scholarly journals Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2593
Author(s):  
Leandro Donisi ◽  
Giuseppe Cesarelli ◽  
Armando Coccia ◽  
Monica Panigazzi ◽  
Edda Maria Capodaglio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Occupational Safety ◽  
Inertial Sensor ◽  
Risk Groups ◽  
Receiver Operating Curve ◽  
Work Activity ◽  
Work Related ◽  
Related Risk ◽  
Intensity Duration Frequency ◽  
Niosh Lifting Equation

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

ANALISIS DAN PERANCANGAN MATERIAL HANDLING DENGAN PERHITUNGAN NIOSH LIFTING EQUATION SINGLE TASK

2018 ◽  
Vol 23 (1) ◽  
pp. 56-65
Author(s):  
Moehamad Adi Rochmat
Keyword(s):  
Material Handling ◽  
Occupational Safety ◽  
Occupational Safety And Health ◽  
Single Task ◽  
Safety And Health ◽  
Microsoft Office ◽  
Niosh Lifting Equation

Revised NIOSH Lifting Equation (RNLE) merupakan sebuah aplikasi yang dibuat oleh National Institute for Occupational Safety and Health (NIOSH) yang merupakan sebuah institusi di Amerika yang mengembangkan perangkat penilaian dalam bidang keselamatan dan kesehatan kerja. Salah satu aplikasi yang dikembangkan dinamakan Revised NIOSH Lifting Equation Single Task yang digunakan untuk menguji aktifitas pemindahan barang tanpa perpindahan posisi kaki. Aplikasi yang dibangun akan memberikan penilaian terhadap sistem kerja yang dilakukan oleh seorang pekerja. Salah satu hasil perhitungan dari aplikasi tersebut adalah nilai Lifting Index (LI) yang menyatakan tingkat resiko pekerjaan. Aplikasi RNLE dikembangkan dengan dasar program Microsoft Office Excel. Rumus perhitungan untuk mendapatkan nilai LI dan parameter yang diperlukan disediakan oleh NIOSH dan dapat dipelajari. Penelitian ini mengembangkan rekomendasi perbaikan sistem kerja pada aplikasi RNLE dengan menggunakan program Microsoft Office Excel. Rekomendasi perbaikan yang diutamakan adalah posisi awal benda dan posisi akhir yang sebaiknya diatur sedemikian sehingga mengoptimalkan kemampuan pekerja. Cara optimalisasi yang bisa dilakukan dengan merubah salah satu nilai parameter masukkan tanpa merubah nilai yang lainnya.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

Updates on NORA Musculoskeletal Health Cross-Sector Council Activities

2020 ◽  
Vol 64 (1) ◽  
pp. 1279-1281
Author(s):  
David M. Rempel ◽  
Scott Schneider ◽  
Sean Gallagher ◽  
Sheree Gibson ◽  
Susan Kotowski ◽  
...  
Keyword(s):  
Musculoskeletal Disorders ◽  
Research Agenda ◽  
Occupational Safety ◽  
Occupational Safety And Health ◽  
Evidence Based ◽  
Musculoskeletal Health ◽  
Work Related ◽  
Safety And Health ◽  
Work Related Musculoskeletal Disorders ◽  
Collaborative Efforts

The National Occupational Research Agenda (NORA) is a research framework for the nation and for the National Institute for Occupational Safety and Health (NIOSH). The NORA Musculoskeletal Health Cross-Sector (MUS) Council focuses on the mitigation of work-related musculoskeletal disorders (WMSDs). Two projects have been chosen by the MUS Council for disseminating existing information on ergonomic assessment methods and interventions. The first project involves collaboration with the AIHA Ergonomics Committee on the latest update of the AIHA Ergonomic Assessment Toolkit. The second project aims to post all-industry information on ergonomic solutions/interventions/guidelines in collaboration with the International Ergonomics Association (IEA). The MUS Council plans on leveraging the collaborative efforts for promoting widespread adoption of evidence-based workplace practices for the prevention of WMSDs.

scholarly journals Machine Learning in Aging: An Example of Developing Prediction Models for Serious Fall Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 268-269
Author(s):  
Jaime Speiser ◽  
Kathryn Callahan ◽  
Jason Fanning ◽  
Thomas Gill ◽  
Anne Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Random Forest ◽  
Decision Tree ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Learning Methods ◽  
Life Study ◽  
Fall Injury ◽  
Machine Learning Methods

Abstract Advances in computational algorithms and the availability of large datasets with clinically relevant characteristics provide an opportunity to develop machine learning prediction models to aid in diagnosis, prognosis, and treatment of older adults. Some studies have employed machine learning methods for prediction modeling, but skepticism of these methods remains due to lack of reproducibility and difficulty understanding the complex algorithms behind models. We aim to provide an overview of two common machine learning methods: decision tree and random forest. We focus on these methods because they provide a high degree of interpretability. We discuss the underlying algorithms of decision tree and random forest methods and present a tutorial for developing prediction models for serious fall injury using data from the Lifestyle Interventions and Independence for Elders (LIFE) study. Decision tree is a machine learning method that produces a model resembling a flow chart. Random forest consists of a collection of many decision trees whose results are aggregated. In the tutorial example, we discuss evaluation metrics and interpretation for these models. Illustrated in data from the LIFE study, prediction models for serious fall injury were moderate at best (area under the receiver operating curve of 0.54 for decision tree and 0.66 for random forest). Machine learning methods may offer improved performance compared to traditional models for modeling outcomes in aging, but their use should be justified and output should be carefully described. Models should be assessed by clinical experts to ensure compatibility with clinical practice.

scholarly journals Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2469
Author(s):  
Chen-Yi Xie ◽  
Chun-Lap Pang ◽  
Benjamin Chan ◽  
Emily Yuen-Yuen Wong ◽  
Qi Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Imaging ◽  
Risk Groups ◽  
Review Of Literature ◽  
Primary Tumors ◽  
Non Invasive ◽  
Significant Difference ◽  
Recent Developments ◽  
Health Significance ◽  
Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

scholarly journals Using Machine Learning and Wearable Inertial Sensor Data for the Classification of Fractal Gait Patterns in Women and Men During Load Carriage

2021 ◽  
Vol 185 ◽  
pp. 282-291
Author(s):  
Nizam U. Ahamed ◽  
Kellen T. Krajewski ◽  
Camille C. Johnson ◽  
Adam J. Sterczala ◽  
Julie P. Greeves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Sensor ◽  
Load Carriage ◽  
Sensor Data ◽  
Gait Patterns

scholarly journals Machine learning identifies two autophagy-related genes as markers of recurrence in colorectal cancer

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052095880
Author(s):  
Jianping Wu ◽  
Sulai Liu ◽  
Xiaoming Chen ◽  
Hongfei Xu ◽  
Yaoping Tang
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Protein Interactions ◽  
Early Stage ◽  
Predictive Ability ◽  
Improve Patient Care ◽  
Active Role ◽  
Risk Groups ◽  
Recurrence Risk ◽  
Machine Learning Algorithms

Objective Colorectal cancer (CRC) is the most common cancer worldwide. Patient outcomes following recurrence of CRC are very poor. Therefore, identifying the risk of CRC recurrence at an early stage would improve patient care. Accumulating evidence shows that autophagy plays an active role in tumorigenesis, recurrence, and metastasis. Methods We used machine learning algorithms and two regression models, univariable Cox proportion and least absolute shrinkage and selection operator (LASSO), to identify 26 autophagy-related genes (ARGs) related to CRC recurrence. Results By functional annotation, these ARGs were shown to be enriched in necroptosis and apoptosis pathways. Protein–protein interactions identified SQSTM1, CASP8, HSP80AB1, FADD, and MAPK9 as core genes in CRC autophagy. Of 26 ARGs, BAX and PARP1 were regarded as having the most significant predictive ability of CRC recurrence, with prediction accuracy of 71.1%. Conclusion These results shed light on prediction of CRC recurrence by ARGs. Stratification of patients into recurrence risk groups by testing ARGs would be a valuable tool for early detection of CRC recurrence.

Sign in / Sign up

Export Citation Format

Share Document