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

Donisi, Leandro; Cesarelli, Giuseppe; Coccia, Armando; Panigazzi, Monica; Capodaglio, E; D’Addio, Giovanni

doi:10.3390/s21082593

Cited by 48 publications

(24 citation statements)

References 67 publications

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“…Several tools can be used to perform ML analyses: Tougui et al performed a study on these tools in the context of heart disease classification [41] and identified Knime analytics platform as the best tool in terms of data manipulation, creating complex workflows, parameter tuning, and control of the algorithms. Moreover, this tool has already been used to perform biomedical studies also in fields such as ophthalmology and signal processing [42][43][44], and in cardiology [45,46].…”

Section: Machine Learning: Tool and Algorithmsmentioning

confidence: 99%

Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

et al. 2021

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Heart-rate variability has proved a valid tool in prognosis definition of patients with congestive heart failure (CHF). Previous research has documented Poincaré plot analysis as a valuable approach to study heart-rate variability performance among different subjects. In this paper, we explored the possibility to feed machine-learning (ML) algorithms using unconventional quantitative parameters extracted from Poincaré plots (generated from 24-h electrocardiogram recordings) to classify patients with CHF belonging to different New York Heart Association (NYHA) classes. We performed in sequence the following investigations: first, a statistical analysis was carried out on 9 morphological parameters, automatically measured from Poincaré plots. Subsequently, a feature selection through a wrapper with a 10-fold cross-validation method was performed to find the best subset of features which maximized the classification accuracy for each considered ML algorithm. Finally, patient classification was assessed through a ML analysis using AdaBoost of Decision Tree, k-Nearest Neighbors and Naive Bayes algorithms. A univariate statistical analysis proved 5 out of 9 parameters presented statistically significant differences among patients of distinct NYHA classes; similarly, a multivariate logistic regression confirmed the importance of the parameter ρy in the separability between low-risk and high-risk classes. The ML analysis achieved promising results in terms of evaluation metrics (especially the Naive Bayes algorithm), with accuracies greater than 80% and Area Under the Receiver Operating Curve indices greater than 0.7 for the overall three algorithms. The study indicates the proposed features have a predictive power to discriminate the NYHA classes, to which the features seem evenly correlated. Despite the NYHA classification being subjective and easily recognized by cardiologists, the potential relevance in the clinical cardiology of the proposed features and the promising ML results implies the methodology could be a valuable approach to automatically classify CHF. Future investigations on enriched datasets may further confirm the presented evidence.

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Section: Machine Learning: Tool and Algorithmsmentioning

confidence: 99%

Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

et al. 2021

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“…The proposed models may be able to be improved by utilizing body kinematic data. Such data were used to estimate biomechanical risk factors for lifting in two previous studies [35], [36]. The Varrecchia et al study classified the lifting index of the RNLE using kinematic features collected by a reflective marker-based motion capture system, while the Dopnisi et al study predicted one lifting index level using one IMU device on the lower back.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Detection and Risk Assessment of Lifting Action

Thomas

Jha

et al. 2022

IEEE Trans. Human-Mach. Syst.

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Repetitive occupational lifting has been shown to create an increased risk for incidence of back pain. Ergonomic workstations that promote proper lifting technique can reduce risk, but it is difficult to assess the workstations without constant risk monitoring. Machine learning systems using inertial measurement unit (IMU) data have been successful in various human activity recognition (HAR) applications, but limited work has been done regarding tasks for which it is difficult to collect significant amounts of data, such as manual lifting tasks. In this article, we discuss why traditional methods of data expansion may fail to improve performance on IMU data, and we present a machine learning system capable of detecting lifting action for assessing the risk for back pain using a relatively small amount of data. The proposed models outperform baseline HAR models and function on raw time-series data with minimal preprocessing for efficient real-time application.

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“…ML algorithms were implemented through the artificial intelligence platform Knime Analytics Platform (version 3.7.1), which is increasingly diffused in the scientific literature [ 38 , 39 , 40 ] and has achieved an interesting performance when compared with other platforms and programming languages.…”

Section: Methodsmentioning

confidence: 99%

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

et al. 2021

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Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

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

Cited by 48 publications

References 67 publications

Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

Extracting Features from Poincaré Plots to Distinguish Congestive Heart Failure Patients According to NYHA Classes

Machine Learning for Detection and Risk Assessment of Lifting Action

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

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