A Skiing Trace Clustering Model for Injury Risk Assessment

2016 ◽  
Vol 8 (1) ◽  
pp. 56-68 ◽  
Author(s):  
Milan Dobrota ◽  
Boris Delibašić ◽  
Pavlos Delias
Keyword(s):  
Injury Risk ◽  
Activity Patterns ◽  
Risk Level ◽  
Risk Category ◽  
Clustering Methods ◽  
Cluster Number ◽  
Event Logs ◽  
Clustering Model ◽  
Initial Transformation ◽  
Process Event

This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. The goal was to achieve the best possible discrimination. Several experimental settings were made to achieve and suggest a good combination of algorithm parameters and cluster number. After clusters are obtained, they are categorized in three categories according to risk level. It can be concluded that the proposed method can be used to distinguish skiing patterns by risk category based on injury occurrences.

scholarly journals Trace Clustering: A Preprocessing Method to Improve the Performance of Process Discovery

2021 ◽  
Vol 10 (09) ◽  
pp. 116-121
Author(s):  
Huiling LI ◽  
Shuaipeng ZHANG ◽  
Xuan SU
Keyword(s):  
Process Model ◽  
Process Mining ◽  
Process Models ◽  
Clustering Methods ◽  
Clustering Method ◽  
Model Quality ◽  
Process Discovery ◽  
Event Logs ◽  
Clustering Model ◽  
Preprocessing Method

The information system collects a large number of business process event logs, and process discovery aims to discover process models from the event logs. Many process discovery methods have been proposed, but most of them still have problems when processing event logs, such as low mining efficiency and poor process model quality. The trace clustering method allows to decompose original log to effectively solve these problems. There are many existing trace clustering methods, such as clustering based on vector space approaches, context-aware trace clustering, model-based sequence clustering, etc. The clustering effects obtained by different trace clustering methods are often different. Therefore, this paper proposes a preprocessing method to improve the performance of process discovery, called as trace clustering. Firstly, the event log is decomposed into a set of sub-logs by trace clustering method, Secondly, the sub-logs generate process models respectively by the process mining method. The experimental analysis on the datasets shows that the method proposed not only effectively improves the time performance of process discovery, but also improves the quality of the process model.

scholarly journals Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245264
Author(s):  
Ali Sabah ◽  
Sabrina Tiun ◽  
Nor Samsiah Sani ◽  
Masri Ayob ◽  
Adil Yaseen Taha
Keyword(s):  
Web Search ◽  
Multiple Views ◽  
Consensus Clustering ◽  
Clustering Methods ◽  
Cluster Number ◽  
Clustering Ensemble ◽  
Single View ◽  
Search Result ◽  
Clustering Model ◽  
Clustering Quality

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

Injury prevention knowledge, beliefs and strategies in elite female footballers at the FIFA Women’s World Cup France 2019

2021 ◽  
pp. bjsports-2020-103131
Author(s):  
Celeste Geertsema ◽  
Liesel Geertsema ◽  
Abdulaziz Farooq ◽  
Joar Harøy ◽  
Chelsea Oester ◽  
...  
Keyword(s):  
Risk Factors ◽  
Injury Prevention ◽  
Injury Risk ◽  
Risk Level ◽  
Attitudes And Beliefs ◽  
Implementation Barriers ◽  
World Cup ◽  
Positive Attitudes ◽  
High Level ◽  
Women's Football

ObjectivesThis study assessed knowledge, beliefs and practices of elite female footballers regarding injury prevention.MethodsA survey was sent to players participating in the FIFA Women’s World Cup France 2019. Questions covered three injury prevention domains: (1) knowledge; (2) attitudes and beliefs; (3) prevention practices in domestic clubs. Additionally, ACL injury history was assessed.ResultsOut of 552 players, 196 women responded (35.5%). More than 80% of these considered injury risk to be moderate or high. Players listed knee, ankle, thigh, head and groin as the most important injuries in women’s football. The most important risk factors identified were low muscle strength, followed by poor pitch quality, playing on artificial turf, too much training, reduced recovery and hard tackles. In these elite players, 15% did not have any permanent medical staff in their domestic clubs, yet more than 75% had received injury prevention advice and more than 80% performed injury prevention exercises in their clubs. Players identified the two most important implementation barriers as player motivation and coach attitude. Two-thirds of players used the FIFA 11+ programme in their clubs.ConclusionsThis diverse group of elite players demonstrated good knowledge of risk level and injury types in women’s football. Of the risk factors emphasised by players, there was only one intrinsic risk factor (strength), but several factors out of their control (pitch quality and type, training volume and hard tackles). Still players had positive attitudes and beliefs regarding injury prevention exercises and indicated a high level of implementation, despite a lack of medical support.

scholarly journals Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1820
Author(s):  
Ekaterina V. Orlova
Keyword(s):  
Interest Rates ◽  
Additional Data ◽  
Risk Level ◽  
Gradient Boosting ◽  
Cluster Number ◽  
New Approach ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
The Stability ◽  
Digital Footprints

This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k-means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

Intra- and Inter-Rater Reliability of a Ballet-based Dance Technique Screening Instrument

2020 ◽  
Vol 35 (1) ◽  
pp. 28-34
Author(s):  
Shaw Bronner ◽  
Ivetta Lassey ◽  
Jessie R Lesar ◽  
Zachary G Shaver ◽  
Catherine Turner
Keyword(s):  
Standardized Testing ◽  
Injury Risk ◽  
Online Survey ◽  
Physical Therapists ◽  
Screening Instrument ◽  
Risk Level ◽  
Level Of Evidence ◽  
Rater Reliability ◽  
Training Video ◽  
Dance Medicine

OBJECTIVES: To investigate intra- and inter-rater reliability of a ballet-based Dance Technique Screening Instrument used by physical therapists (PTs) and student PTs (SPTs) with prior dance medicine or dance experience. METHODS: Ten pre-professional dancers were video-recorded in the sagittal and frontal planes while performing four dance sequences: 1) second position grand plié; 2) développé à la seconde; 3) single-limb passé relevé balance; and 4) jumps in first position. Dance videos and electronic versions of the demographics and scoring forms were provided through a secure online survey to 28 PTs and SPTs who served as raters. Raters reviewed a training video prior to scoring the 10 dancers. Raters were asked to repeat their assessments 1–2 wks later. Intraclass correlations (ICC) were assessed for all-raters, PTs, and SPTs for total and sequence scores. RESULTS: Twenty-eight raters assessed the videos one time. Inter-rater reliability was ICC=0.98 (CI95=0.96–0.99) (all-raters), with PTs and SPTs displaying similar values (ICC=0.96 and 0.96, respectively). Eighteen raters (11 PTs, 7 SPTs) repeated the video analysis. Intra-rater reliability was ICC=0.78 (CI95=0.72–0.83) with PTs ICC=0.81 and SPTs ICC=0.70. CONCLUSIONS: Correlations were high for all-raters. SPTs were as reliable as PTs in inter-rater comparisons. PTs exhibited higher intra-rater reliability compared to SPTs. These results substantiate the reliability of a standardized testing instrument to conduct dance technique assessment. Validity of this instrument was demonstrated in a previous study which found dancers with better technique were less likely to sustain injury. The ability to identify technique deficits can guide preventative programs that may reduce injury risk. LEVEL OF EVIDENCE: Level III.

scholarly journals Automatic Repair of Same-Timestamp Errors in Business Process Event Logs

2020 ◽  
pp. 327-345 ◽  
Author(s):  
Raffaele Conforti ◽  
Marcello La Rosa ◽  
Arthur H. M. ter Hofstede ◽  
Adriano Augusto
Keyword(s):  
Business Process ◽  
Event Logs ◽  
Process Event

Female breast shape categorization based on analysis of CAESAR 3D body scan data

2018 ◽  
Vol 89 (4) ◽  
pp. 590-611 ◽  
Author(s):  
Jie Pei ◽  
Huiju Park ◽  
Susan P. Ashdown
Keyword(s):  
Goodness Of Fit ◽  
Misclassification Rate ◽  
Clustering Methods ◽  
Cluster Number ◽  
Female Breast ◽  
Multiple Data ◽  
Intimate Apparel ◽  
Breast Shape ◽  
Scan Data ◽  
Relative Measurements

In this study we explore the variation in female breast shape across the younger (age: 18–45), non-obese (BMI < 30) North American Caucasian population, a population that has not previously been well-represented in studies of breast shape. A method of classifying breast shape was developed based on multiple data-mining techniques. Forty-one relative measurements (i.e., ratios and angles) were constructed from 66 raw measurements (circumferences, depths, widths, etc.), extracted from 478 CAESAR (Civilian American and European Surface Anthropometry Resource) scans, using self-developed Matlab® programs. Seventy subjects were regarded as outliers and were removed. The remaining data were transformed and standardized to ensure robust analysis. To judge results, an algorithm was developed to visualize clustering outcomes in the form of side profiles of breasts. The results of three clustering methods, namely hierarchical, K-means, and K-medoids clustering, were compared. Finally, breast shapes were categorized into three and five groups by two different cluster number selection criteria proposed by the study: (1) based on misclassification rate; (2) based on the goodness-of-fit of the model. Several of the relative body measurements were identified to be critical in defining breast shape. The findings and the proposed methods of this study can contribute to the development of improved shape and sizing systems of bra products that work for both manufacturers and consumers. The new methodology developed in this study can also be applied to other types of intimate apparel products where an understanding of body shape plays a key role in body support, comfort, and fit.

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

Sign in / Sign up

Export Citation Format

Share Document