Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 Nm (9% of peak moment) and increased up to 35.0 Nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 Nm (10% of peak moment), and not substantially different than that of the extensive calibration.

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Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

Current Pharmaceutical Design ◽

10.2174/1381612826666200427111309 ◽

2020 ◽

Vol 26 (33) ◽

pp. 4195-4205

Author(s):

Xiaoyu Ding ◽

Chen Cui ◽

Dingyan Wang ◽

Jihui Zhao ◽

Mingyue Zheng ◽

...

Keyword(s):

Prediction Model ◽

Large Scale ◽

Prediction Models ◽

Predictive Accuracy ◽

Lead Optimization ◽

Consensus Method ◽

Molecular Pair ◽

Bioactivity Prediction ◽

Compound Synthesis ◽

Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

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In the Moment: Fostering Mindfulness and Reducing Stressors in the Healthcare Workplace

HERD Health Environments Research & Design Journal ◽

10.1177/1937586720988243 ◽

2021 ◽

pp. 193758672098824

Author(s):

Shabboo Valipoor ◽

Sheila J. Bosch

Keyword(s):

Design Research ◽

Healthcare Professionals ◽

Well Being ◽

Job Dissatisfaction ◽

Future Research ◽

Design Strategies ◽

Environmental Interventions ◽

Work Related ◽

Healthcare Settings ◽

The Moment

While healthcare design research has primarily focused on patient outcomes, there is a growing recognition that environmental interventions could do more by promoting the overall quality of care, and this requires expanding the focus to the health and well-being of those who deliver care to patients. Healthcare professionals are under high levels of stress, leading to burnout, job dissatisfaction, and poor patient care. Among other tools, mindfulness is recommended as a way of decreasing stress and helping workers function at higher levels. This article aims to identify potential environmental strategies for reducing work-related stressors and facilitating mindfulness in healthcare settings. By examining existing evidence on workplace mindfulness and stress-reducing design strategies, we highlight the power of the physical environment in not only alleviating stressful conditions but intentionally encouraging a mindful perspective. Strategies like minimizing distractions or avoiding overstimulation in the healthcare environment can be more effective if implemented along with the provision of designated spaces for mindfulness-based programs. Future research may explore optimal methods and hospital workers’ preferences for environments that support mindfulness and stress management. The long-term goal of all these efforts is to enhance healthcare professionals’ well-being, reignite their professional enthusiasm, and help them be resilient in times of stress.

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System Dynamics Model Application for Ergonomic Assessment of Manual Material Handling Tasks

Modular and Offsite Construction (MOC) Summit Proceedings ◽

10.29173/mocs30 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hossein Abaeian ◽

Osama Moselhi ◽

Mohamad Al-Hussein

Keyword(s):

System Dynamics ◽

Material Handling ◽

Dynamic Equilibrium ◽

Project Managers ◽

Dynamics Modeling ◽

Work Related ◽

Manual Material Handling ◽

Compressive Loads ◽

Levels Of Automation ◽

Free Body

Despite increased levels of automation in manufacturing occupations in recent years, many activities are still performed through human intervention and involve Manual Material Handling (MMH), thus exposing workers to stress due to over-exertion and potential Work-Related Musculoskeletal Disorders (WRMSDs). An early ergonomic and physical demand assessment of work activities is critical to reducing exposure to risk and to maintaining desired levels of productivity. Biomechanics consists of applying concepts of static and dynamic equilibrium to different parts of the human musculoskeletal system using free-body diagrams to estimate muscle force and loads generated across the joints and tissues. System dynamics is a powerful tool applied in resolving complex problems with different influencing variables. This technique can help designers and managers to understand, evaluate and simulate the factors causing problems in the system. This paper presents the application of System Dynamics modeling to assess the biomechanical risks associated with manual material handling tasks. The case study presents predicted cumulative biomechanical compressive loads from material handling task and can assist project managers to understand and reduce exposure to ergonomic risks in the workplace.

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Development of a pest threshold decision support system for minimising damage to winter wheat from wheat bulb fly, Delia coarctata

10.1101/2021.03.13.435242 ◽

2021 ◽

Author(s):

Daniel J Leybourne ◽

Kate E Storer ◽

Pete Berry ◽

Steve Ellis

Keyword(s):

Decision Support ◽

Decision Support System ◽

Winter Wheat ◽

Prediction Model ◽

Support System ◽

Predictive Accuracy ◽

Control Strategies ◽

Wheat Crop ◽

Shoot Number ◽

The Uk

Graphical AbstractIn this article we describe two predictive models that can be used for the integrated management of wheat bulb fly. Our first model is a pest level prediction model and our second model predicts the number of shoots a winter wheat crop will achieve by the terminal spikelet developmental stage. We revise and update current wheat bulb fly damage thresholds and combine this with our two models to devise a tolerance-based decision support system that can be used to minimise the risk of crop damage by wheat bulb fly. SummaryWheat bulb fly, Delia coarctata, is an important pest of winter wheat in the UK, causing significant damage of up to 4 t ha-1. Accepted population thresholds for D. coarctata are 250 eggs m-2 for crops sown up to the end of October and 100 eggs m-2 for crops sown from November. Fields with populations of D. coarctata that exceed the thresholds are at higher risk of experiencing economically damaging pest infestations. In the UK, recent withdrawal of insecticides means that only a seed treatment is available for chemical control of D. coarctata, however this is only effective for late-sown crops (November onwards) and accurate estimations of annual population levels are required to ensure a seed treatment is applied if needed. As a result of the lack of post-drilling control strategies, the management of D. coarctata is becoming increasingly reliant on non-chemical methods of control. Control strategies that are effective in managing similar stem-boring pests of wheat include sowing earlier and using higher seed rates to produce crops with more shoots and greater tolerance to shoot damage.In this study we develop two predictive models that can be used for integrated D. coarctata management. The first is an updated pest level prediction model that predicts D. coarctata populations from meteorological parameters with a predictive accuracy of 70%, which represents a significant improvement on the previous D. coarctata population prediction model. Our second model predicts the maximum number of shoots for a winter wheat crop that would be expected at the terminal spikelet development stage. This shoot number model uses information about the thermal time from plant emergence to terminal spikelet, leaf phyllochron length, plant population, and sowing date to predict the degree of tolerance a crop will have against D. coarctata. The shoot number model was calibrated against data collected from five field experiments and tested against data from four experiments. Model testing demonstrated that the shoot number model has a predictive accuracy of 70%. A decision support system using these two models for the sustainable management of D. coarcata risk is described.

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Development and Validation of a Clostridium difficile Infection Risk Prediction Model

Infection Control and Hospital Epidemiology ◽

10.1086/658944 ◽

2011 ◽

Vol 32 (4) ◽

pp. 360-366 ◽

Cited By ~ 62

Author(s):

Erik R. Dubberke ◽

Yan Yan ◽

Kimberly A. Reske ◽

Anne M. Butler ◽

Joshua Doherty ◽

...

Keyword(s):

High Risk ◽

Clostridium Difficile ◽

Prediction Model ◽

Risk Prediction ◽

Predictive Accuracy ◽

Medical Center ◽

Tertiary Care ◽

Risk Prediction Model ◽

Brier Score ◽

Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

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QSAR Prediction Model to Search for Compounds with Selective Cytotoxicity Against Oral Cell Cancer

Medicines ◽

10.3390/medicines6020045 ◽

2019 ◽

Vol 6 (2) ◽

pp. 45 ◽

Cited By ~ 2

Author(s):

Junko Nagai ◽

Mai Imamura ◽

Hiroshi Sakagami ◽

Yoshihiro Uesawa

Keyword(s):

Prediction Model ◽

Anticancer Drugs ◽

Predictive Accuracy ◽

Cell Cancer ◽

External Validation ◽

Selective Toxicity ◽

Normal Cells ◽

Validation Set ◽

Structure Toxicity Relationship ◽

Toxicity Relationship

Background: Anticancer drugs often have strong toxicity against tumours and normal cells. Some natural products demonstrate high tumour specificity. We have previously reported the cytotoxic activity and tumour specificity of various chemical compounds. In this study, we constructed a database of previously reported compound data and predictive models to screen a new anticancer drug. Methods: We collected compound data from our previous studies and built a database for analysis. Using this database, we constructed models that could predict cytotoxicity and tumour specificity using random forest method. The prediction performance was evaluated using an external validation set. Results: A total of 494 compounds were collected, and these activities and chemical structure data were merged as database for analysis. The structure-toxicity relationship prediction model showed higher prediction accuracy than the tumour selectivity prediction model. Descriptors with high contribution differed for tumour and normal cells. Conclusions: Further study is required to construct a tumour selective toxicity prediction model with higher predictive accuracy. Such a model is expected to contribute to the screening of candidate compounds for new anticancer drugs.

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Prediction Model of Serum Lithium Concentrations

Pharmacopsychiatry ◽

10.1055/s-0043-116855 ◽

2017 ◽

Vol 51 (03) ◽

pp. 82-88 ◽

Cited By ~ 1

Author(s):

Kazunari Yoshida ◽

Hiroyuki Uchida ◽

Takefumi Suzuki ◽

Masahiro Watanabe ◽

Nariyasu Yoshino ◽

...

Keyword(s):

Standard Deviation ◽

Prediction Model ◽

Predictive Accuracy ◽

Compartment Model ◽

Absolute Error ◽

Oral Dosage ◽

Therapeutic Drug ◽

Suitable Model ◽

Blood Draw ◽

The Mean

Abstract Introduction Therapeutic drug monitoring is necessary for lithium, but clinical application of several prediction strategies is still limited because of insufficient predictive accuracy. We herein proposed a suitable model, using creatinine clearance (CLcr)-based lithium clearance (Li-CL). Methods Patients receiving lithium provided the following information: serum lithium and creatinine concentrations, time of blood draw, dosing regimen, concomitant medications, and demographics. Li-CL was calculated as a daily dose per trough concentration for each subject, and the mean of Li-CL/CLcr was used to estimate Li-CL for another 30 subjects. Serum lithium concentrations at the time of sampling were estimated by 1-compartment model with Li-CL, fixed distribution volume (0.79 L/kg), and absorption rate (1.5/hour) in the 30 subjects. Results One hundred thirty-one samples from 82 subjects (44 men; mean±standard deviation age: 51.4±16.0 years; body weight: 64.6±13.8 kg; serum creatinine: 0.78±0.20 mg/dL; dose of lithium: 680.2±289.1 mg/day) were used to develop the pharmacokinetic model. The mean±standard deviation (95% confidence interval) of absolute error was 0.13±0.09 (0.10–0.16) mEq/L. Discussion Serum concentrations of lithium can be predicted from oral dosage with high precision, using our prediction model.

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Checking the predictive accuracy of basic symptoms against ultra high-risk criteria and testing of a multivariable prediction model: Evidence from a prospective three-year observational study of persons at clinical high-risk for psychosis

European Psychiatry ◽

10.1016/j.eurpsy.2017.05.026 ◽

2017 ◽

Vol 45 ◽

pp. 27-35 ◽

Cited By ~ 10

Author(s):

M.P. Hengartner ◽

K. Heekeren ◽

D. Dvorsky ◽

S. Walitza ◽

W. Rössler ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Sensitivity And Specificity ◽

Predictive Value ◽

Predictive Accuracy ◽

Positive Symptoms ◽

Clinical High Risk ◽

Verbal Iq ◽

Basic Symptoms ◽

Ultra High Risk

AbstractBackground:The aim of this study was to critically examine the prognostic validity of various clinical high-risk (CHR) criteria alone and in combination with additional clinical characteristics.Methods:A total of 188 CHR positive persons from the region of Zurich, Switzerland (mean age 20.5 years; 60.2% male), meeting ultra high-risk (UHR) and/or basic symptoms (BS) criteria, were followed over three years. The test battery included the Structured Interview for Prodromal Syndromes (SIPS), verbal IQ and many other screening tools. Conversion to psychosis was defined according to ICD-10 criteria for schizophrenia (F20) or brief psychotic disorder (F23).Results:Altogether n = 24 persons developed manifest psychosis within three years and according to Kaplan–Meier survival analysis, the projected conversion rate was 17.5%. The predictive accuracy of UHR was statistically significant but poor (area under the curve [AUC] = 0.65, P < .05), whereas BS did not predict psychosis beyond mere chance (AUC = 0.52, P = .730). Sensitivity and specificity were 0.83 and 0.47 for UHR, and 0.96 and 0.09 for BS. UHR plus BS achieved an AUC = 0.66, with sensitivity and specificity of 0.75 and 0.56. In comparison, baseline antipsychotic medication yielded a predictive accuracy of AUC = 0.62 (sensitivity = 0.42; specificity = 0.82). A multivariable prediction model comprising continuous measures of positive symptoms and verbal IQ achieved a substantially improved prognostic accuracy (AUC = 0.85; sensitivity = 0.86; specificity = 0.85; positive predictive value = 0.54; negative predictive value = 0.97).Conclusions:We showed that BS have no predictive accuracy beyond chance, while UHR criteria poorly predict conversion to psychosis. Combining BS with UHR criteria did not improve the predictive accuracy of UHR alone. In contrast, dimensional measures of both positive symptoms and verbal IQ showed excellent prognostic validity. A critical re-thinking of binary at-risk criteria is necessary in order to improve the prognosis of psychotic disorders.

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Determining Changes in Electromyography Indices when Measuring Maximum Acceptable Weight of Lift in Iranian Male Students

Journal of Biomedical Physics and Engineering ◽

10.31661/jbpe.v8i1mar.443 ◽

2018 ◽

Vol 8 (1Mar) ◽

Author(s):

A Salehi Sahl Abadi ◽

A Mazloumi ◽

G Nasl Saraji ◽

H Zeraati ◽

M R Hadian ◽

...

Keyword(s):

Muscle Activity ◽

Material Handling ◽

Objective Assessment ◽

Wilcoxon Test ◽

Male Students ◽

Spearman Correlation ◽

Lifting Technique ◽

Spearman Correlation Test ◽

Left And Right ◽

External Oblique

Background: In spite of the increasing degree of automation in industry, manual material handling (MMH) is still performed in many occupational settings. The aim of the current study was to determine the maximum acceptable weight of lift using psychophysical and electromyography indices.Methods: This experimental study was conducted among 15 male students recruited from Tehran University of Medical Sciences. Each participant performed 18 different lifting tasks which involved three lifting frequencies, three lifting heights and two box sizes. Each set of experiments was conducted during the 20 min work period using free-style lifting technique and subjective as well as objective assessment methodologies. SPSS version 18 software was used for descriptive and analytical analyses by Friedman, Wilcoxon and Spearman correlation techniques.Results: The results demonstrated that muscle activity increased with increasing frequency, height of lift and box size (P<0.05). Meanwhile, MAWLs obtained in this study are lower than those in Snook table (P<0.05). In this study, the level of muscle activity in percent MVC in relation to the erector spine muscles in L3 and T9 regions as well as left and right abdominal external oblique muscles were at 38.89%, 27.78%, 11.11% and 5.55% in terms of muscle activity is more than 70% MVC, respectively. The results of Wilcoxon test revealed that for both small and large boxes under all conditions, significant differences were detected between the beginning and end of the test values for MPF of erector spine in L3 and T9 regions, and left and right abdominal external oblique muscles (P<0.05). The results of Spearman correlation test showed that there was a significant relation between the MAWL, RMS and MPF of the muscles in all test conditions (P<0.05).Conclusion: Based on the results of this study, it was concluded if muscle activity is more than 70% of MVC, the values of Snook tables should be revisited. Furthermore, the biomechanical perspective should receive special attention in determining the standards for MMH

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Development of a Lot-Sizing Model to Prevent Heat Stress and Work-Related Musculoskeletal Disorders

PROMET - Traffic&Transportation ◽

10.7307/ptt.v33i6.3837 ◽

2021 ◽

Vol 33 (6) ◽

pp. 871-882

Author(s):

Sezen Korkulu ◽

Krisztián Bóna

Keyword(s):

Heat Stress ◽

Production Line ◽

Material Handling ◽

Lot Sizing ◽

Metabolic Cost ◽

Heat Strain ◽

Total Cost ◽

Rest Time

Management of heat stress and metabolic cost is vital for preventing any work-related disorders. In this paper, we integrated rest time formulations for heat strain and metabolic cost to develop a new lot sizing model for preventing heat exposure and work-related musculoskeletal disorders. The effects of heat strain and rest allowance on the total cost of the production supply process were investigated. The problem studied in this paper was the handling of the raw materials placed in boxes by manual material handling in order to supply the material requirement of a production line placed in a production area. For the realisation of the material handling transactions between the raw material warehouse and the production line, Electric Pallet Jack (EPJ) was used. The study covers the investigation of picking, storing, and carrying motions for the manual handling of these materials. The result of the analysis has shown that 8.5% savings were achieved by using the heat strain and rest time in comparison to the total cost of this part of the production line supply process with the ISO 7243 maximum metabolic work limit. Consequentially, the analysis results showed that the developed method demonstrated the viability of lot sizing model optimisation with multiple objectives and complex constraints with regards to the metabolic cost and heat strain.

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