Hand Waving Research Articles

Continuity·Evolution: Exploring the development trend of Tujia hand-waving dance along the river in Guizhou

The Tujia people are famous for their bravery, hard work, and ability to sing and dance. Their long-term working life and migration have shaped their splendid history and culture. Among them, the most representative one is the "hand waving dance", which is often performed during festivals, sacrifices and prayer activities. With the rapid development of industrialization and modernization, the ideas and production methods of Tujia people in the Tujia area have undergone drastic changes . For example, the Tujia people along the river in Guizhou have gradually accepted new ideas and culture and abandoned their traditional way of life, resulting in the original material civilization. and spiritual culture are gradually fading out of the stage of history, including the hand-waving dance of the Tujia people , and they are also facing the risk of disappearing or being forgotten. In order to protect the sustainable development of hand- waving dance, it is necessary to conduct in-depth research on its dance form, artistic style and future development trends to deepen people's awareness of the protection of hand-waving dance. It also has an important impact on China's cultural diversity and the protection of intangible cultural heritage.

Wireless Gesture Controlled Robot for Landmine Detection

Abstract: Most prominent problems today in society are terrorism and insurgency. Soldiers are risking their lives to keep a check on the above problems. Soldiers are facing difficulties to reach challenging places. Landmine explosions killed more and more amputations than ordnance. A lot of research is going-on in defence systems which are capable of safeguarding citizens from terrorist threats. This has motivated us to work on Unmanned Ground Vehicle (UGV) which could serve military operations. Robots are utilized to increase the warrior’s capacity in an open territory. Gestures involve motion of human hands, arms and head and are used within a certain context. This robot is based on the Gesture control mode or Hand wave mode. Gesture Controlled car is a robot which can be controlled by simple human gestures. User can interact with the robot in more friendly way due to wireless communication. The old method of detecting landmines such as direct sweeping is very risky for stepping the landmine unintentionally. In this project, the robot system is equipped with a metal detector useful to detect the metal presence based on coil induction when it's approaching the metal. When the robot detects the metal presence, the buzzer sound will be triggered. These robots can be used in the place of the spy team and also for the Investigation process. This robot will help in detecting the landmines and saving the precious lives of military. It will also help in detecting injured soldiers and send information to the base station. This robot has a potential to be incredibly useful for applications like surveillance, military operations and Industrial robotic arms for physically challenged people.

Slinky-inspired triboelectric–electromagnetic hybrid generator

Researchers are attempting to develop hybrid generators by integrating several mechanical energy harvesting techniques, although most of them employ complicated device structures and fabrication methods. In this paper, we propose a slinky inspired triboelectric-electromagnetic hybrid generator (S-TEHG) that can easily function as a portable power source for devices used in daily life. This S-TEHG generates a high output using a peak voltage and current output of up to 7.4 V and 83.2 mA, respectively. For practical application as a portable power source in everyday life, the S-TEHG is enclosed (packaged) in an acrylic cylinder. In this study, upon hand waving, the proposed packaged S-TEHG successfully illuminated an array of 15 light-emitting diodes and charged a 47 μF capacitor.

Modern architectures convolutional neural networks in human activity recognition

In recent years, many researchers have focused on using convolutional neural networks to perform human activity recognition as evidenced by the emergence of a number of convolutional neural network architectures such as LeNet-5, AlexNet and VGG16 and modern architectures such as ResNet, Inception V3, Inception-ResNet, MobileNet V2, NASNet and PNASNet. The main characteristic of a convolutional neural network (CNN) is its ability to extract features automatically from input images, which facilitates the processes of activity recognition and classification. Convolutional networks indeed derive more relevant and complex features with every additional layer. In addition, CNNs have achieved perfect classification on highly similar activities that were previously extremely difficult to classify. In this paper, we evaluate modern convolutional neural networks in terms of their human activity recognition accuracy, and we compare the results with the state of the art methods. In our research, we used two public data sets, HMDB (Shooting gun, kicking, falling to the floor, punching) and the Weizman dataset (walking, running, jumping, bending, one hand waving, two-hand waving, jumping in place, jumping jack, skipping). Our experimental results indicated that the CNN with NASNet architecture achieves the best performance of the six CNN architectures on both human activity data sets (HMDB and Weizman).

A Contactless IoT-Based GPS-Tracked Waste Bin to Curb Medical Waste Infections in Ghana

Irregular checking of waste especially in some medical facilities of Ghana leads to overflow of waste. A study by World Health Organisation (WHO) in February 2018 indicates that 15% of the total waste collected from medical facilities may be infectious, toxic, or radioactive and may contribute to unintended release of chemical or biological hazards. Considering the present COVID-19 pandemic and other diseases like ebola and hantavirus, it is critical to properly manage waste collected from medical facilities. In this study, Internet of Things (IoT) is used to design a smart bin to help reduce user contact to waste. Using a flowchart, a schematic model of the system was developed using Proteus 8.11 software. Two HCSR04 ultrasonic sensors were used to measure the waste level in the bin and detect proximity of objects to the bin to trigger an MG996R servo motor for automatic operation of the lid of the waste bin having a monitoring system. A NEO-6M GPS module was used to determine the location of the waste bin and displayed on a 16x2 LCD. A Graphical User Interface (GUI) was created for remote monitoring of waste over the internet. An ESP32 node MCU was programmed using Arduino software and used to interface the system with the GUI. The designed system was constructed and incorporated into a rectangular-shaped plastic bin. During testing, a hand wave at the sensor on the side of the container triggered opening of the bin. Sample waste placed in the bin were detected and real-time information regarding waste levels were sent to a self-designed HTML webpage called ‘Smart Bin’ with dynamic IP address. This system could be used in health facilities to prevent medical waste overflow, limit human contact to waste and avoid spread of infections.

Asymptotic analysis of multi-phase-field models: A thorough consideration of binary interfaces.

Although multi-phase-field models are applied extensively to simulate various pattern formations, their asymptotic analysis is not typically performed at a level of rigor common to their scalar counterparts. Most of the time, arguments given, such as for the justification of the selection of the bulk phases or the phasal composition of the interfaces between them, are only heuristic in nature. In particular, the reduction of the multi-phase-field models to two-phase ones, so as to ascertain the dynamical laws captured by them, can only be termed as hand waving, at best. It is also common to land the starting point of the analysis directly at a point where the binary interfaces have already formed and continue therefrom with the prediction of their instantaneous evolution. However, exactly how a given initial filling transitions to a state characterized by the presence of bulk phases separated by internal layers, and with what distribution, is rarely addressed. Moreover, a detailed and systematic study, focused on the numerical realization of the asymptotics predicted laws, has never been reported before for multi-phase-field models. In the current article, endorsing against these undesirabilities of the common presentations, a full-fledged asymptotic analysis of a multi-grain-growth phase-field model is put forth and numerically verified. However, the consideration is only limited to the analysis of binary interfaces; that of junctions (triple points, quadruple points, etc.) is deferred to a later work.

INTERDEPENDENCE OF HAND PLASTICITY AND ABILITY TO CONTROL SPATIAL CHARACTERISTICS OF MOVEMENTS AMONG SCHOOLCHILDREN

The data obtained indicate that schoolchildren of 7-8th classes find it difficult to equally control over the movements with right and left hands in space. Based on the analysis of data pertaining to the plasticity of upper limb movements a low level of parameters studied was disclosed. The correlation features between the accuracy of movements in the elbow, radiocarpal and metacarpal joints of the right and left hands with two types of wave movements were investigated. In some cases, a weak connection was established between the “hand wave” parameters and the metacarpals kinematometry.

Comparative evaluation of incidence of dentinal defects after root canal preparation using hand, rotary, and reciprocating files: An ex vivo study

Aim: To compare dentinal cracks incidences after root canal preparation using Hand K-files; Rotary files: Protaper NEXT (PTN), Hyflex Controlled Memory (CM); and Reciprocating files: Wave One and Wave One Gold files. Materials and Methods: Hundred extracted human mandibular first premolars with straight root canals were taken postextraction and were allocated randomly into five groups (n = 20 teeth per group). The canals were instrumented using Hand K-files, Protaper Next (PTN), Hyflex CM, Wave One, and Wave One Gold systems. Group 1-Control group comprised of canals prepared by Hand files. Further, the roots were sectioned at three levels: 3, 6, and 9 mm horizontally from the apex using a saw at a low-speed saw and under water cooling. These slices were examined under a stereomicroscope at 25× magnification and the presence of any dentinal cracks was noted. The frequency of cracks was presented in number and percentage. Intergroup comparison of crack frequency was performed using analysis of variance. Results: No cracks were observed in the control group. In all the other groups, dentinal cracks were observed. The rotary files PTN and Hyflex CM files produced significantly fewer dentinal cracks than the reciprocating Wave One and Wave One Gold files (P < 0.05). The reciprocating files significantly produced more cracks apically than the rotary files (P < 0.05). Analyses were performed on SPSS software (windows version 22.0). Conclusion: Instrumentation with file systems can cause crack formation in the dentinal wall of the root canal. Determination of which files to choose for root canal preparation is technique sensitive and each instrument type has own benefits. However, care should be taken while biomechanical preparation to avoid dentinal cracks formation.

Noncontact button operation system using hand gestures

Recently, many noncontact sensors are intended for facilities that require hygiene to be considered. However, there is a problem in that noncontact sensors could malfunction unintentionally. In addition, there is a demerit in that noncontact sensor is not possible to perform complicated operations with one sensor because it would be limited to the simple operation of turning on when hold hand over it. In this paper, we examine the principle of a noncontact sensor that responds only to the action of waving in front of the sensor as an intentional action and does not malfunction. Additionally, since there are large and small hand waving methods, hand waving alone could be used for multiple signals. Experiments show that the proposed system is to realize not only the states of ON and OFF but also various operations with a single noncontact button.

Beyond Hand Waving: Indices For Hymn Leading in Corporate Christian Worship

The paper examines the significance of the selection, planning, and factors that affect the leading and singing of hymns in corporate Christian worship. While myriads of scholarly literature abound on hymnology, through bibliographies and an in-depth library search, the paper seeks to discuss the guidelines that engender the leading of congregation hymns and methods required for a dynamic leading of hymns. The research findings reveal that in some Nigerian churches where there are no trained music ministers, many untrained song leaders merely stand before the congregation to announce the hymns for the congregation to sing without performing a leadership role. Others merely stand to wave a hand. Leading congregational hymns requires training and vivaciousness in skill application. The three major areas of focus include planning, leading, and congregational response to hymn singing. The paper concludes that planning and selection of hymns are both spiritual and intellectual exercises that involve basic knowledge of the rudiments of music. The research will help the church musicians and academics in further research into church music and congregational hymn singing. Keywords: Song Leader, Hymn, Hymnody, Worship, Corporate Christian Worship

COVID-19

COVID-19

Understanding Sunflower syndrome: Results of an online questionnaire

ObjectiveTo characterize the clinical phenotype, treatments, and impact on quality of life of Sunflower syndrome. MethodsA 138-question survey was created focusing on seizure description, disease course, treatment history, medical history, family history, and aspects of quality of life of individuals with Sunflower syndrome. The survey was administered to individuals with Sunflower syndrome who experience hand waving episodes (HWE) and/or their caregivers via Research Electronic Data Capture (REDCap). ResultsSixty-eight responses were included in analysis. Seventy-one% of respondents were female. The mean age of participants was 13.6 years, with 84% of respondents under the age of 18. The average age of onset of HWE was 6.7 years. HWE frequency varied from a few episodes per week to multiple episodes per hour. Sixty-two% of participants experienced other seizure types. Participants had been on an average of 1.9 anti-seizure medications with varying efficacy. Other methods to reduce HWE included wearing a hat or sunglasses, hand holding, using special tinted lenses, and avoiding the sun and bright lights. Sixty-nine% of participants reported anxiety or depression related to their epilepsy, and 65% said their HWE affected their social life. SignificanceSunflower syndrome is a highly stereotyped, refractory epilepsy which significantly impacts the lives of affected individuals. It remains underrecognized and poorly understood. These results characterize Sunflower syndrome in a large population of affected individuals and provides a basis for future research to better understand the epilepsy and improve clinical care.

Ictal EEG in sunflower syndrome: Provoked or unprovoked seizures?

Sunflower syndrome (SS) is a rare, photosensitive epilepsy characterized by an attraction to light and highly stereotyped seizures with associated hand-waving (HW). It is controversial whether HW is part of the seizure or a provoking factor; therefore, we aimed to characterize the ictal electroencephalogram (EEG) in patients with SS.Video-EEG (vEEG) and charts of five patients with SS with HW during vEEG from Massachusetts General Hospital's Pediatric Epilepsy Program were reviewed and analyzed.In four out of five patients, the ictal EEG showed high amplitude (500–700 μV) 3–4 Hz generalized spike/polyspike-and-slow wave discharges, lasting 1.63–24.41 s.One hundred and twelve of 126 HW episodes, correlating to epileptiform activity (vEEG), had a lag time of less than 1.00 s (88.89%) between onset of HW and appearance of epileptiform activity. This suggests that HW does not induce seizure activity.Awareness of the ictal EEG features of this syndrome is important, as patients are frequently described as “self-inducing” their seizures.

LEVERAGING THE INTERNET OF THINGS BASED ON THE ANALYTICAL STUDY OF THE SKELETON JOINT POSITION IN RECOGNISING THE QUALITY OF HUMAN ACTIVITY

To give consequently dissecting and identifying human exercises to offer better help in the medical services area, security reason and so on Strategy: We have utilized UTKinect-Action 3D dataset containing the Position of 20 body joint caught by Kinect sensor. We chose two arrangement of joints J1 and J2; after that, we have shaped a few principles for movement grouping then we have applied SVM classifier, KNN classifier utilizing Euclidean distance and KNN classifier using Minkowski distance for action order. At the point when we have been used joint set J1 we got 97.8% exactness with SVM classifier, 98.8% precision with KNN classifier utilizing Euclidean distance, and 98.9% exactness with KNN classifier utilizing Minkowski distance and for joint set J2 we got 97.7% exactness with SVM classifier, 98.6% exactness with KNN classifier using Euclidean distance, and 98.7% exactness with KNN classifier utilizing Minkowski distance. We have arranged four exercises hand waving, standing, sitting and picking. In future, more exercises can likewise be remembered for this examination. IoT alongside this action acknowledgement strategy can be utilized to lessen overheads

Microstructured Multilayered Surface-Acoustic-Wave Device for Multifunctional Sensing

A multifunctional sensor based on a surface-acoustic-wave (SAW) device is fabricated. It allows independent measurements of temperature and applied magnetic field. Optimization of the multilayered device structure leads to a temperature-coefficient frequency of the Love-wave resonance reduced to zero. The sensitivity to an applied magnetic field is obtained through magnetostriction of a $\mathrm{Co}$-$\mathrm{Fe}$-$\mathrm{B}$ layer. By use of shape anisotropy, the variation of intrinsic magnetic anisotropy with temperature is strongly reduced. On one hand, interrogating the device at the Love-wave-resonance frequency allows us to extract the applied magnetic field independently of the temperature in a [130--370 K] range. On the other hand, the Rayleigh or Leaky waves are less or not sensitive to applied fields but have a high temperature coefficient of frequency. So interrogating the device at the Rayleigh resonance frequency allows us to extract the temperature independently of the magnetic field. In addition, the used resonator geometry offers the possibility for future batteryless and wireless interrogation.

RETRACTED ARTICLE: Construction and evaluation of the human behavior recognition model in kinematics under deep learning

To explore the construction and evaluation of the human behavior recognition model in kinematics by deep learning, the convolution neural network (CNN) in the field of deep learning was applied to build the CNN human behavior recognition algorithm model. The image data were collected from the KTH and Weizmann datasets and trained; then, the proposed algorithm was simulated by the TensorFlow platform. The results suggested that in the analysis of recognition effect of kinematic description in two datasets, the accuracy of the histogram of optical flow orientation (HOF) method was the worst in both KTH and Weizmann datasets, while the accuracy of the constructed Visual Geometry Group-16 (VGG-16) algorithm model was the highest. In the analysis of the accuracy of the KTH dataset, the boxing action had the highest accuracy of recognition, and the running action had the lowest accuracy of recognition. The average recognition value of all kinds of actions was 91.93%. In the precision analysis of the Weizmann dataset, the bending and hand waving actions had the most accurate recognition rate, while the running action had the lowest recognition rate.

Nondestructive Transmission Tomography of Pipeline Surface Defects

Based on results of theoretical analysis, a new method of transmission tomography of pipeline surface defects using ultrasound waves propagating along the pipeline walls is proposed. The waves are excited by a source and received by sensors that are located on ring-shaped lines and spaced along the tube surface. These waves propagating along the pipe surface represent either left or right handed rectilinear cylindrical waves like torsion waves. When a defect arises on the pipeline surface, the resulting waves are accompanied by changes in their shadow projections at the controlled pipe end. The problem of defect tomography is to determine locations and forms of defects using the backward projections. The physical feature of this method is that unlike traditional tomography, these projections arise on mutually intersecting left and right handed trajectories. The problem is solved by multiple joining of cylindrical pipe sections unfolded onto the plane in which all wave paths become rectilinear. A number of examples given in the paper confirm the possibility of restoration of locations and forms of various defects by the proposed method.

Handshake habit amongst medical practitioners, need to abandon and embrace an alternative: analytical study in view of COVID-19 pandemic

Background: Doctors are often ignorant of safeguarding their own health. The present COVID-19 pandemic has necessitated maintenance of social distancing. Handshake is one of the forms of greeting and physical contact to show empathy to patient, however it carries risk of transfer of microbes. The need of the hour is to find a suitable alternative to handshake. Aims was to study the knowledge, attitude and practice of medical doctors in an Indian tertiary care hospital towards hand shake.Methods: Study targeted 500 medical practitioners in a tertiary care hospital by presenting them with a google based questionnaire form. The questions were based on the practice of handshake amongst medical practitioners and the preferred alternatives. Statistical analysis used: Descriptive and analytic statistics.Results: Despite 100% respondents being aware about guidelines to avoid non-essential physical contact, 37.3% respondents shook hands. Out of the 85 respondents who had a handshake only 57.6% (n=49) washed hands after handshake. Namaste or Aadab followed by wave gesture are the most acceptable alternatives to handshake for medical practitioners.Conclusions: According to the study a large number of medical practitioners find it difficult to completely quit handshakes, mostly because of habit even in this pandemic emergency. Namaste or Aadab or hand waving are the best alternatives to handshake. A long term change of habit to a universally acceptable “wave” gesture needs to be developed to prevent possible community transmission of infectious diseases.

Thermal core profiling as a novel and accurate method for efficient characterization of oil reservoirs

The paper describes a new approach for obtaining detailed experimental data on the thermal properties of hydrocarbon reservoirs using continuous non-contact non-destructive high-resolution thermal conductivity and volumetric heat capacity profiling throughout the borehole core samples. The approach can offer representative experimental data on multi-scale heterogeneity of reservoirs and their anisotropy. Combination of the new approach with traditional well-logging methods offers more detailed data on variations of porosity and density of rock, its mineral composition, elastic wave velocity, and physical properties of the rock matrix. The work illustrates a case study of the new method application: continuous non-destructive non-contact high-resolution profiling of thermal properties was carried out for approximately 3000 core samples from two boreholes drilled through Carboniferous-Permian strata of the Usinskoye heavy oil field. The profiling provided detailed data on the thermal conductivity zoning, volumetric heat capacity, the coefficient of thermal anisotropy and heterogeneity factor of the rock. The knowledge of these properties is essential for hydrodynamic simulation of the heat and mass transfer processes in the reservoir when conducting thermal enhanced oil recovery. The experimental results revealed considerable variations in the set of thermal properties along boreholes on micro and macro scales. This suggests new opportunities for in-depth analysis of reservoir heterogeneity when studying the geological structure of the oil field. The work shows that application of thermal core profiling can provide details (unreachable for standard well logging techniques) of porosity variations and establish new correlations between thermal conductivity, on the one hand, and density and elastic wave velocity, on the other. That promises to improve the quality of geo-mechanical and geothermal investigations.

Robust Human Action Recognition System via Image Processing

Human actions detection is very much investigated in utilization of artificial intelligence and computer vision. Numerous effective action recognition strategies have demonstrated and the action information are successfully gained from motion videos and still pictures. In order to get the equivalent actions, the proper activity information gained from various kinds of media like videos or pictures might be connected. The majority of the existing video activity action identification strategies experience the ill effects of inadequate marked recordings. In that cases, over-fitting should be a potential issue and the execution of activity acknowledgment is controlled. In this paper, image processing techniques are used in order to recognize the different hand poster of the human body, also the over-fitting can be eased and the execution of activity acknowledgment is improved. Initially, the human action video including hand waving, walking, jogging, clapping, boxing is converted into image of 2D frames and then it is preprocessed followed by feature extraction using LST and classification by KNN classifier has been done individually. The kernel principal component analysis (KPCA) technique is used in the proposed system for finding the image features and joined features.The extracted features from the frames are compared with trained quantized dataset in order to identify the actions. The advantage of quantized dataset is that it occupies very less space. Thus, the result shows which action is present in the examined data. Trials on open benchmark data sets and genuine world data sets demonstrate that our technique outflanks a few other cutting edge activity acknowledgment strategies.