Non-invasive physical demand assessment using wearable respiration sensor and random forest classifier

Sadat-Mohammadi, Milad; Shakerian, Shahrad; Liu, Yizhi; Asadi, Somayeh; Jebelli, Houtan

doi:10.1016/j.jobe.2021.103279

Cited by 19 publications

(6 citation statements)

References 56 publications

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“…Notable examples include the iBreve brooch (iBreve, Dublin, Ireland) [77], ingeniously attached to the hem of a bra, and the Oxa (Oxa Life, Rumlang, Switzerland) [78], designed for placement on the chest during relaxation exercises. In the domain of commercially available BCG devices, Sadat-Mohammadi et al [79] presented a wearable respiration sensor based on an accelerometric sensor and random forest classifier and achieved an accuracy of up to 93.4% while being less sensitive to body and sensor movement artifacts. In the article by Tavakolian et al [80], the focus was on enhancing BCG processing through the incorporation of respiration information.…”

Section: Seismocardiography Ballistocardiography and Similar Methodsmentioning

confidence: 99%

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

Vitazkova,

Foltan,

Kosnacova

et al. 2024

Biosensors

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This article explores the importance of wearable and remote technologies in healthcare. The focus highlights its potential in continuous monitoring, examines the specificity of the issue, and offers a view of proactive healthcare. Our research describes a wide range of device types and scientific methodologies, starting from traditional chest belts to their modern alternatives and cutting-edge bioamplifiers that distinguish breathing from chest impedance variations. We also investigated innovative technologies such as the monitoring of thorax micromovements based on the principles of seismocardiography, ballistocardiography, remote camera recordings, deployment of integrated optical fibers, or extraction of respiration from cardiovascular variables. Our review is extended to include acoustic methods and breath and blood gas analysis, providing a comprehensive overview of different approaches to respiratory monitoring. The topic of monitoring respiration with wearable and remote electronics is currently the center of attention of researchers, which is also reflected by the growing number of publications. In our manuscript, we offer an overview of the most interesting ones.

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Section: Seismocardiography Ballistocardiography and Similar Methodsmentioning

confidence: 99%

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

Vitazkova,

Foltan,

Kosnacova

et al. 2024

Biosensors

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“…For instance, reports of the workers' general status could be sent to the occupational physician and alert workers and supervisors of any alarming situation. In the construction industry, some studies have already included this type of monitoring, determining task demand by resorting to respiratory measures [71]. Additionally, alert systems based on HRV were implemented to activate when signs of fatigue were detected, thereby informing supervisors that workers required a break [72].…”

Section: Practical Implicationsmentioning

confidence: 99%

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Furk,

Silva,

Dias

et al. 2024

Applied Sciences

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Repetitive tasks can lead to long-term cardiovascular problems due to continuous strain and inadequate recovery. The automobile operators on the assembly line are exposed to these risks when workload volume changes according to the workstation type. However, the current ergonomic assessments focus primarily on observational and, in some cases, biomechanical methods that are subjective and time-consuming, overlooking cardiorespiratory adaptations. This study aimed to analyze the cardiorespiratory response to distinct workload volumes and ergonomic risk (ER) scores for an automotive assembly line. Sixteen male operators (age = 38 ± 8 years; BMI = 25 ± 3 kg·m2) volunteered from three workstations (H1, H2, and H3) with specific work cycle duration (1, 3, and 5 min respectively). Electrocardiogram (ECG), respiratory inductance plethysmography (RIP), and accelerometer (ACC) data were collected during their shift. The results showed significant differences from the first to the last 10 min, where H3 had its SDRRi reduced (p = 0.014), H1’s phase synchrony and H2’s coordination between thoracic and abdominal movements decreased (p < 0.001, p = 0.039). In terms of ergonomic risk, the moderate-high rank showed a reduction in SDRRi (p = 0.037) and moderate-risk activities had diminished phase synchrony (p = 0.018) and correlation (p = 0.004). Thus, the explored parameters could have the potential to develop personalized workplace adaptation and risk assessment systems.

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“…When the relation between input and output data is nonlinear, the accuracy of ANNs is high. For this reason, when there are no rules among datasets, ANN would be the best option [42]. An ANN consists of three types of layers: (1) input layer, (2) hidden layer, and (3) output layer.…”

Section: Develop ML Models Description Of Ml Modelsmentioning

confidence: 99%

Optimizing Machine Learning Algorithms for Improving Prediction of Bridge Deck Deterioration: A Case Study of Ohio Bridges

Rashidi Nasab,

Elzarka

2023

Buildings

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The deterioration of a bridge’s deck endangers its safety and serviceability. Ohio has approximately 45,000 bridges that need to be monitored to ensure their structural integrity. Adequate prediction of the deterioration of bridges at an early stage is critical to preventing failures. The objective of this research was to develop an accurate model for predicting bridge deck conditions in Ohio. A comprehensive literature review has revealed that past researchers have utilized different algorithms and features when developing models for predicting bridge deck deterioration. Since, there is no guarantee that the use of features and algorithms utilized by past researchers would lead to accurate results for Ohio’s bridges, this research proposes a framework for optimizing the use of machine learning (ML) algorithms to more accurately predict bridge deck deterioration. The framework aims to first determine “optimal” features that can be related to deck deterioration conditions, specifically in the case of Ohio’s bridges by using various feature-selection methods. Two feature-selection models used were XGboost and random forest, which have been confirmed by the Boruta algorithm, in order to determine the features most relevant to deck conditions. Different ML algorithms were then used, based on the “optimal” features, to select the most accurate algorithm. Seven machine learning algorithms, including single models such as decision tree (DT), artificial neural networks (ANNs), k-nearest neighbors (k-NNs), logistic regression (LR), and support vector machines (SVRs), as well as ensemble models such as Random Forest (RF) and eXtreme gradient boosting (XGboost), have been implemented to classify deck conditions. To validate the framework, results from the ML algorithms that used the “optimal” features as input were compared to results from the same ML algorithms that used the “most common” features that have been used in previous studies. On a dataset obtained from the Ohio Department of Transportation (ODOT), the results indicated that the ensemble ML algorithms were able to predict deck conditions significantly more accurately than single models when the “optimal” features were utilized. Although the framework was implemented using data obtained from ODOT, it can be successfully utilized by other transportation agencies to more accurately predict the deterioration of bridge components.

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Non-invasive physical demand assessment using wearable respiration sensor and random forest classifier

Cited by 19 publications

References 56 publications

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

Advances in Respiratory Monitoring: A Comprehensive Review of Wearable and Remote Technologies

Cardiorespiratory Response to Workload Volume and Ergonomic Risk: Automotive Assembly Line Operators’ Adaptations

Optimizing Machine Learning Algorithms for Improving Prediction of Bridge Deck Deterioration: A Case Study of Ohio Bridges

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