ArticleSensors (Basel, Switzerland)2021
Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05777304 (Wearable Sensors and Machine Learning), which is not on this map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Wearable Sensors and Machine Learning: a Technological Approach to Biomechanical Risk Assessment in Lifting Tasks
Who cites it
21 citing papers in PubMed.
- Modeling the Impact of Ergonomic Interventions and Occupational Factors on Work-Related Musculoskeletal Disorders in the Neck of Office Workers with Machine Learning Methods.Journal of research in health sciences · 2024Trial
- Current Trends in Artificial Intelligence for Recognizing Work Postures to Prevent Work-Related Musculoskeletal Disorders: Systematic Review and Meta-Analysis by Occupational Activity.Bioengineering (Basel, Switzerland) · 2026Review
- An automatic approach to assess biomechanical risk using machine learning algorithms and inertial sensors.Physical and engineering sciences in medicine · 2026Article
- Computer vision and tactile glove: A multimodal model in lifting task risk assessment.Applied ergonomics · 2025Article
- Classification of musculoskeletal pain using machine learning.Scientific reports · 2025Article
- Predictive power of the HEMPA risk assessment method for musculoskeletal disorders in nurses and caregivers: insights and implications.BMC nursing · 2025Article
- Article
- Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities.Diagnostics (Basel, Switzerland) · 2025Article
- Accuracy of Automatically Identifying the American Conference of Governmental Industrial Hygienists Threshold Limit Values Twelve Lifting Zones over Three Simplified Zones Using Computer Algorithm.Sensors (Basel, Switzerland) · 2024Article
- A Case Study of Ergonomic Risk Assessment in Slovakia with Respect to EU Standard.International journal of environmental research and public health · 2024Article
- Capability of Machine Learning Algorithms to Classify Safe and Unsafe Postures during Weight Lifting Tasks Using Inertial Sensors.Diagnostics (Basel, Switzerland) · 2024Article
- Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics.Sensors (Basel, Switzerland) · 2023Article
- sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings.Bioengineering (Basel, Switzerland) · 2023Article
- Tactile Gloves Predict Load Weight During Lifting With Deep Neural Networks.IEEE sensors journal · 2023Article
- Detection of Suspicious Cardiotocographic Recordings by Means of a Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2023Article
- Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature.Diagnostics (Basel, Switzerland) · 2022Review
- Sensor-Based Motion Tracking System Evaluation for RULA in Assembly Task.Sensors (Basel, Switzerland) · 2022Article
- A Logistic Regression Model for Biomechanical Risk Classification in Lifting Tasks.Diagnostics (Basel, Switzerland) · 2022Article
- Classifying hazardous movements and loads during manual materials handling using accelerometers and instrumented insoles.Applied ergonomics · 2022Article
- A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset.Journal of imaging · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.