Digital Data Recording Research Articles

The Swiss Claw Health Project - an interim assessment after 5 years

The Swiss Claw Health Project (SCHP) started on 1 January 2019 and will last a total of 8 years. The main goals of the SCHP are (a) digital recording of claw health data of cattle in Switzerland by 100 professional claw trimmers, (b) development of claw health programs, (c) improvement of cattle claw health in Switzerland, (d) development of breeding values for claw health and (e) reduction of antibiotic use to control claw health problems. Data was collected digitally by 45 hoof trimmers who had previously successfully completed the training program for diagnosis and digital documentation in accordance with the ICAR Claw Health Atlas. The national animal health database was further developed for data storage. Livestock farms with unfavorable claw health were offered an on farm analysis with subsequent annual support visits. A total of 1155 cattle owners joined the project by the end of the 5th project year. The claw health data of 149753 claw trimmings at cow-level was recorded, and 176 individual visits and analyses of farms with claw health issues were carried out. A health program for mechanical and metabolism-related claw disorders was developed and successfully established. Biosecurity measures during claw trimming were analyzed, and a brochure with recommendations for claw trimmers was developed. Risk factors for the occurrence of digital dermatitis in dairy herds were identified. The prevalence of alarm lesions (painful lesions) decreased significantly and continuously in the participating farms over the project years 2 to 4. The scientific project support proved to be extremely useful, as the progress of the project was continuously monitored, and new issues could be addressed promptly. Digitally recorded findings during herd hoof trimming are essential for monitoring and improving claw health. This description and critical assessment of the SKGP procedure can be used by other consortia planning a similar project.

Developing an Android-Based Application Documentation System of Postpartum Care for Indonesian Midwifery Students

Midwifery students still take notes the old-fashioned way, on paper, which has become very inconvenient. Students struggle to create accurate and complete care documentation due to the risk of losing important data, transferring data, and scribbling. Mobile Case Midwifery Notes (CMNotes) is an Android-based documentation system designed to develop learning methods and interactive consultations between students and lecturers. CMNotes facilitates digital and comprehensive recording of patient data starting from assessment, diagnosis, and plan of care to implementation and evaluation by applying Varney’s management principles and Subjective Objective Analysis Planning (SOAP) documentation. This study is research and development (R&D) to produce a documentation system for midwifery care emphasising postpartum and breastfeeding care. The researchers recruited 185 midwifery students from Aceh Health Polytechnics to be involved in a needs analysis. Out of the total number of students, 39 students took part in the implementation stage where their experience using the application was assessed. The needs analysis revealed that postpartum care and breastfeeding are important subjects, and their documentation is the most important aspect. The implementation results show that the CMNotes provide convenience and satisfaction for students when documenting childbirth and postpartum care. Since CMNotes could provide the reference range of data, the system allows students to identify and focus on the issue experienced by the patients. Due to user feedback on the system’s usability, it is necessary to create an Android-based CMNotes application to fill in patient data more easily and effectively without the need for internet access.

Digitalisation in pre-purchase examination and prospects for an equine health database in Germany-Results of an online survey among equine veterinarians.

Digital recording and storage of health data are becoming increasingly important in the fields of both human and veterinary medicine. There is currently a lack of information on the level of digitalisation among equine veterinarians in Germany and their attitudes towards advanced digitalisation. To collect opinions of equine veterinarians about (1) digital data collection in pre-purchase examinations (PPEs), and (2) the establishment of an equine health database in Germany. Cross-sectional survey. An online survey was developed and distributed via email exclusively to equine veterinarians in Germany. After 40 days of data collection, answers were used for descriptive statistical analyses. The survey was sent to 1055 recipients, of which 147 (13.9%) participated and 130 survey responses from equine veterinarians could be used for analyses. Most respondents were working in an equine practice (77.9%) and performing PPEs (93.8%), for which they mostly used printed protocols (86.0%). The PPE protocol of the German Equine Veterinary Association as a paper print was the format most frequently used (47.1%). The majority of participating equine veterinarians (90.2%) would use a digital protocol for PPEs if available. Concerns were expressed about user-friendliness (55.9%) and integration into the daily veterinary routine (54.6%). Regarding the establishment of an equine health database, 72.3% of the respondents stated they would appreciate it. However, about one-third would support such a development only under certain conditions: guaranteed data safety, collection of objective data or involving the opinion of other stakeholders. Low response rate, potential non-response bias and wide range of question topics. A positive attitude towards digital PPEs and an equine health database in Germany became apparent. To facilitate further progress, it would be crucial to consider the concerns and conditions identified in this study to generate maximum compliance.

Core analysis in a changing world – how technology is radically benefiting the methodology to acquire, the ability to visualize and the ultimate value of core data

Abstract Core analysts principally study the storage, flow and saturation properties of porous rocks and sediments. Some of the derived parameters are specific to hydrocarbon production but many have commonality with other subsurface disciplines such as hydrology and soil science. Traditional core analysis involves direct physical experimentation on core plugs to derive a range of parameters used as calibration for conventional well logs, and to predict hydrocarbon reserves and recovery. The mechanisms and processes for obtaining such data have evolved significantly during the last century, from the manual instruments of the mid-twentieth century to the accredited digital data collection and recording of the 1990s onwards. X-ray micro- and nano-scale computed tomography (CT) imaging led to the development of the digital rock physics subdiscipline in the early 2000s. This has subsequently allowed direct visualization of fluid flow at the pore scale, imaging the wetting phase and multiphase fluid mobility. Multiscale imaging workflows are being developed to overcome issues around heterogeneous rock and the limited field of view associated with the highest resolution X-ray CT images. Hybrid workflows, which combine digital rock physics with traditional core analysis, are becoming increasingly common to meet the challenges associated with some of the most difficult to constrain properties, such as relative permeability. At a larger scale, the recent development of multisensor core logging (MSCL) tools has allowed the cost-effective acquisition of essentially continuous high-resolution 1D, 2D and 3D datasets from both slabbed and unslabbed whole core. Often aided by artificial intelligence to manage and interpret these large physical and chemical datasets, both new and legacy core can be rapidly screened to allow representative subsampling for detailed laboratory experimentation. The context and data provided by the MSCL then allows effective upscaling of these time- and cost-intensive point-source measurements. In the last decade, extended reality (XR) has resulted in a step change in the ability to visualize and integrate core and core-derived information with other subsurface datasets. A very wide range of scales can be managed effectively, from micrometre- to centimetre-scale petrographical and core analysis data, to metre-scale well logs and up to kilometre-scale 3D and 4D seismic. These tools allow stakeholders to work and meet from any location in a common workspace, and efficiently scale and interrogate data in a virtual 3D environment. The various advances in core analysis and associated technologies during the early twenty-first century mean that the study of porous media to help enable the energy transition looks assured. During the coming decades, applications as diverse as carbon capture, utilization and storage (CCUS), hydrogen storage, geothermal energy generation, mining for critical minerals, palaeoclimate studies, radioactive waste management, and site surveys for windfarms will all continue to benefit from the data and understanding derived from core analysis.

Assessment of fully digitalized workflow for implant-prosthetic rehabilitation in temporomandibular disorders patients: A clinical study

Digitalized workflow eliminates the need for the tray, impression materials, its decontamination, packaging and shipping, pouring with plaster, cast fabrication, mounting in an articulator, reducing storage spaces, and the risks of any loss or fracture of the plaster model is overcome by archiving on the computer. This clinical investigation aimed to evaluate the effectiveness of the fully digitalized rehabilitation [implant-supported prosthesis] method in partially edentulous patients and with TMD, using advanced software. Twelve patients requiring implant-supported prosthesis in the mandibular molar area with Temporomandibular disorders [TMD] were selected. The fully digitalized rehabilitation method with advanced software was used for rehabilitation. For each subject, Optical impressions, CBCT scan, and Digital recording of jaw movement data. Guided implant surgery and digitalized prosthetic rehabilitation; were performed. The effectiveness of the digitalized workflow was assessed by evaluating the changes in the joint symptoms before and after the end of the treatment, changes in the electromyographic tracings, the precision of the prosthetic artefact, assessed through the amount of chair adjustment operating time and the number of retouching/ modifications to be carried out before the completion of the work. The results showed that the mean operative time required in 12 patients was 9.42 min, significantly less than the time recorded in previous studies when the medium mean was 16.00 min. The mean number of touch-ups [adjustments] was less than 3, most of which were on the interproximal surfaces. There were no significant changes recorded in the electromyography tracings. There were also no changes in joint symptoms. It was found that this way of working was entirely reliable and significantly reduced operating times and the number of appointments. Digital flow is beneficial ei dysfunctional patients, not about improvements in temporomandibular symptoms but in times of operability and prosthetic retouching.

Technology-Enhanced Learning and Its Association with Motivation to Learn Science from a Cross-Cultural Perspective.

Fostering technology-enhanced science learning in elementary schools is an ongoing challenge as young students are not always motivated to engage with science lessons. The use of technology, such as digital sensors and data recorders, has been found to result in higher engagement with science. However, the association between technology-enhanced science learning and students' motivation to learn, from a cross-cultural viewpoint, is still discussed among researchers. Thus, the goal of this study was twofold: (a) to examine the motivation to learn science of elementary school students from different countries and cultural backgrounds; (b) to identify phases of technology-enhanced science learning and their association with students' motivation. Applying the sequential mixed-methods research design, data were collected via questionnaires, semi-structured interviews, and online observations. The study included seven experienced science teachers from the USA and Israel and 109 sixth-grade students: English speakers (N = 43), Arabic speakers (N = 26), and Hebrew speakers (N = 40). The findings indicated differences in students' internal motivation, in terms of "interest and enjoyment," "connection to daily-life," and "cross-cultural interactions," with medium ratings for "self-efficacy." The study identified and characterized two consecutive phases of technology-enhanced science learning-"divergence" and "convergence"-that can be associated with motivation to learn science. Overall, the study's results highlight the importance of seamlessly embedding technology to support cross-cultural learning of scientific practices.

Implementation of Web-Based Case Midwifery Notes Documentation System (CMNotes) for Intra Natal Care

Case Midwifery Notes (CMNotes) is a digital-based documentation system designed to develop learning methods and interactive consultations between students and lecturers. CMNotes facilitates digital and comprehensive recording of patient data starting from assessment, diagnosis, plan of care, implementation to evaluation by applying Varney's management principles and SOAP documenta-tion. This study aims to design produce a documentation system for midwifery care emphasized on the Intranatal care. The app was developed using php and html followed by hosting via wordpress. The data was collected using pssuq instrument (usability assessment), need analysis instrument and student responses questionnaire. 63 students from Midwifery student of Health Polytechnic Aceh were involved in the implementation of the system. The implementation results show that overall score of the system usability measured using PSSUQ is 2.87 indicating that the CMNotes system provides convenience and satisfaction for students in making documentation of childbirth care. Due to comments of users regarding the practi-cality of system, it is necessary to develop an Android-based CMNotes application to be able to fill in patient data more easily and effectively without the need of internet access.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

Data-Driven Derivation of Sheet Metal Properties Gained from Punching Forces Using an Artificial Neural Network

The ongoing digitization of production processes provides new possibilities and potentials for process monitoring of forming and stamping processes. The component quality achievable by these processes is strongly dependent on the properties of the sheet metal material, so that a permanent digital recording of material data offers high potential for monitoring each component produced. In this context, presented paper deals with a novel AI-based method for the direct determination of ma-terial parameters from measured punching force curves. Using software systems Python and Tensor-Flow, an artificial neural network was first set up to determine mechanical material parameters (out-put data) from punching force curves (input data). As data basis for the adopted neural network, force curves were measured during punching of various sheet metal materials using a punching tool equipped with a direct force measurement device. Punching force curves were experimentally deter-mined for the sheet metal materials DP1200, DP1000, DP800, DP600, HX380LA, DC03 and DX54. Additionally, tensile tests were performed for these sheet metal materials to determine ultimate tensile strengths (Rm), yield strengths (Rp0.2, Re), uniform strains (Ag), elongations at break (At) and strain hardening exponents (n). The presented paper reveals that neural networks can accurately quantify the relationship between characteristic parameters of punching force curves and the mentioned me-chanical material properties.

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging.

Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.

PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations.

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

On Hedgehogs and Marvelous Minds

ABSTRACTThe collection of 3D point data is a common bottleneck for archaeological excavations despite an increasing range of powerful spatial data collection technologies. Total stations often require a dedicated operator, and they are optimal for excavation-level data collection over relatively short line-of-site distances. Precision Global Navigation Satellite Systems (GNSS) require reliable communication with constellations of distant satellites and may not be accurate enough for all data recording contexts. A new category of spatial data collection hardware, called Indoor Positioning Systems (IPS), or “indoor GPS,” has the potential to provide a more cost-effective and efficient approach to the collection of point data during excavations by making 3D point data collection widely available and accessible. Additionally, such systems may allow greater detail in digital field data recording by enabling the collection of shape data via continuous recording. In this article, we present one such IPS system—the Marvelmind IPS—discuss its potential value and limitations, and provide a case study of a field test of the system at the Chalcolithic (4600–3600 BC) site of Horvat Duvshan, Israel.

An incremental clustering method for anomaly detection in flight data

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has gained interest for proactive safety management. New anomaly detection methods, primarily clustering methods, have been developed to monitor pilot operations and detect any risks from such flight data. However, all existing anomaly detection methods are offline learning — the models are trained once using historical data and used for all future predictions. In practice, new flight data are accumulated continuously and analyzed every month at airlines. Clustering such dynamically growing data is challenging for an offline method because it is memory and time intensive to re-train the model every time new data come in. If the model is not re-trained, false alarms or missed detections may increase since the model cannot reflect changes in data patterns. To address this problem, we propose a novel incremental anomaly detection method based on Gaussian Mixture Model (GMM) to identify common patterns and detect outliers in flight operations from digital flight data. It is a probabilistic clustering model of flight operations that can incrementally update its clusters based on new data rather than to re-cluster all data from scratch. It trains an initial GMM model based on historical offline data. Then, it continuously adapts to new incoming data points via an expectation–maximization (EM) algorithm. To track changes in flight operation patterns, only model parameters need to be saved, not the raw flight data. The proposed method was tested on three sets of simulation data and two sets of real-world flight data. Compared with the traditional offline GMM method, the proposed method can generate similar clustering results with significantly reduced processing time (57 %–99 % time reduction in testing sets) and memory usage (91 %–95 % memory usage reduction in testing sets). Preliminary results indicate that the incremental learning scheme is effective in dealing with dynamically growing data in flight data analytics.

RANCANG BANGUN SISTEM PENGIDENTIFIKASI TRAVEL BAG PADA KELOMPOK BIRO PERJALANAN UMROH/HAJI BERBASIS WEB

Perkembangan Teknologi Informasi salah satunya bergerak di bidang jasa Perjalanan Umrah. Salah satunya pencatatan yang dilakukan adalah proses pencatatan dan identifikasi terhadap kepemilikan tas peserta umrah atau haji. Pencatatan yang dilakukan secara manual atau diatas kertas akan menyulitkan proses identifikasi tas karena beresiko besar untuk rusak maupun hilang. 
 Sistem informasi berbasis web yang berjalan di jaringan computer diintegrasikan dengan perangkat keras mikrokontroler dapat dimanfatkan untuk pencatatan data dan identifikasi tas secara digital. Penggunaan system dengan metode digital akan mempermudah identifikasi tas. Yang akan menjadi lebih cepat dan data tersimpan secara aman. 
 Perancangan dan pembuatan system pengidentifikasian travel bag berbasis web menggunakan mikrokontroler adalah solusi untuk aplikasi pencatatan data dan identifikasi tas peserta dari biro perjalanan umroh atau haji. Dengan menggunakan system ini pihak biro perjalanan dan peserta akan merasa aman dan nyaman karena resiko tas tertukar sangat minim. Selain itu seluruh data tas dan pemiliknya tersimpan secara digital dan bisa diakses.

LAY YOUR BURDENS DOWN: DEFERRED RELIGIOUS COPING AS A MARITAL STRESS BUFFER FOR OLDER AFRICAN AMERICAN COUPLES

Although growing bodies of research explore the dynamics of minority families, few consider the African American family from a strengths-focused perspective. Stressors that threaten familial stability, such as financial strain, health disparities, and sporadic employment, disproportionately affect African American families and contribute to high rates of dissolution. In response, African American families often rely on religion as a source of coping and resilience. While existing literature adequately captures the frequency of religious-based responses to stress, opportunities to examine the nuances and underlying processes of religious coping for African American families exist. This study addresses the need to move beyond the broad measures of religiosity and religious coping, in exchange for a more in-depth exploration of how various forms of religious coping, specifically deferred coping, impact well-being. Deferred religious coping is characterized as a complete reliance on a higher power during a time of stress. Thirty-five older African American couples (N=70 individuals), representing 11 states in the U.S., were interviewed regarding the dynamics of deferred religious coping in the marital dyad. Following the digital recording and transcription of the narrative data, the interviews were analyzed with an open coding procedure consistent with grounded theory and Numeric Content Analysis (Marks, 2015). Analyses reveal that nearly 75% of the couples interviewed utilized deferred religious coping in response to stressors that could threaten marital stability. Further, salient themes include: (1) The Three-Party, Divine Triangle of Marriage, (2) Deferring Health Problems Reduces Worry, and (3) A Healthy Work-Family-Prayer Balance. Implications for practice are also discussed.

Augmented, Hyper-mediated, IRL

In this article, I explore how digital data collection in the context of the Berkeley-Abiquiú Collaborative Archaeology (BACA) project works, some of the affordances of this new-ish technology, and how they articulate with analogue art practices to achieve the goals of engaged research. Thinking with affordances helps me reflect critically on what digital data recording offers our research goals. In this case, the most important aspect of using digital data recording is how it changes our relationship to time. New orientations of research time created by such technology is an opportunity to engage creatively with how archaeology can represent complexity, produce embodied experience, and share senses of place through both digital and analogue practices. As archaeologists trying to think trans-humanistically, we need to reflect critically on digital technologies to produce engaged research. This is always a shifting target. New uses reveal new possibilities, and vice versa. But newness is not what makes an impact, a difference, or changes the way we do research together; what makes a difference is the result, effects, and affects of these affordances.

Uji Kinerja Surface Scanner 3D Menggunakan Sensor VL53L0X dan Mikrokontroler ATMEGA8535

Digital data recording of the geometry of 3-dimensional objects require a 3D scanner tool which are mostly using an imagery sensor. However there is hardly used information of scanned result, named color. Imagery sensor requires high spesification processing unit as capable as a personal computer for data acquisition processing. This research aims to build a 3D surface scanner using a time of flight laser ranging sensor and to develop its simple function to become more valuable device. Using point to point displacement method, the sensor measures the distance between the outermost point of the object and the sensor surface perpendicularly, once when one measurement is done then the object to be rotated along with the rotary table. The prototype was built using the VL53L0X sensor and ATMEGA8535 microcontroller as a motor controller for rotary table and vertical axis. Scanned data is sent from the microcontroller to the computer to be visualized in real time. The results show that the VL53L0X sensor is suitable for scanning convex objects but it is not capable to handle objects with multiple cavities.

The Digital Tree Drawing Test for Screening of Early Dementia: An Explorative Study Comparing Healthy Controls, Patients with Mild Cognitive Impairment, and Patients with Early Dementia of the Alzheimer Type.

The digital tree drawing test (dTDT) is a newly developed screening tool for the early detection of Alzheimer's disease. It is performed with a digitizing pen, recording each pen stroke with temporal and spatial precision. It was hypothesized that movement characteristics recorded during the painting process contribute to the identification of patients with mild cognitive impairment (MCI) and early dementia of the Alzheimer type (eDAT). The study population consisted of 187 participants (67 healthy controls, 64 MCI, and 56 eDAT patients) with a mean age of 68.6±10.6 years. Between-group comparisons of the dTDT-variables were conducted with analysis of variance. The diagnostic power of dTDT variables was analyzed with stepwise logistic regressions and areas under curve (AUC) of receiver operating control curves. Cognitively impaired persons used less colors and line widths and changed them less often than healthy subjects (p-values ≤0.05). Compared to control, eDAT patients had larger not-painting periods, were slower, and their pictures had less contrast, image size, and complexity (p-values ≤0.01). Logistic regression models of stepwise selected dTDT variables resulted in an AUC of 0.84 (95% confidence interval (CI) [0.79, 0.90], sensitivity = 0.78, specificity = 0.77) for discriminating healthy subjects from all cognitive impaired, an AUC of 0.77. (95% CI [0.69; 0.85], sensitivity = 0.56, specificity = 0.83) for discriminating healthy controls from MCI patients and an AUC of 0.90 (95% CI [0.84, 0.96], sensitivity = 0.86, specificity = 0.82) for discriminating controls from eDAT patients. The results suggest that digital recording of pen-stroke data during the drawing process can contribute to the screening of cognitive impaired patients.

Digital Data Recording of Transient Phenomena in RC Circuits with Arduino

Digital Data Recording of Transient Phenomena in RC Circuits with Arduino

Simple Method for Digital Data Recording of Damped Oscillations of Mechanical Pendulum

Simple Method for Digital Data Recording of Damped Oscillations of Mechanical Pendulum