based on patient responses. Comprehensive medical history reports summarize patient data, facilitating informed healthcare decisions. The research prioritizes ethical compliance and data privacy. OpenAI has integrated GPT-3.5 to generate automated patient reports, offering structured overviews of patient health history. The study’s results indicate the potential for enhanced disease prediction accuracy and streamlined medical history collection, contributing to more reliable healthcare assessments and patient care. Machine learning, deep learning, and AI-driven approaches hold promise for a wide range of applications, particularly in healthcare and beyond.

A computer aided system for skin cancer detection based on Developed version of the Archimedes Optimization algorithm

In recent years, skin cancer has been recognized as the most dangerous and common type of cancer in humans. Melanoma is a common skin cancer, and the diagnosis of melanoma in the early stages of the disease can significantly prevent death from this deadly skin cancer. Providing a method that facilitates the diagnosis of melanoma in the early stages is very useful and valuable. The current paper proposes a new methodology for the best diagnosis of melanoma cancer using dermoscopic images. The proposed method begins with a normalization of scaling the data to a standard range and a histogram equalization to enhance the quality of the input images. Then, some different features based on the Gray-Level Co-occurrence Matrix (GLCM) are extracted from the image. GLCM features capture the spatial distribution and correlation of the pixel intensities, which reflect the texture information of the images. For reducing the complexity of the method, minimum features have been selected using a newly Developed version of Archimedes Optimization Algorithm (DAOA). Then, the selected features are classified by a Support Vector Machine (SVM) to distinguish between benign and malignant lesions. The proposed method is applied to the American Cancer Society (ACS) dataset, which consists of 68 pairs of TLM and XLM images with a size of 180×180 pixels. The results have been compared with five different methods based on five performance indicators: precision, sensitivity, accuracy, specificity, and F-measure. The results indicate that the presented approach gives a proper efficiency for the diagnosis of the melanoma. The proposed method achieves the highest values for all the performance indicators among the compared methods. The proposed method achieves an accuracy of 88 %, a sensitivity of 96 %, a specificity of 81 %, a precision of 97 %, and an F-measure of 97 % for the diagnosis of melanoma.

Recent Advances in the HPPH-Based Third-Generation Photodynamic Agents in Biomedical Applications.

Photodynamic therapy has emerged as a recognized anti-tumor treatment involving three fundamental elements: photosensitizers, light, and reactive oxygen species. Enhancing the effectiveness of photosensitizers remains the primary avenue for improving the biological therapeutic outcomes of PDT. Through three generations of development, HPPH is a 2-(1-hexyloxyethyl)-2-devinyl derivative of pyropheophorbide-α, representing a second-generation photosensitizer already undergoing clinical trials for various tumors. The evolution toward third-generation photosensitizers based on HPPH involves structural modifications for multimodal applications and the combination of multifunctional compounds, leading to improved imaging localization and superior anti-tumor effects. While research into third-generation HPPH is beneficial for advancing PDT treatment, equal attention should also be directed toward the other two essential elements and personalized diagnosis and treatment methodologies.

Interdisciplinary approach to the diagnosis of dentofacial deformities and speech disorders in preschoolers

Relevance. Maxillofacial deformities and speech disorders are common types of oral pathology detected in children under seven years. Comprehensive rehabilitation of patients with a combination of these forms of pathology is a difficult task since there is no situational analysis of their prevalence; motivated specialists work in different institutions (education, healthcare) and have different guidelines for the treatment (correction) of existing diseases (disorders), which complicates planning and administering treatment, and predicting results.Material and methods. To identify the nature of the relationship between various forms of orthodontic and speech disorders in children, we conducted a comprehensive clinical dental examination of 80 children aged 4-6 years in a preschool educational institution, where children with speech disorders receive speech therapy assistance.Results. Maxillofacial deformities appeared to be in 56.25% of children with some speech sound disorder. Children with identified speech sound disorders combined with dentofacial deformities showed not only skeletal but also soft tissue defects: shifted midline (86.67 ± 0.10%), distal occlusion (42.22 ± 0.30%), deep overbite (46.68%), as well as tongue and upper lip ties in 51.10% and 22.20% of cases, respectively. On examination, 44.45% of patients demonstrated clinical signs of mouth breathing, 32.5% – reverse swallowing, and 53.33% – inefficient chewing.Conclusion. Analysis of the results of a comprehensive speech pathology assessment and dental examination allowed us to conclude that there is a need to rethink the methodology of diagnosis, prevention and treatment of combined speech and dental disorders due to the high frequency of their comorbidity in preschool children.

A feature selection and ensemble learning based methodology for transformer fault diagnosis

Dissolved gas analysis (DGA) data are generally used to diagnose a transformer fault. However, the measurement errors in DGA data are inevitable and will affect the accuracy and reliability of the diagnosis results. Nevertheless, so far, only a few efforts have been devoted to addressing this issue. To provide an accurate and stable transformer fault diagnosis system, a feature selection and ensemble learning based methodology is proposed. Firstly, an Overlapping Information Feature Selection (OIFS) method is proposed to select efficient features from the given feature set for classifiers. Secondly, an Intelligent Voting Ensemble Learning (IVEL) method is proposed to generate a diagnosis model based on the feature combination from the OIFS method. The reported test results show that the OIFS method outperforms existing methods in 9 out of 10 tested classifiers. Additionally, the IVEL outperforms three popular ensemble learning methods, including random forest (RF), gradient boosting decision tree (GBDT), and LightGBM, in terms of both accuracy and robustness performances. Finally, the proposed methodology (OIFS-IVEL) is applied to diagnose the transformer faults in the IEC TC 10 database, achieving a 100% accuracy in recognizing fault types and a 92.6 % accuracy in evaluating fault severity.

Selection of relevant features to detect and diagnose single and multiple simultaneous soft faults in air-source heat pumps

Soft faults in air-source heat pump systems can cause various issues, such as decreased performance, increased energy consumption, and long-term damage. This paper presents a comprehensive modeling approach of air-source heat pumps that considers individual and simultaneous soft faults validated against empirical experiments from the literature, a perspective limited in previous research. The methodology is based on the simulation of fault-free and faulty system performance utilizing heat pump modeling software. The results are a selection of variables and their trends to select the appropriate features to detect and diagnose single and double soft faults. The model also computes the impacts of these combined faults on COP, revealing significant reductions. Specifically, combinations of overcharge with condenser fouling and undercharge with compressor valve leakages induce COP reductions ranging from 17% to 38%, depending on operating conditions. The model can be used to develop faulty heat pump data, which can be adapted to other system typologies and conditions and used as input for data-driven Fault Detection and Diagnosis (FDD) methodologies.

Brain Tumor Detection and Classification Using Hybrid VGG-XGB model

To avoid potential fatalities, it is vital to diagnose brain tumors at its earlier stage itself. Tumors vary widely in shape, size, and location and this makes accurate segmentation and classification difficult even with significant efforts in this field. Therefore, a hybrid method for classifying brain tumors called the hybrid VGG-XGBoost (hybrid VGG-XGB) model is proposed in this work. The K-means technique is used to extract significant characteristics from brain tumor images after they have first been improved using contrast-limited adaptive histogram equalization (CLAHE). Then, for extracting the high-level features segmented regions, VGG-19 model has been used and for classification process, XGBoost supervised learning technique has been utilized. As evidenced by criteria like precision, recall, F1-score, kappa score, and the confusion matrix, our novel approach outperformed baseline models and produced excellent results with a 98% accuracy rate. The suggested methodology for brain cancer diagnosis in healthcare systems is recommended by us, based on highly predictive outcomes

An anti-noise algorithm based on locally linear embedding and weighted XGBoost for fault diagnosis of T/R module

Due to the noise accompanied with fault signals, it is challenging to identify the discriminant information and the local geometric feature from the complex fault data for enhancing fault diagnosis accuracy. To address this challenge, this work proposed an anti-noise algorithm based on locally linear embedding integrated with diffusion distance and maximum correntropy criterion (DMLLE). In DMLLE, diffusion distance was adopted instead of the Euclidean distance for neighborhood construction. Meanwhile, the optimal weights were updated to reveal local geometry information based on the loss function of the maximum correntropy criterion. Subsequently, DMLLE is eventually developed to further restrain noise embedding into raw signals and obtain low dimensional features. Furthermore, weighted extreme gradient boosting is used to map the low dimensional features to the types of faults, which easily implements fault pattern recognition. Finally, two synthetic manifold datasets and fault data acquired from the transmit/receive (T/R) module are used to validate the performance of the proposed diagnosis methodology. Compared with the existing methods, the proposed diagnosis methodology generates a smoother flow structure by preserving the local neighborhood of the dataset with noises and realizes a higher accuracy of 94.41% on the T/R module dataset, which outperforms 3%–9% better than other classification models. Therefore, it can be concluded that the proposed diagnosis methodology can effectively extract intrinsic fault features by weakening the influence aroused by noise and achieve superior accuracy in fault diagnosis by addressing the problem of small sample size.

Computerized analysis of hypomimia and hypokinetic dysarthria for improved diagnosis of Parkinson's disease

Background and ObjectiveAn aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. MethodsWe enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm. ResultsThe acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. ConclusionsThe introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life.