A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting

Lee, Wook-Gee; Karwowski, Waldemar; Marras, William S.; Rodrick, David

doi:10.1080/00140130303520

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…(e.g., Karwowski, Jarvinen, and Zurada 1992;Arcand 1994;Jung and Park 1994;Lim, Fok, and Tan 1996;Harden, Crumpton, and Killough 1996;Zurada, Karwowski, and Marras 1997;Carnahan and Redfern 1998;Lin and Hwang 1999;Chen, Kaber, and Dempsey 2000;Chung, Lee, Inseok, Dohyung, and Sang 2002;Kiryu, Shibai, Hayashi, and Tanaka 2002;Kaya et al 2003;Lee, Karwowski, Marras, and Rodrick 2003;Chen, Kaber, and Dempsey 2004;Kolich, Seal, and Taboun 2004). Chen et al (2004) used neural networks for data classification problems.…”

Section: Literature On the Use Of Artificial Neural Network For Ergomentioning

confidence: 97%

A Neural Network-Based System for Prediction of Computer User Comfort

Seçkiner

2009

Applied Artificial Intelligence

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This article proposes a neural network-based system for prediction of computer user comfort with respect to the existing settings of the workstation. In this context, anthropometric measures and the existing measures of a computer workstation were related to back-support comfort, distance comfort, keyboard comfort, monitor comfort, and seat comfort using two distinct modeling approaches-multiple linear regression and artificial neural network. The purpose of this article was to compare and contrast the resulting models. The data from 144 computer workstations were used and a total of 12 different data types such as shoulder to elbow, eye to buttock, pan height, monitor height, or distance from the chair were recorded. While multiple linear regression could not be used to adequately predict the computer workstation comfort, the neural network was deemed superior. This approach allows ergonomists to aid in the decisionmaking process of computer environment design and the prediction of the health risk in an occupational environment.Computer-related repetitive disorders have become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In previous years, the prevalence of neck and upper extremity complaints among computer users is high (Tittiranonda, Burastero, and Rempel 1999;Flodgren, Heiden, Lyskov, and Crenshaw 2007). Studies have shown that factors such as repetitive and stereotype movements, fixed postures, static load, insufficient recovery time, muscular fatigue, and psychosocial stress increase the risk of developing musculoskeletal symptoms (Flodgren et al. 2007;Tittiranonda et al. 1999;Jensen 2003;Juul-Kristensen, Søgaard, Strøyer, and Jensen 2004).In the wake of the expanding use of computers, concerns have been expressed about their potential health effects. Researches have shown

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Section: Literature On the Use Of Artificial Neural Network For Ergomentioning

confidence: 97%

A Neural Network-Based System for Prediction of Computer User Comfort

Seçkiner

2009

Applied Artificial Intelligence

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“…This makes it difficult to determine current system states and predict future states. HF research sometimes refers to this attribute (or elements of it) as “fuzziness” (e.g., Bell & Crumpton, 1997 ; Karwowski & Ayoub, 1984 ; Karwowski, 2000 ; Lee et al, 2003 ; Luczak & Ge, 1989 ). Uncertainty reflects the fact that artifacts of HF problems do not always occupy neat categories.…”

Section: Theoretical Underpinningsmentioning

confidence: 99%

The Binary-Based Model (BBM) for Improved Human Factors Method Selection

Holman¹,

Walker²,

Lansdown

et al. 2020

Hum Factors

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Objective This paper presents the Binary-Based Model (BBM), a new approach to Human Factors (HF) method selection. The BBM helps practitioners select the most appropriate HF methodology in relation to the complexity within the target system. Background There are over 200 HF methods available to the practitioner and little guidance to help choose between them. Method The BBM defines a HF “problem space” comprising three complexity attributes. HF problems can be rated against these attributes and located in the “problem space.” In addition, a similar HF “approach space” in which 66 predictive methods are rated according to their ability to confront those attributes is defined. These spaces are combined into a “utility space” in which problems and methods coexist. In the utility space, the match between HF problems and methods can be formally assessed. Results The method space is split into octants to establish broad groupings of methods distributed throughout the space. About 77% of the methods reside in Octant 1 which corresponds to problems with low levels of complexity. This demonstrates that most HF methods are suited to problems in low-complexity systems. Conclusion The location of 77% of the rated methods in Octant 1 indicates that HF practitioners are underserved with methods for analysis of HF problems exhibiting high complexity. Application The BBM can be used by multidisciplinary teams to select the most appropriate HF methodology for the problem under analysis. All the materials and analysis are placed in the public domain for modification and consensus building by the wider HF community.

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“…Soft‐computing techniques have been applied successfully to a wide range of problems in different domains, and several authors have performed a substantial number of applications in EMG‐related prediction and classification models. Soft‐computing techniques can be performed separately or jointly to assess the relationship between EMG signals and kinetics/kinematics variables (Brzostowski, ; Hou et al., ; Hou et al., ; Karwowski et al., ; Lee et al., ; Liu and Young, , ). In addition to EMG modeling, soft‐computing models have been applied by several authors to classify complicated EMG patterns such as hand motions (Karimi, Pourghassem, & Shahgholian, ; Karlik, Tokhi, & Alci, ; Khezri & Jahed, , ; Khushaba & Al‐Jumaily, ; Matsumura, Fukumi, & Akamatsu, ; Oskoei & Hu, ; Shi, Cai, Zhu, Zhong, & Wang, ; Wang, Yan, Hu, Xie, & Wang, ; Yan, Wang, & Xie, ; Zalzala & Chaiyaratana ; Zhang, Yang, Xu, & Zhang, ), wrist motions (Qingju & Kai, ; Tohi, Mitsukura, Yazama, & Fukumi, ; Yazama, Fukumi, Mitsukura, & Akamatsu, ), leg motions (Hussein & Granat, ), arm motions (Balbinot & Favieiro, ; Micera, Sabatini, Dario, & Rossi, ; Micera, Sabatini, & Dario, ), and finger motions (Kanitz, Antfolk, Cipriani, Sebelius, & Carrozza, ).…”

Section: Introductionmentioning

confidence: 99%

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

Çakıt

Karwowski

2017

Hum Ftrs & Erg Mfg Svc

Self Cite

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This study aimed to develop an adaptive neuro‐fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US‐based (54 males and 21 females) and 10 Japan‐based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft‐computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects’ demographic variables, posture, and muscle groups.

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A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting

Cited by 18 publications

References 36 publications

A Neural Network-Based System for Prediction of Computer User Comfort

A Neural Network-Based System for Prediction of Computer User Comfort

The Binary-Based Model (BBM) for Improved Human Factors Method Selection

Estimating electromyography responses using an adaptive neuro‐fuzzy inference system with subtractive clustering

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