Supervised ANN-assisted modeling of seated body apparent mass under vertical whole body vibration

Taghavifar, Hamid; Rakheja, Subhash

doi:10.1016/j.measurement.2018.05.092

Cited by 24 publications

(12 citation statements)

References 34 publications

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“…In his methodology instead of recording the subjective responses he compared them directly with the chart from the ISO2631 standard, as presented in this paper in table 5, which does not provide any insight in what the actual subject responses of the people in the vehicle might be. Another example of using supervised learning for ride comfort estimation in a laboratory environment is a study conducted at Concave research centre in Canada by Taghavifar et al [67], where the neural networks were used as a tool for apparent mass estimation and prediction tool. As ANNs can be used for discovering nonlinear correlations between parameters, Nybacka et al, used them to correlate objective metrics for steering with the obtained driver ratings [68].…”

Section: Artificial Neural Network and Ride Comfortmentioning

confidence: 99%

Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Cieslak

Kanarachos

Blundell

et al. 2019

Neural Comput & Applic

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Section: Artificial Neural Network and Ride Comfortmentioning

confidence: 99%

Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Cieslak

Kanarachos

Blundell

et al. 2019

Neural Comput & Applic

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“…BPNNs have also been applied to ergonomic research [29]. Taghavifar et al [30] developed a multilayer feedforward neural network combined with the backpropagation mathematical method to predict the apparent mass of a seated body to perform layout optimization at divergent vibration excitation levels. Artificial neural networks also have good application prospects in the field of ergonomic reliability [31].…”

Section: Introductionmentioning

confidence: 99%

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

Liu¹,

Liu²,

Huang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Ergonomic reliability plays a significant role in the safe operation of devices. With the spread of infectious diseases around the world, in work environments with high loads and high infection rates, medical staff work in a state of high self-protection. The use of visual display terminal (VDT) for medical equipment has undergone fundamental changes, and the traditional medical equipment human-machine interface design needs to be improved. After the completion of design and development, a VDT design enters the experimental testing stage, which has significant limitations for simulating the work of medical staff in the high-load and high-infection environments. The testing cost is high, and subjects face harsh conditions; thus, an ergonomic reliability model that can predict the use of VDT in such special high-infection and high-load circumstances must be established. An ergonomic reliability model based on an improved backpropagation neural network (BPNN) and human cognition reliability (HCR) is proposed for predicting and evaluating operation flows according to medical equipment VDTs. Firstly, a small data sample can be used to train BPNN to generate a network that can ensure suitable accuracy. To prevent the model from falling into local optimal solutions, the bat algorithm is introduced to improve the BPNN. Compared to a traditional BPNN, the superiority of the improved BPNN is clearly demonstrated. Secondly, the HCR method is used to analyze and highlight changes in the human factor reliability of VDTs for medical equipment in different time processes and operating processes according to BPNN prediction results, to provide a reference for selecting the optimal method. Finally, the validity and availability of the proposed method are verified through an eye tracker experiment and statistical analysis results.

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“…Artificial Intelligent techniques fall under this approach. Several algorithms can be used to mimic the model, such as Artificial Neural Network (ANN) [24][25][26][27], Autoregressive Moving Average (ARMA) [28,29], and Fuzzy Logic [30]. The choice between the two approached depend on the application and the objective of the study.…”

Section: Introductionmentioning

confidence: 99%

“…ANN is a well-known modelling tool that is used for prediction, pattern recognition, data fitting, and classifications of complex systems [24]. It possesses the ability to learn and generalize functions from rounds of training as well as extract essential information from data [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Hybrid artificial genetic – neural network model to predict the transmission of vibration to the head during whole-body vibration training

Al-Shabi¹,

Nawayseh²,

Bettayeb³

2020

J. vibroeng.

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In this work, Artificial Neural Network (ANN) modelling has been employed to investigate the effects of various factors on the biodynamic responses to vibration represented by the transmissibility and its phase. These factors include, height, weight, Body Mass Index (BMI), age, frequency and posture. Nine subjects stood on a vibrating plate and were exposed to vertical vibration at nine frequencies in the range 17-46 Hz while adopting four different standing postures; Bent Knee posture (BK), Locked Knee posture (LK), right foot to the Front and left foot to the Back posture (FB) and One Leg posture (OL). The accelerations of the vibrating plate and the head of the subjects were measured during the exposure to vibration in order to calculate the transmissibility between the vibrating plate and the head. Genetic Algorithm (GA) was used to choose ANN’s number of hidden layers and number of neurons in each layer to obtain the best performance for predicting the transmissibility. The GA compared the root mean square errors (RMSE) between the ANN outputs and the experimental outputs, and then choose the best results that could be achieved. The number of hidden layers and number of neurons tested in GA vary from one hidden layer to four hidden layers, and from one neuron per layer to one hundred neurons per layer. Several runs have been conducted to train and validate the ANN model. The results show that double hidden layer with 13 neurons in the first layer and 12 neurons in the second layer give the best candidate. The proposed model can be integrated with whole-body vibration machines in order to choose the suitable exposure based on the user’s characteristics.

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Supervised ANN-assisted modeling of seated body apparent mass under vertical whole body vibration

Cited by 24 publications

References 34 publications

Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Ergonomic Reliability Assessment of VDT System for Operation Design Based on Improved BPNN and HCR under Special Circumstances

Hybrid artificial genetic – neural network model to predict the transmission of vibration to the head during whole-body vibration training

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