scholarly journals Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Belén Rubio Ballester ◽  
Fabrizio Antenucci ◽  
Martina Maier ◽  
Anthony C. C. Coolen ◽  
Paul F. M. J. Verschure
Keyword(s):  
Data Science ◽  
Objective Assessment ◽  
Noise Model ◽  
Alternative Methods ◽  
Reaching Movements ◽  
Stroke Patients ◽  
Kinematic Data ◽  
Work Area ◽  
Wide Range ◽  
Computer Based

Abstract Introduction After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients’ hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model’s performance to estimate FM-UE scores reaches an accuracy of $$R^2$$ R 2 : 0.38 with an error ($$\sigma$$ σ : 12.8). Next, we evaluate its reliability ($$r=0.89$$ r = 0.89 for test-retest), longitudinal external validity ($$95\%$$ 95 % true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements ($$R^2$$ R 2 : 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory ($$R^2$$ R 2 : 0.40) and Barthel Index ($$R^2$$ R 2 : 0.35). Conclusions Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

scholarly journals Predicting Upper-Extremity Function Recovery From Kinematics in Stroke Patients Following Goal-Oriented Computer-Based Training

2021 ◽  
Author(s):  
Fabrizio Antenucci ◽  
Belén Rubio Ballester ◽  
Martina Maier ◽  
Anthony C.C. Coolen ◽  
Paul F. M. J. Verschure
Keyword(s):  
Virtual Reality ◽  
Upper Extremity ◽  
Motion Capture ◽  
Multivariate Regression ◽  
Alternative Methods ◽  
Upper Limbs ◽  
Stroke Patients ◽  
Kinematic Data ◽  
Wide Range ◽  
Computer Based

Abstract Background: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, predicting impairment and recovery are enormous challenges in neurorehabilitation. Body function and structure, as well as activities, are assessed using clinical scales. For functional deficits of the upper extremities these include the Fugl-Meyer Assessment for the Upper Extremity (FM-UE), the Chedoke Arm and Hand Activity Inventory (CAHAI) and Barthel Index (BI), administered by clinicians. Although these scales are generally accepted for the evaluation of the motor and functional impairment of the upper-limbs, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. For these reasons, alternative methods need to be developed for efficient and objective assessment. Computer-based motion capture and classification tools have the potential to collect and process kinematic data to estimate impairment, function and recovery while overcoming these limitations.Methods: We present a method for estimating clinical scores from movement parameters that are entirely extracted from kinematic data recorded during unsupervised rehabilitation sessions performed with the Rehabilitation Gaming System (RGS). RGS is a rehabilitation technology that uses image-based motion capture, goal-oriented individualised training, virtual reality content delivery, and restricts compensatory trunk movements through feedback. The main protocol considered in this study asks patients to use their upper limbs to intercept spheres that are presented in a 3 dimensional virtual reality display. RGS maps the planar physical arm movements onto matching movements by an avatar presented in a first-person perspective. In this analysis, we performed a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS.Results: Our multivariate regression model reaches an accuracy of R2 : 0.38, with an error (σ : 12.8), in predicting FM-UE scores. We analyse our model by assessing reliability (r = 0:89 for test-retest), sensitivity to clinical improvements (95% true positive rate) and generalisation to other tasks that involve planar reaching movements (R2 : 0.39). The model achieves comparable accuracy also for the CAHAI (R2 : 0.40) and BI scales (R2 : 0.35).Conclusions: Our results highlight the clinically relevant predictive power of kinematic data collected during unsupervised goal-oriented motor training combined with automated inference techniques and provide new insight into factors underlying recovery and its biomarkers.

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

Classical and modern drug extraction techniques: facts and figures

2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Bharti Umrethia ◽  
Bharat Kalsariya ◽  
Prof. P. U. Vaishnav
Keyword(s):  
Solvent Extraction ◽  
Dosage Forms ◽  
Herbal Extract ◽  
Alternative Methods ◽  
Extraction Techniques ◽  
Modern Drug ◽  
Novel Drugs ◽  
Wide Range ◽  
In The Mists ◽  
Heating Pattern

In present era, herbal extract succeeds inimitable place in pharmaceutical science. In view back the earliest extraction techniques are lost in the mists of history. As time went the plants have been processed by grinding, boiling or immersing. The systemic presentation of Ayurvedic extraction system has been first time familiarized by Acharya Charaka as Panchavidha Kashaya Kalpana (five basic primary dosage forms) and based upon these primary dosage forms, secondary dosage forms are developed by using different heating pattern for extraction of pharmacological active ingredients. The administration of these dosage forms is mainly dependent on the Bala (strength) of Vyadhi (disease) and Atura (patient). Due to increased demand of Ayurvedic medicines and industrialization, the transformation of classical dosage forms takes place by implanting a wide range of technologies with different methods of extraction include conventional techniques such as maceration, percolation, infusion, decoction, hot continuous extraction etc. and recently, alternative methods like ultrasound assisted solvent extraction (UASE), microwave assisted solvent extraction (MASE) and supercritical fluid extractions (SFE). The extract obtained by these procedure uses as a large source of therapeutic phyto-chemicals that may lead to the development of novel drugs. Essentially, the purpose behind this changing face in both the extraction systems are different but can say that it is a new insight from ancient essence.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Video Games ◽  
Analysis Method ◽  
Engineering Economics ◽  
Game Ai ◽  
Content Analysis Method ◽  
Wide Range ◽  
Computer Based ◽  
Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

Early Acoustic Warning for the Onset of Acute Chest Syndrome in Sickle Cell Patients

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Brian Henry ◽  
Gardner Yost ◽  
Robert Molokie ◽  
Thomas J. Royston
Keyword(s):  
Sickle Cell ◽  
Hospital Setting ◽  
Pulmonary Infiltrate ◽  
Acute Chest Syndrome ◽  
Computational Techniques ◽  
X Ray ◽  
Wide Range ◽  
Chest X Ray ◽  
Computer Based ◽  
In Silico Models

Acute chest syndrome (ACS) is a leading cause of death for those with sickle cell disease (SCD). ACS is defined by the development of a new pulmonary infiltrate on chest X-ray, with fever and respiratory symptoms. Efforts have been made to apply various technologies in the hospital setting to provide earlier detection of ACS than X-ray, but they are expensive, increase radiation exposure to the patient, and are not technologies that are easily transferrable for home use to help with early diagnosis. We present preliminary studies on patients suggesting that acoustical measurements recorded quantitatively with contact sensors (electronic stethoscopes) and analyzed using advanced computational analysis methods may provide an earlier diagnostic indicator of the onset of ACS than is possible with current clinical practice. Novel in silico models of respiratory acoustics utilizing image-based and algorithmically developed lungs with full conducting airway trees support and help explain measured acoustic trends and provide guidance on the next steps in developing and translating a diagnostic approach. More broadly, the experimental and computational techniques introduced herein, while focused on monitoring and predicting the onset of ACS, could catalyze further advances in mobile health (mhealth)-enabled, computer-based auscultative diagnoses for a wide range of cardiopulmonary pathologies.

scholarly journals Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ali Rohani ◽  
Jennifer A. Kashatus ◽  
Dane T. Sessions ◽  
Salma Sharmin ◽  
David F. Kashatus
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Data Science ◽  
Structural Information ◽  
Morphological Changes ◽  
Mitochondrial Morphology ◽  
Single Cell Level ◽  
Mitochondrial Network ◽  
Cell Level ◽  
Wide Range

Abstract Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

scholarly journals Family and childcare support public expenditures and short-term fertility dynamics

2013 ◽  
Vol 60 (3) ◽  
pp. 347-364 ◽  
Author(s):  
Cosmin Enache
Keyword(s):  
Birth Rate ◽  
Crude Birth Rate ◽  
Low Fertility ◽  
Alternative Methods ◽  
Public Expenditures ◽  
Short Term ◽  
Wide Range ◽  
Policy Measures ◽  
Support Policy ◽  
Cash Benefits

In a period of very low fertility, effective family and childcare support policy measures are needed. From a wide range of instruments available to government intervention, we focus on public expenditures effects on short-term fertility. Using a sample of 28 European countries in a panel framework, we found that there is a small positive elasticity of crude birth rate to cash benefits related to childbirth and childrearing provided through social security system. Different public services provided to ease the burden of parents and all other benefits in kind, means or non-means tested, are found to be insignificant. These results are robust to alternative methods of estimation. Controlling for country heterogeneity by religion and by culture, some particularly interesting differences in birth rate determinants were highlighted as well.

scholarly journals Frequency and Risk Factors of Pneumonia and Urinary Tract Infection during Hospitalization in Acute Stroke Patients.

2016 ◽  
Vol 32 (2) ◽  
pp. 74-84
Author(s):  
Shahed Ahmad ◽  
Matiur Rahman ◽  
Mostafa Hosen ◽  
Abul Kalam ◽  
Mohammed Shoab ◽  
...  
Keyword(s):  
Risk Factors ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Acute Stroke ◽  
Female Gender ◽  
Older Age ◽  
Stroke Patients ◽  
Severe Stroke ◽  
Wide Range

Background: Acute stroke Patients are at risk of developing a wide range of complications. Among these medical complications the most common are infections, including pneumonia and urinary tract infection (UTI). This study was designed to see the frequency and risk factors of pneumonia and UTI after acute stroke in hospitalized patients. Methods : This prospective observational study was done in the Department of Neurology and Department of Medicine, Sylhet M.A.G Osmani Medical College Hospital, from May 2014 to November 2014. After hospitalization, a total number of 80 acute stroke patients were enrolled in this study. All patients of both sexes, presented with acute stroke, were confirmed by CT scan of head; vascular risk factors were recorded and relevant investigations were done. Results: Among the study subjects Urinary tract infection was found in 23 (28.8%) patients. Statistically significant risk factors for UTI were : > 65 years age (OR=2.926; 95% of CI=1.044-8.202; p=0.037). Female gender (OR=0.327; 95% of CI=0.120-0.889; p=0.026), diabetes (OR=2.015; 95% of CI=1.019-7.780; p=0.042), Severe stroke (OR=3.331; 95% of CI=1.217-9.116; p=0.017), Foley tube catheterization (OR=4.229; 95% of CI=1.492-11.982; p=0.005). Pneumonia developed in 17 (21.2%) patients and no pneumonia in 63 (78.8%) patients. Conclusion : UTI and pneumonia are common occurrence after acute stroke during stroke hospitalization. Older age, female gender, diabetes mellitus, severe stroke at presentation and urinary catheterization were found the risk factors of UTI; whereas older age, severe stroke at presentation, nasogastric tube feeding, oropharyngeal suction and difficulty in swallowing were found the risk factors of pneumonia in acute stroke. Bangladesh Journal of Neuroscience 2016; Vol. 32 (2): 74-84

scholarly journals Experience with the use of microbiological analyzer BactoSCREEN in a routine practice of clinical microbiology laboratory

2020 ◽  
pp. 221-230
Author(s):  
Svetlana A. Gordeeva ◽  
A.Yu. Zolotarev ◽  
M.G. Movsisyan ◽  
A.V. Rozinko
Keyword(s):  
Species Identification ◽  
Clinical Microbiology ◽  
Objective Assessment ◽  
Maldi Ms ◽  
Routine Practice ◽  
Clinical Microbiology Laboratory ◽  
Full Time ◽  
Microbiology Laboratory ◽  
Staff Time ◽  
Wide Range

Objective. Assessment of bacterial identification effectiveness in clinical microbiology laboratory using the MALDI-MS based system BactoSCREEN. Materials and Methods. Bacteriological testing was done by the cultivation on Сolumbia agar with 5% of sheep blood (at 37°C for 24 hours). Colonies for identification were selected based on their growth pattern, type of hemolysis, morphology and consistency. The species identification was done by the MALDI-MS using the microbiology analyzer BactoSCREEN. Apart from MALDI-MS, we used morphology and biochemical methods for species identification when necessary. Serological tests were used for serovar and biovar identifications. Results. A total of 85945 bacterial identifications was performed in 2018. When compared to 2017, the throughput of the laboratory increased ten times. A total of 23252 isolates were obtained in the previously mentioned period. A single identification took 2.98–13.22 minutes including time for supporting procedures, whereas the staff time for one identification itself constituted an average of 1.55 minutes. When compared to manual methods, introduction of mass-spectrometry allowed us to achieve 3.5-fold decrease of the staff time in the average. Therefore, annual labor saving in terms of staffing corresponds to 11 full-time positions. Conclusions. In view of high throughput, analysis speed, simplicity and low cost of sample preparation, MALDI-MS identification fits well into the practice of clinical microbiology laboratory, especially when large-scale screening studies of bacterial cultures are required. The use of MALDI-MS is likely to be most promising when carrying out microbiological monitoring that is traditionally associated with large number of samples and wide range of microorganisms detected.

Sign in / Sign up

Export Citation Format

Share Document