scholarly journals Assessing the validity of dual-minima algorithm for heel-strike and toe-off prediction for the amputee population

2021 ◽  
Author(s):  
Zohaib Aftab
Keyword(s):  
Prediction Accuracy ◽  
Open Data ◽  
Heel Strike ◽  
Transfemoral Amputation ◽  
Data Set ◽  
Velocity Signal ◽  
Rule Based ◽  
Marker Data ◽  
Actual Event ◽  
Median Error

AbstractAssessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. Results showed considerable accuracy for the HS with a median error of -1ms. The TO prediction error was larger with the median ranging from 35-84ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects’ mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and amputee’s functional ability level.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

scholarly journals Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gait Analysis ◽  
Real Time ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Clinical Applications ◽  
Performance Comparison ◽  
Heel Strike ◽  
Rule Based ◽  
Pathological Gait

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

scholarly journals Analyzing University of Virginia Health publications using open data, Python, and Streamlit

2021 ◽  
Vol 109 (4) ◽  
Author(s):  
Anson Parker ◽  
Abbey Heflin ◽  
Lucy Carr Jones
Keyword(s):  
Open Access ◽  
Data Visualization ◽  
Web Of Science ◽  
Open Data ◽  
Health Sciences ◽  
Open Access Publishing ◽  
University Of Virginia ◽  
Data Set ◽  
Novel Approach ◽  
Rest Api

As part of a larger project to understand the publishing choices of UVA Health authors and support open access publishing, a team from the Claude Moore Health Sciences Library analyzed an open data set from Europe PMC, which includes metadata from PubMed records. We used the Europe PMC REST API to search for articles published in 2017–2020 with “University of Virginia” in the author affiliation field. Subsequently, we parsed the JSON metadata in Python and used Streamlit to create a data visualization from our public GitHub repository. At present, this shows the relative proportions of open access versus subscription-only articles published by UVA Health authors. Although subscription services like Web of Science, Scopus, and Dimensions allow users to do similar analyses, we believe this is a novel approach to doing this type of bibliometric research with open data and open source tools.  

scholarly journals TermPicks: A century of Greenland glacier terminus data for use in machine learning applications

2021 ◽  
Author(s):  
Sophie Goliber ◽  
Taryn Black ◽  
Ginny Catania ◽  
James M. Lea ◽  
Helene Olsen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Greenland Ice Sheet ◽  
Median Number ◽  
Google Earth ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Outlet Glacier ◽  
Glacier Terminus ◽  
Median Error

Abstract. Marine-terminating outlet glacier terminus traces, mapped from satellite and aerial imagery, have been used extensively in understanding how outlet glaciers adjust to climate change variability over a range of time scales. Numerous studies have digitized termini manually, but this process is labor-intensive, and no consistent approach exists. A lack of coordination leads to duplication of efforts, particularly for Greenland, which is a major scientific research focus. At the same time, machine learning techniques are rapidly making progress in their ability to automate accurate extraction of glacier termini, with promising developments across a number of optical and SAR satellite sensors. These techniques rely on high quality, manually digitized terminus traces to be used as training data for robust automatic traces. Here we present a database of manually digitized terminus traces for machine learning and scientific applications. These data have been collected, cleaned, assigned with appropriate metadata including image scenes, and compiled so they can be easily accessed by scientists. The TermPicks data set includes 39,060 individual terminus traces for 278 glaciers with a mean and median number of traces per glacier of 136 ± 190 and 93, respectively. Across all glaciers, 32,567 dates have been picked, of which 4,467 have traces from more than one author (duplication of 14 %). We find a median error of ∼100 m among manually-traced termini. Most traces are obtained after 1999, when Landsat 7 was launched. We also provide an overview of an updated version of The Google Earth Engine Digitization Tool (GEEDiT), which has been developed specifically for future manual picking of the Greenland Ice Sheet.

scholarly journals A new database structure for the IHFC Global Heat Flow Database

2021 ◽  
Vol 4 (1) ◽  
pp. 1-14
Author(s):  
Sven Fuchs ◽  
Graeme Beardsmore ◽  
Paolo Chiozzi ◽  
Orlando Miguel Espinoza-Ojeda ◽  
Gianluca Gola ◽  
...  
Keyword(s):  
Heat Flow ◽  
International Association ◽  
Open Data ◽  
Flow Data ◽  
Ocean Drilling Program ◽  
Data Set ◽  
Data Services ◽  
External Data ◽  
Database Structure ◽  
Starting Point

Periodic revisions of the Global Heat Flow Database (GHFD) take place under the auspices of the International Heat Flow Commission (IHFC) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI). A growing number of heat-flow values, advances in scientific methods, digitization, and improvements in database technologies all warrant a revision of the structure of the GHFD that was last amended in 1976. We present a new structure for the GHFD, which will provide a basis for a reassessment and revision of the existing global heat-flow data set. The database fields within the new structure are described in detail to ensure a common understanding of the respective database entries. The new structure of the database takes advantage of today's possibilities for data management. It supports FAIR and open data principles, including interoperability with external data services, and links to DOI and IGSN numbers and other data resources (e.g., world geological map, world stratigraphic system, and International Ocean Drilling Program data). Aligned with this publication, a restructured version of the existing database is published, which provides a starting point for the upcoming collaborative process of data screening, quality control and revision. In parallel, the IHFC will work on criteria for a new quality scheme that will allow future users of the database to evaluate the quality of the collated heat-flow data based on specific criteria.

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

Characterising RDF data sets

2017 ◽  
Vol 44 (2) ◽  
pp. 203-229 ◽  
Author(s):  
Javier D Fernández ◽  
Miguel A Martínez-Prieto ◽  
Pablo de la Fuente Redondo ◽  
Claudio Gutiérrez
Keyword(s):  
Data Structures ◽  
Large Scale ◽  
Open Data ◽  
Structural Features ◽  
Data Sets ◽  
Data Set ◽  
Wide Range ◽  
Rdf Data ◽  
Description Framework ◽  
Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

Sharing Open Data in Agriculture

2021 ◽  
pp. 244-266
Author(s):  
Liah Shonhe
Keyword(s):  
Agricultural Sector ◽  
Open Data ◽  
Research Data ◽  
Data Sets ◽  
Research Activity ◽  
African Countries ◽  
Data Set ◽  
Data Repositories ◽  
Bibliographic Data ◽  
Prolific Authors

The main focus of the study was to explore the practices of open data sharing in the agricultural sector, including establishing the research outputs concerning open data in agriculture. The study adopted a desktop research methodology based on literature review and bibliographic data from WoS database. Bibliometric indicators discussed include yearly productivity, most prolific authors, and enhanced countries. Study findings revealed that research activity in the field of agriculture and open access is very low. There were 36 OA articles and only 6 publications had an open data badge. Most researchers do not yet embrace the need to openly publish their data set despite the availability of numerous open data repositories. Unfortunately, most African countries are still lagging behind in management of agricultural open data. The study therefore recommends that researchers should publish their research data sets as OA. African countries need to put more efforts in establishing open data repositories and implementing the necessary policies to facilitate OA.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

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