Comparison Method Research Articles

What dress code do we teach students and residents? A survey of patients’ and their families’ preferences regarding physicians’ appearance

Introduction: From the late 1960s to the present, physicians’ dress codes have been actively studied in Western countries. Until the early 21st century, patients tended to prefer a conservative dress style, such as “shirt and tie or skirt” with white coats for physicians. However, as attitudes toward dress codes have changed, knowledge regarding this issue needs to be updated. A variety of colours of scrubs are currently commonly used by medical professionals, but it is not known whether all colours are acceptable to patients. The current study sought to investigate the acceptability of various dress codes for physicians from the patients’ perspective, to inform medical education. Methods: Outpatients and their family members at a university hospital and a small-to-medium-sized hospital were surveyed. We inquired about which of the different styles of white coats and different colours of scrubs were most desirable for male and female physicians. We used Scheffe’s paired comparison method to determine rankings. Results: Patients and their family members expected their physicians to wear white coats rather than scrubs. Furthermore, a more traditional and formal dress code was preferred. The least preferred colour of scrubs was yellow. Conclusion: The current results indicated that patients’ preference for a traditional, conservative appearance has not changed over time. This finding does not match current perspectives on infection prevention. Both patient preferences and infection prevention are important for informing education and gaining patient trust. Keywords: White Coat, Scrub Colour, Physicians’ Appearance, Medical Education, Doctor-Patient Communication

Kansei Drives Sustainable Material Innovation—An Approach to Enhance the Added Value of Biomass Materials

Abstract: In this study, we discovered that the physical composition of materials can significantly affect users’ psychological and physiological responses. Materials research constantly offers novel materials as better alternatives to convention. However, the functionality of a material no longer ensures its commercial success and widespread use. Additionally, the material should provide significant added value beyond its practical evaluation. Kansei, a concept derived from the Japanese language, pertains to the psychological perception of a product’s functionality and has attracted considerable attention in both industry and academia. This study proposes a Kansei-driven sustainable material method to enhance materials’ added value. We quantified users’ Kansei of tea waste materials via experiments. Specifically, we first measured the physical qualities of the materials, such as their color, surface roughness, and odor index. Next, we used semantic differential and pairwise comparison methods to measure users’ impressions and preferences toward the materials. We also employed wearable physiological measurement devices to capture users’ reactions to the materials, including the skin conductance level (SCL) and heart rate (HR). Finally, we conducted correlation analysis using the Statistical Package for the Social Sciences (SPSS) on the collected physical, psychological, and physiological data. This study found strong correlations between the materials’ frictional coefficients and surface roughness and people’s psychological evaluations, such as sensory and safety factors. In addition, we also found that these physical qualities affect people’s HR and SCL, leading to changes in people’s emotions. These findings carry significant implications for enhancing the added value of materials.

UCP2-ACGAN: An adaptive condition GAN guided by U-shaped context perceptual processor for PET/CT images fusion

In order to better highlight the lesion area information and edge detailed information of the fused images, an adaptive condition Generative Adversarial Network (GAN) guided by U-shaped context perceptual processor (UCP2-ACGAN) for Positron Emission Tomography (PET) and Computed Tomography (CT) images fusion is proposed in this paper. Firstly, the network architecture of single generator and dual relative discriminators with weights sharing is used in UCP2-ACGAN. Secondly, a multi-granularity adaptive feature extraction module from coarse to fine (MGAFE) is proposed in generator, it is used to extract the valid information contained in the source images. Thirdly, a dual branch feature interactive fusion module guided by U-shaped context perceptual processor (UCP2-DBFIF) is proposed in generator. The context perceptual feature maps are obtained by U-shaped context perceptual processor (UCP2) and multiply them with the feature extraction results as the guiding conditions in the fusion process. In addition, in order to supplement the effective information lost during the feature fusion process, this paper converges the feature information of different granularity during the feature extraction stage. Finally, compared to the best of the 6 comparison methods, the results of comparison experiments show that UCP2-ACGAN is effective on several evaluation metrics values. For example, on the evaluation metrics of Average Gradient (AG), Edge Intensity (EI), Spatial Frequency (SF), Visual Information Fidelity (VIF) and Mutual Information (MI), the metrics values of CT mediastinal window images and PET fused images average increased by 30.74%, 29.35%, 40.24%, 35.47% and 26.18%.

Improving prediction accuracy of laser-induced shock wave velocity prediction using neural networks

The velocity of laser-induced shock waves affects the efficiency and efficacy of laser-based processes. The ability to accurately estimate shock wave velocity is critical for optimizing experimental combinations, creating laser-based systems, and assuring desired results. Traditional approaches to predict shock wave velocity involve empirical equations and analytical models based on simplified assumptions. However, these methods often lack accuracy and fail to capture the complex dynamics of laser-matter interactions. To overcome these limitations, we used a combination of an artificial neural network and a genetic algorithm to predict shock wave velocity. In this method, the neural network structure is dynamically designed. The optimization method does this by modifying the neural network's weights and figuring out the network's structure on our behalf. Based on the findings, our suggested technique worked very well; it surpassed other comparison methods by achieving the lowest average errors in terms of RMSE and MAE, which are 4.38 and 3.74, respectively. Moreover, the analysis has shown that our proposed method has a high level of reliability in predicting impulsive wave velocity using a neural network.

KEY PERFORMANCE INDICATORS FOR THE CREATIVE INDUSTRY

At present, the economy is developing technologically, and the creative economy makes use of new knowledge, technology and human creativity. The creative economy consists of the creative and cultural industries. Many authors deal with the issue of the creative economy and conclude that the creative industry has the potential to lead the economic sector or the economic level of regions. This paper addresses the issue of key performance indicators in the creative industries. It defines concepts such as creativity, the difference between creative and cultural industries and key performance indicators (KPIs). The research for this paper focused on the world's most awarded agencies for creativity, the world's most awarded brands for creativity and the world's most awarded countries for creativity. KPIs can be used to identify and compare the work of organisations in the creative and cultural industries. If the goals of the organisation are set and selected correctly, KPIs will provide measures of effectiveness in achieving them. The aim of this paper is to define key performance indicators for the creative industries. The method of collecting and processing information was used to collect information and then process the information related to the given topic. The method of analysis and synthesis was used to select a number of secondary data from internet sources and to trace the relationships between the chapters. The method of comparison was used to compare the information obtained. The methods of induction and deduction are used to evaluate and draw general conclusions. One of the main sources of data for the piece was the WARC Rankings 2023: Creative 100, which represents the most awarded campaigns and companies in the world. The KPIs were developed in six areas: financial, client, project, people, sales and website. Financial KPIs are the most important and reflect the performance of the creative agency as a whole. Customer KPIs show performance for the customer domain, track cost per customer and track the customer satisfaction index. Project KPIs focus on the moving parts of the project, regardless of the project management methodology. People KPIs include specific and quantifiable metrics to measure people performance, effectiveness and productivity. In sales, monthly sales growth, sales target attainment, cost as a percentage of sales and shopping basket abandonment rates are monitored. Performance monitoring should be important to all business units because it shows and reflects performance that is important to the achievement of goals or the survival of the business or institution.

IMPLEMENTATION OF THE RULE OF LAW PRINCIPLE IN THE PRACTICE OF UKRAINE: CONCEPT, STRATIFICATION AND ECONOMIC JUSTIFICATION

The subject of the study is the segmental manifestation of the rule of law principle and its elements in the activities of justice authorities, courts and public associations, taking into account the economic basis of its influence. The purpose of the study is to examine the forms of manifestation of the rule of law principle and its individual elements in the activities of justice and court authorities, to clarify the role of the public in its practical application, and to identify opportunities for its economic support. Methodology. The study used general scientific and special methods of scientific knowledge: the method of system analysis, the dialectical method, the formal logical method, the structural and functional method, as well as a number of empirical methods. In particular, the method of comparison was used to determine the transformation of penitentiary bodies and their transformation into penitentiary justice bodies. The results of the study demonstrated the need for: qualitative consideration of the aspect of ensuring the correction of a convicted person after serving his or her sentence; involvement of representatives of civil society institutions in the resocialisation processes; and identification of potential ways to implement effective reforms that can increase the efficiency and fairness of the judiciary. Conclusion. The article reveals the relationship between the transformation of the penal system into the penitentiary system and the implementation of the rule of law in this area and its comprehensive enforcement. It is noted that the criminal executive system takes into account the aspect of ensuring the serving of sentences, and in the penitentiary system, which operates under the rule of law, the authors additionally identifies such a qualitative component as ensuring the correction of a convicted person after serving his/her sentence and his/her resocialisation. It was found that the economic rationale for the transformation of the judiciary based on the rule of law consists in reducing the costs of: court functioning due to the possibility of digitalization of the preparatory stages before the court hearing and the holding of court hearings in videoconference mode; reducing costs for the court apparatus through optimization of its functions; reducing the number of court cases due to active mediation and other possible options for resolving disputes peacefully, etc. It is noted that justice in judicial proceedings means not only formal compliance with the law, but also the exercise of rights and freedoms of individuals with due regard for equality, objectivity and impartiality. The authors prove that non-governmental organisations are an important institution capable of promoting the principle of justice as an integral element of the rule of law, since they ensure the right to apply to court and the right to protection of members and third parties. The role of the public in shaping the economic basis for reforms and the proper functioning of state bodies was also highlighted. It is noted that national legislation needs to be amended in terms of granting NGOs the right to apply to court in the interests of third parties, in particular, in cases where a person is unable to protect his or her rights for valid reasons. The study analyses the fundamental provisions on economic support for the implementation of the rule of law in various spheres of public relations.

Event-triggered exponential stabilization of delayed systems on time scales

This paper addresses exponential stabilization issue for delayed systems on time scales. A dynamic event-triggered strategy (DETS) is proposed that relies on both the last sampled state and an exponential threshold, which can reduce the transmission frequency of the feedback control signal. Through inequality techniques and comparison method, a criterion of exponential stabilization for the underlying systems is presented. Besides, the measurement errors and estimation of system states are used to prevent Zeno behavior in DETS design. Two simulations are provided to illustrate the theoretical outcomes.

Approach of obtaining the error of current transformers through the measurement of turns correction factor and impedance

The error detection platform for current transformers (CTs) typically measures CT error by using the comparison method provided by International Electrotechnical Commission (IEC) 61869–2. It uses a more accurate standard CT as reference and the error of tested CT is obtained through comparison. This paper proposes an alternative approach of determining CT error by measuring the turns correction factor and magnetizing impedance. The measurement of magnetizing impedance is essentially equivalent to the excitation current measurement method. Therefore, high current sources are not required. More importantly, the measurement process requires only an impedance measurement instrument and a millivoltmeter, eliminating the need for constructing a complex system, and on-site CT error measurement can be realized. This method has engineering practical value. This paper firstly derives formulas for CT error in terms of turns correction factor and impedance. The turns correction factor is measured through the proposed differential voltage method, while the impedance is measured by using an impedance measurement instrument. Additionally, the measurement uncertainty introduced by the nonlinearity and impedance measurement instrument is analyzed. The final CT error results indicate that the proposed method yields comparable accuracy to the comparison method.

Long short-term memory (LSTM) neural networks for predicting dynamic responses and application in piezoelectric energy harvesting

Long Short-Time Memory (LSTM) deep neural networks are capable of learning order dependence in sequence problems and capturing long-term, non-linear temporal dependencies between the input and out of a system. With the long-term vision to model dynamical systems to which analytical or numerical methods are impossible or difficult to apply, this paper presents a study of modeling system dynamics and predicting responses using the LSTM networks, which have demonstrated excellent capability in predicting single-mode responses in a prior study. However, the LSTM network exhibits difficulties in modeling and predicting multi-mode responses accurately. To resolve the multi-mode issue, this paper presents an approach that obtains an equivalent network consisting of a set of sub-networks learned on isolated modes, and demonstrates its effectiveness on a simulated 2-degree-of-freedom mass-spring-damper system of nonlinear Duffing springs. The second part of the paper is focused on the application of the proposed approach in piezoelectric energy harvesting. Experiments are conducted on a harvester subjected to random base-motion excitation and exhibiting nonlinearity in its multi-mode response. Both the direct and mode-separation LSTM modeling approaches are applied to predict the output voltage given a random base-motion excitation. The mode-separation approach outperforms the direct approach significantly, and yields an excellent match between the actual and predicted responses. Specifically, for a test electrical voltage response of RMS value 0.2241 V, the difference between the actual test and predicted responses by using the mode-separation approach has an RMS value of 0.0504 V, compared to 0.1645 V obtained by using the direct LSTM approach. It is also much lower than the RMS value of 0.1835 V obtained by using the attention-based LSTM network, another comparison method. Leveraging a deep learning strategy, the validated approach opens up opportunities for accurately modeling energy harvesting systems of high complexities and/or strong nonlinearities.

Solar energy potential mapping in Ukraine through integration of GIS, remote sensing, and fuzzy logic

ABSTRACT The Green Deal strategic plan for the development of renewable energy until 2030 is of particular importance in the context of the restoration of Ukraine’s post-war energy infrastructure. One of the key topics is the analysis of the possibilities of installing large solar power plants in Ukraine. In this article, a multi-criteria analysis of the suitability of the territory of Ukraine is carried out on the basis of climatic, topographic and land use criteria. To assess land suitability, criteria standardized using fuzzy logic with weights determined by experts through the method of pairwise comparisons were combined using a weighted sum model. Upon completing the study, a suitability map was generated, depicting zones with varying levels of suitability (ranging from 0 to 1) for solar power plant placement. It was found that more than 35.68% of the country has average values of the suitability index (0.65–0.7), and approximately 18.82% show high indicators (<0.7). Conditions are especially favorable in the south of Ukraine.

Intersection of the lexical-semantic fields “Mentality” and “Physiology” in the poetic language of Igor-Severyanin

The intersection of two lexical-semantic fields “mentality” and “physiology” in the poetic texts of Igor-Severyanin is studied. The relevance of the research is determined by the fact that within the framework of modern anthropocentric approach and increased attention to the mental sphere lexical semantics is investigated, lexical-semantic fields, their intersections, semantic shifts are considered, actual problems of linguistic poetics are touched upon. The aim of the work is to explore the intersection of lexical-semantic fields “mentality” and “physiology” in the language of Severyanin's poems. The material includes lexemes of the mental sphere from Igor-Severyanin's poetic texts. While determining the usual meaning of these lexemes, the authors used the materials of “Explanatory Dictionary of the Russian Language” by S.I. Ozhegov. The methods of description, comparison, contrast, lexical-semantic and contextual analysis, and continuous sampling were applied; the lexical-semantic field - a generally accepted form of representing lexical units by their meaning - was used to structure the vocabulary. The intersections of the lexical-semantic fields “mentality” and “physiology” in Igor-Severyanin's texts, revealed in the research, are primarily due to the inseparable connection between the inner world of man and his bodily organs of sense. The language fixes stable expressions indicating the possibility of comprehending the human consciousness through analyzing the signals of his body. Words used to denote phenomena and processes related to the body, health, gestures, etc., are reinterpreted in poetic language and receive new semantics, which allows to refer these new lexical-semantic units to the lexical-semantic field of mentality. In Igor-Severyanin's poems the inner world of the lyrical subject dominates, which is reflected in the author's word usage. Even words and expressions with the semantics of physiology shift in their meanings, acquiring the semantics of mentality, at the same time thoughts and feelings are endowed with the author's positive evaluation. The prospects of the research are connected primarily with further study of the mental sphere in the Russian linguistic lexical-semantic system, its dynamics, as well as with the analysis of semantic shifts in the poetic language vocabulary.

Prioritizing complex health levels beyond autism triage using fuzzy multi-criteria decision-making

AbstractThis study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients.

Strategic Management of Financial and Economic Security: Modern Challenges and Threats in a Hyperdynamic External Environment

The main purpose of the article is to increase the efficiency of strategic management of the financial and economic security of a modern enterprise in the face of new challenges and threats. The object of the study is the system of financial and economic security of a modern company. The scientific task is to assess modern surpluses and threats in the system of ensuring the financial and economic security of an enterprise. The research methodology involves the use of modern methods of system analysis, pairwise comparison and hierarchical analysis to assess challenges and threats. As a result, a matrix of the most significant challenges and threats in the system of ensuring the financial and economic security of a modern enterprise was built. The innovativeness of the results obtained is revealed through the proposed methodological approach to assessing and streamlining challenges and threats in the system of ensuring the financial and economic security of a modern enterprise.

Explainable Graph Wavelet Denoising Network for Intelligent Fault Diagnosis.

Deep learning (DL)-based intelligent fault diagnosis methods have greatly promoted the development of the field of fault diagnosis due to their powerful feature extraction ability for handling massive monitoring data. However, most of them still suffer from the following three limitations. First, many existing DL-based intelligent diagnosis methods cannot extract proper discriminative features from signals with strong noise. Second, the interactions or relationships between signals are ignored, while they mainly focus on extracting temporal features from the signal. Third, owing to their black-box nature, the learned features lack interpretability, which hinders their application in the industry. To tackle these issues, an explainable graph wavelet denoising network (GWDN) is proposed to achieve intelligent fault diagnosis under noisy working conditions in this article. In GWDN, the collected signals are first transformed into graph-structured data to consider the interactions among signals. Then, the graph wavelet denoising convolution (GWDConv) is proposed based on the discrete graph wavelet frame, which allows GWDN to achieve multiscale feature extraction for graph-structured data and realize signal denoising. Extensive experiments are implemented to verify the efficacy of the proposed GWDN, and the experimental results show that GWDN can achieve state-of-the-art performance among the comparison methods. Besides, by using the square envelope spectrum to analyze the extracted features of GWDConv, we find that it can well retain the fault-related components of the signal and realize signal denoising, which further proves that GWDN is explainable.

Controlling parameters proportional integral derivative of DC motor using a gradient-based optimizer

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine the performance of the proposed method, a comparison method with the ant colony optimization (ACO) method. It was compared using the integral of time multiplied absolute error (ITAE). They are most popularly used in the literature. From the test results, the proposed method is promising and has better effectiveness. The proposed method, namely GBO-PID, shows the best performance.

Integration of wavelet packet transform, residual convolution neural network, and support vector machine for series arc fault detection

Series arc fault (SAF) poses a great challenge to the safe and stable operation of civil low-voltage distribution systems. For the accurate and rapid detection of SAF, this article proposes an SAF detection method using wavelet packet transform (WPT), residual convolution neural network (RCNN), and support vector machine (SVM). First, the raw current signal is decomposed into four wavelet components based on WPT. Then, the 1-D wavelet components are converted into 2-D matrices. Afterward, the matrices are input into RCNN through different channels. Finally, the detection results can be yielded by SVM. The effectiveness of the proposed method is verified based on offline experiments. The average detection accuracy of the proposed method is 99.72%, which is higher than that of the eight comparison methods. Moreover, the results of online experiments indicate that the detection time of the proposed method is less than 100 ms and can satisfy the requirement of standard 1699.

A green micelle-enhanced first derivative synchronous fluorescence approach for determination of donepezil HCl and trazodone HCl in their pure state, pharmaceutical dosage form and spiked human plasma.

The study's objective is to establish an eco-friendly, sensitive and economical quantitative methodology for the concurrent analysis of donepezil HCl (DPZ) and trazodone HCl (TRZ) in raw materials, tablets and human plasma. The first derivative synchronous fluorescence spectroscopic (FDSFS) technique was applied at constant wavelength difference (∆λ = 120) for assessment of DPZ and TRZ at each other's zero-crossing point at 279 nm and 297 nm, respectively. The submitted technique was validated in accordance with ICH Q2 R1 guidelines and the linearity of the standard calibration curve was observed over the concentration range of 10-500 ng/ml for DPZ and 20-1,000 ng/ml for TRZ. The detection limits (LOD) were found to be 2.65 and 5.4ng/ml, and the limits of quantitation (LOQ) were 8.05 and 16.3ng/ml for DPZ and TRZ, respectively. This technique was used further to quantify the studied medications in their laboratory-prepared mixtures, commercial tablets and spiked plasma samples. The results obtained were not significantly different from those acquired from the comparison methods, indicating the high accuracy and precision of the proposed method. Furthermore, the ecological friendliness of the suggested method was evaluated and proven to be excellent using Green Analytical Procedure Index (GAPI) and Analytical GREEnness (AGREE) evaluation tools.

Infrared-Visible Synthetic Data from Game Engine for Image Fusion Improvement

Limited by the shooting scenes and angles of fixed cameras, the existing datasets generally lack many detailed pedestrian models in diverse scenarios. Existing deep learning-based image fusion methods, for this reason, bring about overfitting or insufficient information of fusion results in varying degrees. To address this challenge, A new infrared-visible pedestrian synthetic dataset (GIVF) with a synthetic data tagger (GSDT) is constructed and an improved end-to-end image fusion network (FSGAN) is proposed to validate infrared and visible fusion. In the model, the method uses an auxiliary network to extract features that complement the cascade network of the main path, effectively improving the ability to extract pedestrian texture details. Experimental results show that FSGAN can be well applied to GIVF. By conducting extensive comparative experiments with eight state-of-the-art image fusion methods. FSGAN shows better performance than those comparison methods, especially in the two evaluation indexes visual information fidelity (VIF) and structural similarity measurement (SSIM). Besides, by comparing the quantitative analysis results of various methods, and evaluating the fusion results of real images in complex environments on other three datasets, we conclude that FSGAN can be better applied to GIVF datasets than other popular methods, and has outstanding performance in generalization.

Модифицированная методика расчета характеристик дискриминатора угломера в моноимпульсном радиолокаторе методом амплитудного мгновенного сравнения

A modified technique for determining the characteristics of the goniometer discriminator in a monopulse radar using the method of amplitude instantaneous comparison is proposed. Its distinctive features from the generalized technique are: in determining the minimum number of intermediate points of the interpolating function of the discriminatory characteristic of the discriminator, followed by calculating its maximum deviation from the theoretical discriminatory characteristic; in estimating the steepness of the discriminatory characteristic at each intermediate point; in calculating the fluctuation characteristic of the discriminator by estimating the variance of the error signal of angular coordinates at intermediate points of the discriminatory characteristic; in using the interpolating steepness function and the fluctuation characteristic of the discriminator for mismatches smaller than the step of change of intermediate points. The results of mathematical modeling of the discriminatory and fluctuation characteristics of the discriminator of the goniometer using the proposed technique are presented.

A skeleton-based method for exercise recognition based on 3D coordinates of human joints

&lt;p&gt;The aim of the work is to develop the method of identification and comparison of poses and exercises performed by a person that will have a low sensitivity to data errors. This method uses their formal descriptions in the form of conjunctions of logical statements and should work regardless of the shooting angle at which the video was taken and the proportions of the person on it. Each statement describes the position of the joints relative to each other along one of the axes. The joint coordinates are corrected by taking into account the length of the bones that connect them that eliminates the necessity to process outliers and it also improves the accuracy of joints positioning. Removal of errors out of the data using the method of averaging the graph along each axis at every step. In order to do this, consecutive points are grouped so that the difference between the maximum and the minimum does not exceed the error. The groups are then filtered to leave only those in which both are smaller or both are larger. The proposed method of identification requires just a modern smartphone and has no restrictions on how to take video of exercises.&lt;/p&gt;