Augmented Human Research最新文献

{"title":"Detecting Neuromuscular Disorders Using EMG Signals Based on TQWT Features","authors":"Agya Ram Verma,&nbsp;Bhumika Gupta","doi":"10.1007/s41133-019-0020-7","DOIUrl":"10.1007/s41133-019-0020-7","url":null,"abstract":"<div><p>Neuromuscular disorders are characterized by abnormal functioning of muscles and nerves that communicate with the brain, resulting in muscle weakness and ultimately damage to nervous control, for instance amyotrophic lateral sclerosis (ALS) and myopathy (MYO). Diagnosis of these disorders is frequently done by examining ALS, MYO and normal electromyography (EMG) signals. In the present work, an efficient technique that involves wavelet transform using tunable-Q dynamics (TQWT) is proposed in order to identify disorders related to the neuromuscular domain of EMG signals. The EMG signal is decomposed by the TQWT technique into sub-bands, and these sub-bands are used to determine spectral features including spectral flatness, spectral stretch and spectral decrease, and statistical features including kurtosis, mean absolute deviation, and interquartile range. The extracted features are used as inputs into extreme learning machine classifiers in order to identify and analyze EMG signals associated with neuromuscular dysfunction. The results achieved with this technique illustrate a much better classification with regard to neuromuscular disturbance in electromyogram signals when compared with previous methods.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0020-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50053996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue Detection Using Artificial Intelligence Framework","authors":"Vidhi Parekh,&nbsp;Darshan Shah,&nbsp;Manan Shah","doi":"10.1007/s41133-019-0023-4","DOIUrl":"10.1007/s41133-019-0023-4","url":null,"abstract":"<div><p>Technological advances in healthcare have saved innumerable patients and are continuously improving our quality of life. Fatigue among health indicators of individuals has become significant due to its association with cognitive performance and health outcomes and, is one of the major factors contributing to the degradation of performance in daily life. This review serves as a source of studies which helped in better understanding of fatigue and also gave significant detection methods and systematic approaches to figure out the impacts and causes of fatigue. Artificial intelligence was turned out to be one of the essential tactics to detect or monitor fatigue. Artificial neural network, wavelet transform, data analysis of mouse interaction and keyboard patterns, image analysis, kernel learning algorithms, relation of fatigue and anxiety, and heart rate data examination studies were used in this paper to precisely assess the source, factors and features which influenced the recognition of fatigue.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0023-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50048777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design Spline Adaptive Filter with Fractional Order Adaptive Technique for ECG Signal Enhancement","authors":"Papendra Kumar,&nbsp;H. S. Bhadauriya,&nbsp;Agya Ram Verma,&nbsp;Yatendra Kumar","doi":"10.1007/s41133-019-0022-5","DOIUrl":"10.1007/s41133-019-0022-5","url":null,"abstract":"<div><p>In this work, a spline adaptive sieve is designed for recognizing Wiener-style non-constricted arrangement. The proposed filter has ductile lookup table along impulse response sieve whose characteristic is infinite where up-sampling is carried out for a sub-part of low-order polynomial. This process can be applicable pro ECG to get better results for recognizing the QRS complex. The presented approach is suitable to realize the precise converse model directly from simulation data with reduced complexity. Further, variable-order fractional least mean square (VOFLMS) scheme can be developed pro better accuracy recognition. In order to achieve rapid convergence speed and lower error, the VOFLMS method actively adjusts the request for the fragmentary subordinate on the inaccuracy function. Simulation outcomes verify the efficiency of the proposed filter scheme along VOFLMS non-constricted arrangement recognition. It is demonstrated that the VOFLMS can adapt nonlinearity more satisfactorily as compared to other reported schemes in the literature.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0022-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50027649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Buildout of Methodology for Meticulous Diagnosis of K-Complex in EEG for Aiding the Detection of Alzheimer’s by Artificial Intelligence","authors":"Rushikesh Pandya,&nbsp;Shrey Nadiadwala,&nbsp;Rajvi Shah,&nbsp;Manan Shah","doi":"10.1007/s41133-019-0021-6","DOIUrl":"10.1007/s41133-019-0021-6","url":null,"abstract":"<div><p>Application of artificial intelligence (AI) in health-care detection is a domain of exceptional research and interest in today’s world. And hence among this domain, a considerable inclination is toward creating a smart system that is AI for aiding identification of brain-related disease—Alzheimer’s—using electroencephalogram (EEG). Certain AI-based techniques as well as systems have been created for EEG examination and interpretation, but they have a common drawback that is lack of shrewdness and acuteness. Therefore, to overcome these drawbacks, a different methodology or technique is suggested in this paper which is able to mold the AI technique for better EEG Cz strip K-complex identification. This suggested method and structure of AI detection system is relied on quantitative scrutinization of Cz strip and embedding-established EEG explication principles for detection of K-complex and Alzheimer’s. This technique unconditionally relied on facts and information of neuroscience that are applied by expert in health care such as neurologist to create a detailed review of sick person’s EEG. The suggested technique also allots a potential of learning on its own to the AI so that it can apply the events in future examinations.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0021-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50010586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connecting and Controlling Appliances Through Wearable Augmented Reality","authors":"Vincent Becker,&nbsp;Felix Rauchenstein,&nbsp;Gábor Sörös","doi":"10.1007/s41133-019-0019-0","DOIUrl":"10.1007/s41133-019-0019-0","url":null,"abstract":"<div><p>The number of interconnected devices around us is constantly growing, and it may become challenging for users to control all these devices when control interfaces are distributed over mechanical elements, apps, and configuration webpages.\u0000If devices are even supposed to be connected and work together, this challenge is intensified. An alternative way for configuring and controlling devices in situ is enabled by wearable technologies. In this paper, we investigate interaction methods for smart appliances in augmented reality from an egocentric perspective. We examine how users can control appliances through augmented reality directly. The physical objects are augmented with interaction widgets, which are generated on demand and represent the connected devices along with their adjustable parameters.\u0000For example, a widget can be overlaid on a loudspeaker to control its volume. We explore three ways of manipulating the virtual widgets: (1) in-air finger pinching and sliding, (2) whole-arm gestures rotating and waving, (3) incorporating physical objects in the surrounding and mapping their movements to the interaction primitives. We compare these methods in a user study with 25 participants and find significant differences in the preference of the users, the speed of executing commands, and the granularity of the type of control. While these methods only apply to controlling a single device at a time, in a second part, we create a method to also take potential connections between devices into account. Users can view and configure connections between smart devices in augmented reality and furthermore can manipulate them or create new device connections using simple gestures. This facilitates the understanding of existing connections and their modification.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0019-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50024513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach of 2D Adaptive Filter Based on MPSO Technique for Biomedical Image","authors":"Bhumika Gupta,&nbsp;Agya Ram Verma","doi":"10.1007/s41133-019-0017-2","DOIUrl":"10.1007/s41133-019-0017-2","url":null,"abstract":"<div><p>In this paper, extension of the 1-D adaptive filter schemes to 2D formation and the new 2D adaptive filters are designed. The results of proposed scheme are compared with 2D variable step-size normalized least mean squares, the 2D VSS affine projection algorithms, the 2D set-membership NLMS, and 2D SM APA. The performance of proposed scheme is compared with other reported methods for 2D adaptive filter design. Based on simulation results, it is demonstrated that the proposed method can achieve 85% and 90% reduction in normalized mean square error and normalized maximum error mean, respectively. Moreover, the proposed 2D-ANC filter applied for reconstruction of a biomedical image shows 6 dB signal-to-noise ratio improved as compared to recently reported algorithm.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0017-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50045592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Awareness Aspects and Effects on Coordination and Information Sharing in the Domain of Software Engineering as CSCW: An Ethnographic Study","authors":"Simon Elias Bibri","doi":"10.1007/s41133-019-0016-3","DOIUrl":"10.1007/s41133-019-0016-3","url":null,"abstract":"<div><p>This study sets out to understand and explore awareness in software engineering as a form of collaborative work in terms of its entailment and implication. In doing so, it intends to answer the following two questions: What are the aspects of awareness that are most relevant to the domain of software engineering? In what ways does awareness affect coordination and information sharing among collaborating actors in the domain of software engineering? An ethnographic methodology was espoused in order to test existing theoretical perspectives and generalizations pertaining to cooperative awareness. The study shows that what constitutes awareness beyond what and where of work context is, to a great extent, contingent upon the requirements and objectives of each professional domain with respect to the nature and complexity of joint activities and the manner in which the collaborating actors interact with each other accordingly. This also depends on the role they play in cooperative endeavors. The aspects that are most relevant to the domain of software engineering include work-oriented information on each others’ activities, tasks as moment-by-moment work processes, general information of all collaborating actors, detailed information about each others’ attention and responsibility as to ongoing projects, changes to shared document repositories and workspaces, and the delicate interplay between individual and cooperative activities. Moreover, the study corroborates the importance of awareness information for successful and effective collaboration in the domain of software engineering in terms of coordination and information sharing. Accordingly, awareness affects coordination and information sharing in ways that it ensures that the contributions of collaborating actors are relevant to the coordinated work and their actions are evaluated according to the coordinated goals, reduces the effort associated with coordination and information sharing in terms of using CSCW synchronous and asynchronous technologies as well as the frequency of this use, keeps the collaboration efforts to a minimum, enables to structure activities and avoid duplication of work and to fine-grain synergistic group and shared working behavior in relation to coordinated tasks, and acts as a foundation for (and predictor for) closer collaboration in terms of coordination and information sharing. In addition, in the domain of software engineering, the contextual conditions that determine the manner in which coordination and information sharing can be achieved are mostly personal, experiential, and/or professional. This study suggests that it is important to focus on awareness as a set of theoretical perspectives and to support technologies pertaining to work practice in relevance to the domain when designing and evaluating CSCW systems in order to achieve more effective and efficient outcomes in terms of coordination and information sharing as social processes.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0016-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50027430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effects of Whole-Body Vibration (WBV) Evaluated Using Cognitive Brain-Training App Games on Tablet or Cell phone for Both Genders","authors":"Herbert Câmara Nick,&nbsp;Maria Lucia Machado Duarte,&nbsp;Pedro Vieira Xavier","doi":"10.1007/s41133-019-0015-4","DOIUrl":"10.1007/s41133-019-0015-4","url":null,"abstract":"<div><p>Advances in the technology of mobile devices and wireless computers have transformed the way individuals communicate, making people’s use of such devices very frequent while traveling by means of transport. Vibrations are found in a variety of human activities, such as travel, leisure and work activities, and therefore, exposure is unavoidable to the individual. In order to maximize the time during the trip, people try to keep their minds active with study or work activities or in search of socialization for the purpose of distraction or entertainment. This has sparked the interest of the scientific community in conducting studies focusing on how this interaction of the individual with the cellular can affect cognitive performance. The present article intends to constitute an analysis on the effects of whole-body vibration exposure on cognition in university students assessed through the use of a game application for cognitive brain training on a cell phone or tablet for both genders. The aim of this work is to present the results of the influence of whole-body vibration on cognition in different variables (gender, type of device used and frequency of exposure) as a contribution to scientific and technological research. Forty people, being 20 females and 20 males, participated in the experimental test, having to exercise their cognitive abilities through the use of an application for brain-training games. It was based on traffic light images. When the green light appeared, the subjects had to click as quick as possible on the screen, being the processing speed the objective measure. The individuals were subdivided into groups to be able to verify the influence of the vibration according to the type of mobile device (smartphone or tablet) used for the game, gender of the volunteer (female or male) and whole-body vibration frequency (5 Hz or 30 Hz) and with 0.8 m/s² of amplitude. The WBV exposure duration was 10 min. After the exposure, participants remained at rest for 5 min while playing a new round of the game. In this way, it was possible to acquire data before and during exposure to vibration and following a period of rest after the exposure. Considering the collected data, a nonparametric statistical analysis (Mann–Whitney tests) was necessary. The results showed decay in the game score during the time with vibration in relation to the initial time without vibration. However, there is a tendency for recovery in the score obtained in the game during the time at rest after the exposure to the vibration in relation to the initial time at rest without vibration. Comparing the use of cell phone versus tablet, it is evident that for the same-gender group under the influence of vibration at the same frequency, better results are obtained with the use of tablet. With exposure to whole-body vibration at the 30 Hz frequency, volunteers presented better results than those subjected to the effects of whole-body vibration at 5 Hz freque","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0015-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49997362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Artifact Cancelation Based on Bacteria Foraging Optimization for ECG Signal","authors":"Agya Ram Verma,&nbsp;Yashvir Singh","doi":"10.1007/s41133-019-0014-5","DOIUrl":"10.1007/s41133-019-0014-5","url":null,"abstract":"<div><p>In this paper, the design of adaptive artifact canceler (AAC) filter using bacteria foraging optimization (BFO) algorithm is presented. The performance of proposed AAC filter is tested on a corrupted ECG signal. Based on simulation results, it is observed that the AAC filter designed with BFO technique achieves significant improvement in fidelity parameters such as SNR, NRMSE, and NRME when compared with other reported algorithms in the literature. AAC filter based on BFO technique provides 6 dB improvement in output SNR, 85% reduction in NRMSE, and 90% lower NRME as compared to recently reported AAC filter based on ABC-SF algorithm. Further, AAC filter using BFO technique enhances the coherence between pure and reconstructed ECG signals.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0014-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50047203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bringing Modern Machine Learning into Clinical Practice Through the Use of Intuitive Visualization and Human–Computer Interaction","authors":"Richard Osuala,&nbsp;Jieyi Li,&nbsp;Ognjen Arandjelovic","doi":"10.1007/s41133-019-0012-7","DOIUrl":"10.1007/s41133-019-0012-7","url":null,"abstract":"<div><p>The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.</p></div>","PeriodicalId":100147,"journal":{"name":"Augmented Human Research","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s41133-019-0012-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50082820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}