Data Science Methods and Tools for Industry 4.0: A Systematic Literature Review and Taxonomy.

The Fourth Industrial Revolution, also named Industry 4.0, is leveraging several modern computing fields. Industry 4.0 comprises automated tasks in manufacturing facilities, which generate massive quantities of data through sensors. These data contribute to the interpretation of industrial operations in favor of managerial and technical decision-making. Data science supports this interpretation due to extensive technological artifacts, particularly data processing methods and software tools. In this regard, the present article proposes a systematic literature review of these methods and tools employed in distinct industrial segments, considering an investigation of different time series levels and data quality. The systematic methodology initially approached the filtering of 10,456 articles from five academic databases, 103 being selected for the corpus. Thereby, the study answered three general, two focused, and two statistical research questions to shape the findings. As a result, this research found 16 industrial segments, 168 data science methods, and 95 software tools explored by studies from the literature. Furthermore, the research highlighted the employment of diverse neural network subvariations and missing details in the data composition. Finally, this article organized these results in a taxonomic approach to synthesize a state-of-the-art representation and visualization, favoring future research studies in the field.

IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories.

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Rapid, Noninvasive, and Nondestructive Method for Biofilm Imaging on Metallic Surfaces Using Active Thermography.

A simple, rapid, low-cost method was proposed for the imaging of Pseudomonas aeruginosa biofilms on metallic surfaces using an infrared camera. Stainless steel coupons were cooled to generate a thermal gradient in relation to biofilm for active thermography (AT). Both cooling and image acquisition times were optimized and the images obtained with AT were compared with those from scanning electron microscopy. A free software (Thermofilm) was developed for image processing and the results were compared with the software ImageJ, with good agreement (from 87.7 to 103.8%). Images of coupons treated with sanitizer (peracetic acid) were obtained to show the applicability of the proposed method for biofilm studies. All analytical steps could be performed in 3 min in a noncontact, nondestructive, low-cost, portable, and easy-to-use way.

Applied Computing to Education on Noncommunicable Chronic Diseases: A Systematic Mapping Study.

Background: The number of deaths from noncommunicable chronic diseases (NCDs) has increased worldwide. These deaths would be partly avoidable if prevention and follow-up measures were applied to reduce risk factors. Computing can help educate individuals, improving their knowledge about NCDs. This article presents a systematic mapping of studies that apply computing to education on NCDs. The results allow a general view of the literature and the identification of research opportunities. Materials and Methods: The methodology followed three steps: (1) definition of search databases from computer science and health sciences, (2) selection of keywords for search string composition, and (3) application of inclusion and exclusion criteria to filter the results. The survey occurred from January 2008 to April 2018. Results: The initial search resulted in 19,675 papers, of which 38 were selected after applying the filter criteria. The use of mobile computing stood out in 25 papers. The education modalities were self-management and educational content for diabetes (10 papers), asthma (1), cardiovascular disease (1) and chronic diseases (1), self-management of diabetes and games (1), educational content (15), games (6), personalized content (2), and virtual community (1). The percentage of papers on diabetes was 65%. Most solutions (55%) do not use data from individuals to provide information considering their health condition. In addition, 19 papers produced outcome measures by means of experiments. Conclusions: Mobile computing was the most used technology in the papers. In addition, self-management, educational content, and games were the most used mechanisms. A research opportunity consists of personalized assistance. In this sense, ubiquitous learning can provide a continuous and contextualized education.

A Mapping Study on Mobile Games for Patients of Chronic Diseases.

There is a growing interest of using technologies to propose solutions for healthcare issues. One of such issues is the incidence of chronic diseases, which are responsible for a considerable proportion of worldwide mortality. It is possible to prevent the development of such diseases using tools and methods that instruct the population. To achieve this, mobile games provide a powerful environment for teaching different subjects to user, without them actively knowing that they are learning new concepts. Despite the growing interest of using mobile games in healthcare, more specifically by patients with chronic diseases, in the best of our knowledge there are no studies that address the current research being published in the area. To close this gap, we carried out a systematic mapping study to synthesize an overview of the area. Five databases were searched and more than 1200 studies were analyzed and filtered. Among them, 17 met the the inclusion and exclusion criteria defined in this work. The results show that there is still room for research in this area, since the studies focus on a younger audience rather than proposing solutions for all ages. Furthermore, the number of chronic conditions being addressed is still small, obesity and diabetes are prevalent. Besides, the full capacity of game features that foster learning through games are not being employed, the majority of games proposed by the articles encompass less than half of these features.

Ubiquitous computing in light of human phenotypes: foundations, challenges, and opportunities.

The interest in human phenotypes has leveraged interdisciplinary efforts encouraging a better understanding of the broad spectrum of psychological and behavioral disorders. Moreover, the usage of mobile and wearable devices along with unobtrusive computational capabilities provides an extensive amount of information that allows the characterization of phenotypes. This article describes the human phenotype through the lens of computational range and reviews state-of-the-art computational phenotyping. Furthermore, the article discusses computational phenotyping's extension concerning the combination of intelligent environments and personal mobile devices, addressing technical, managerial, and ethical challenges. This combination reinforces ubiquitous computational capabilities for phenotyping as a facilitator of interdisciplinary information convergence in favor of clinical and biomedical research.

Posture monitoring in healthcare: a systematic mapping study and taxonomy.

Palliative treatments for back pain usually include exercise, analgesics, physiotherapy, prostheses, and surgery in severe cases. Technologies for postural monitoring are growing, and they are important in preventing back pain and mitigating permanent damage. Remote work, especially after the COVID-19 pandemic, made people spend more time than usual in chairs and environments not certified by the health aspects of work. This research investigated through a Systematic Mapping Study (SMS) contributions in posture monitoring for healthcare in smart environments, including the different methods to obtain the posture, the limitations, and the target audience of the proposed models. The SMS was conducted in eight databases, including articles from January 2012 to March 2022. The initial search yielded 3161 articles, of which 34 were selected after applying the filtering criteria. Moreover, this study presents the challenges related to posture behavior monitoring, identifying studies and implementations that apply assistive technology for postural monitoring and improving the health and life of remote workers. In addition, three commercial postural devices are presented, and what challenges they currently face. Regarding healthcare, results showed a prevalence of using the Internet of Things (IoT) devices such as wireless sensor networks and inertial measurement unit (IMU) sensors. This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis.

Breast cancer dataset with biomarker Biglycan.

This dataset is composed of photomicrographs of the immunohistochemical expression of Biglycan (BGN) in breast tissue, with and without cancer, using only the staining of 3-3' diaminobenzidine (DAB), after processing images with color deconvolution plugin, from Image J. The immunohistochemical DAB expression of BGN was obtained using the monoclonal antibody (M01) (clone 4E1-1G7 - Abnova Corporation, mouse anti-human). Photomicrographs were obtained, under standard conditions, using an optical microscope, with UPlanFI 100x objective (resolution: 2.75 mm), yielding an image size of 4800 × 3600 pixels. After color deconvolution, the dataset with 336 images was divided into 2 two categories: (I) with cancer and (II) without cancer. This dataset allows the training and validation of machine learning models to diagnose, recognize and classify the presence of breast cancer, using the intensity of the colors of the BGN.

Inhibitory control stimulation in elementary school children through digital games: A systematic mapping study.

The inhibitory control is a component of the executive functions that allows the individual to inhibit inadequate behaviors, resist distractions and select a relevant stimulus when executing activities. In the neuropsychology field, evidences of stimulation and improvement of the inhibitory control through school interventions is sought by using computerized software, such as digital games. These research studies constitute an important investigation area within the executive functions in ecological approaches. This paper presents a systematic mapping study on inhibitory control stimulation in elementary school children with the use of digital games. The investigation encompassed an automated database search with further backward snowballing procedure with the final selection for additional publications as research strategy. The automated search considered six databases: SCOPUS, PubMed, IEEE Explore Digital Library, ACM Library, Springer Link, and Scielo. The initial database search found 641 works published between 2014 and 2019. After the exclusion and inclusion criteria were considered, three publications related to digital games or mobile applications were found and selected for analysis, which focused on inhibitory control or correlated processes stimulation in school-based interventions with elementary school children. Results indicated that investigations within the field are incipient, pointing to an emerging research area.

The application of parallel processing in the selection of spectral variables in beer quality control.

Parallel data analysis was investigated to improve performance in variable selection and to develop predictive models for beer quality control. A set of spectral near infrared (NIR) data from 60 beer samples and its primitive extracts as the original concentration was used. The dataset was distributed to Raspberry Pi 3 Model B devices connected to a network that was running a Machine Learning service. With more than 4 devices acting in parallel, it was possible to reduce time in 57% to find the best linear regression coefficient (0.999) with the lower RMSECV (0.216) if compared to a singular desktop computer. Thus, parallel processing can significantly reduce the time to indicate the best model fitted during the variable's selection.

A computational model for adaptive recording of vital signs through context histories.

Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

Development and testing of iAware model for ubiquitous care of patients with symptoms of stress, anxiety and depression.

BACKGROUND AND OBJECTIVE: This work proposes a model for ubiquitous care of patients with anxiety, depression and stress disorders using gamification and biodata, called iAware. Depression and anxiety are the most prevalent mental disorders, reaching million people worldwide. As they share many characteristics these two disorders can manifest themselves together. In addition, stress is one of the related factors with both depression and anxiety, being relevant in the analysis of these disorders. This work was carried out through a study on depression, anxiety and stress disorders (DASD), their treatments and the use of gamification as a means of engagement. METHODS: A/B tests evaluated with a clinical population the interaction engagement of the patient to the treatment provided by gamification. The iAware monitors and applies interventions for the patient at the most appropriate time, based on the patient's history. In order to evaluate iAware, six patients used a prototype with a smartband for two weeks. The patients also filled out a survey based on the Technology Acceptance Model (TAM). The survey was composed of 10 sentences and the results of each one are discussed. RESULTS: Interactions with intervention stages were greater in patients who used iAware gamified. The patients who used iAware got more occurrences of anxiety at home and in the afternoon and night. TAM evaluation showed that patients considered the use of iAware useful in their anxiety treatment routine. CONCLUSIONS: The results pointed out that biodata is a supplementary alternative for DASD monitoring. In addition, the research work showed that the use of iAware for the support of anxiety treatment is useful for patients. Patients who used iAware without gamification did not perform or score the treatment activities. The evaluation showed evidence that the game improved the engagement of patients in the iAware use.

A model for ubiquitous care of noncommunicable diseases.

The ubiquitous computing, or ubicomp, is a promising technology to help chronic diseases patients managing activities, offering support to them anytime, anywhere. Hence, ubicomp can aid community and health organizations to continuously communicate with patients and to offer useful resources for their self-management activities. Communication is prioritized in works of ubiquitous health for noncommunicable diseases care, but the management of resources is not commonly employed. We propose the UDuctor, a model for ubiquitous care of noncommunicable diseases. UDuctor focuses the resources offering, without losing self-management and communication supports. We implemented a system and applied it in two practical experiments. First, ten chronic patients tried the system and filled out a questionnaire based on the technology acceptance model. After this initial evaluation, an alpha test was done. The system was used daily for one month and a half by a chronic patient. The results were encouraging and show potential for implementing UDuctor in real-life situations.