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Article
Publication date: 16 April 2018

Vahab Khoshdel and Alireza Akbarzadeh

This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to…

298

Abstract

Purpose

This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters.

Design/methodology/approach

In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data.

Findings

There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation.

Originality/value

In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.

Details

Industrial Robot: An International Journal, vol. 45 no. 3
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 15 December 2017

Iman Kardan, Alireza Akbarzadeh and Ali Mousavi Mohammadi

This paper aims to increase the safety of the robots’ operation by developing a novel method for real-time implementation of velocity scaling and obstacle avoidance as the two…

358

Abstract

Purpose

This paper aims to increase the safety of the robots’ operation by developing a novel method for real-time implementation of velocity scaling and obstacle avoidance as the two widely accepted safety increasing concepts.

Design/methodology/approach

A fuzzy version of dynamic movement primitive (DMP) framework is proposed as a real-time trajectory generator with imbedded velocity scaling capability. Time constant of the DMP system is determined by a fuzzy system which makes decisions based on the distance from obstacle to the robot’s workspace and its velocity projection toward the workspace. Moreover, a combination of the DMP framework with a human-like steering mechanism and a novel configuration of virtual impedances is proposed for real-time obstacle avoidance.

Findings

The results confirm the effectiveness of the proposed method in real-time implementation of the velocity scaling and obstacle avoidance concepts in different cases of single and multiple stationary obstacles as well as moving obstacles.

Practical implications

As the provided experiments indicate, the proposed method can effectively increase the real-time safety of the robots’ operations. This is achieved by developing a simple method with low computational loads.

Originality/value

This paper proposes a novel method for real-time implementation of velocity scaling and obstacle avoidance concepts. This method eliminates the need for modification of original DMP formulation. The velocity scaling concept is implemented by using a fuzzy system to adjust the DMP’s time constant. Furthermore, the novel impedance configuration makes it possible to obtain a non-oscillatory convergence to the desired path, in all degrees of freedom.

Details

Industrial Robot: An International Journal, vol. 45 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 15 May 2017

Iman Kardan and Alireza Akbarzadeh

This paper aims to overcome some of the practical difficulties in assistive control of exoskeletons by developing a new assistive algorithm, called output feedback assistive…

300

Abstract

Purpose

This paper aims to overcome some of the practical difficulties in assistive control of exoskeletons by developing a new assistive algorithm, called output feedback assistive control (OFAC) method. This method does not require feedbacks from force, electromyography (EMG) or acceleration signals or even their estimated values.

Design/methodology/approach

The presented controller uses feedbacks from position and velocity of the output link of series elastic actuators (SEAs) to increase the apparent integral admittance of the assisted systems. Optimal controller coefficients are obtained by maximizing the assistance ratio subjected to constraints of stability, coupled stability and a newly defined comfort measure.

Findings

The results confirm the effectiveness of using the inherent properties of SEAs for removing the need for extra controversial sensors in assistive control of 1 degree of freedom (1-DOF) SEA powered exoskeletons. The results also clearly indicate the successful performance of the OFAC method in reducing the external forces required for moving the assisted systems.

Practical implications

As the provided experiments indicate, the proposed method can be easily applied to single DOF compliantly actuated exoskeletons to provide a more reliable assistance with lower costs. This is achieved by removing the need for extra controversial sensors.

Originality/value

This paper proposes a novel assistive controller for SEA-powered exoskeletons with a simple model-free structure and independent of any information about interaction forces and future paths of the system. It also removes the requirement for the extra sensors and transforms the assistive control of the compliantly actuated systems into a simpler problem of position control of the SEA motor.

Details

Industrial Robot: An International Journal, vol. 44 no. 3
Type: Research Article
ISSN: 0143-991X

Keywords

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Article
Publication date: 16 March 2012

Masoumeh Simbar, Fatemeh Nahidi, Mahrokh Dolatian and Alireza Akbarzadeh

Improving hospital service access and mothers' care are said to be the best approaches for decreasing maternal mortality. This study aims to evaluate prenatal care (PNC) and…

431

Abstract

Purpose

Improving hospital service access and mothers' care are said to be the best approaches for decreasing maternal mortality. This study aims to evaluate prenatal care (PNC) and suggest ways to improve hospital and health center maternity services.

Design/methodology/approach

This was a cross‐sectional descriptive study to evaluate prenatal care services in three domains: service structure; care process; and outcomes. Using non‐randomized quota sampling, 600 PNC clients were assessed in 12 pre‐natal clinics. Two checklists were used to assess facilities and care process and a questionnaire to assess client satisfaction. Validity and reliability were assessed and confirmed.

Findings

Six hundred subjects, averaging 29.3 (±9.4) weeks pregnancy, were included. Assessing different care processes demonstrated the following compliance to standards: counseling process 55.5 per cent (±21.2); history‐taking 48.71 per cent (±11.4); vital signs assessment 53.4 per cent (±10.6); general examination 30.2 per cent (±13.3); obstetrical examination 91.8 per cent (±27); blood tests 93.8 per cent (±21.9); urinary tests 86.9 per cent (±26.3); clients' education about peri‐natal‐risk 39.7 per cent (±27.1). Results showed that 0.54 per cent (±13.1) of clients were satisfied with care processes.

Research limitations/implications

Clients and providers were informed about the study's evaluation processes, which may have affected the results.

Practical implications

Study method and results can be used to improve PNC services.

Originality/value

This is the first time a study has focused on Iranian PNC structures, processes and outcomes.

Details

International Journal of Health Care Quality Assurance, vol. 25 no. 3
Type: Research Article
ISSN: 0952-6862

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Article
Publication date: 9 May 2020

Aziz Homayouni, Reza Rezaei Mokarram, Sharareh Norouzi, Alireza Dehnad, Ali Barkhordari, Hamideh Homayouni and Hadi Pourjafar

Among soy products, soy ice cream with neutral pH, high total solids contents and prebiotic oligosaccharides is an appropriate vehicle for probiotics. The purpose of this paper is…

235

Abstract

Purpose

Among soy products, soy ice cream with neutral pH, high total solids contents and prebiotic oligosaccharides is an appropriate vehicle for probiotics. The purpose of this paper is to survey soy ice cream as a carrier for the efficient delivering of Lactobacillus casei, or L. casei.

Design/methodology/approach

Probiotic soy ice cream containing L. casei was produced via the powder of soy milk. The physicochemical and organoleptic properties of the product were assessed. Also, the viability of L. casei was surveyed over a 180-day period of storage at −25 °C.

Findings

The density characteristic of probiotic soy ice cream demonstrated a significant rise (P < 0.05). The result of the viability analysis showed significant alterations in the number of probiotics in this product after freezing and throughout the 180-day period (P < 0.05). The most noticeable drop was seen throughout the first 60 days about 1.83 logs after that the trend of survival of this probiotic strain leveled off over the next 120 days. Also, no significant differences were found in the organoleptic properties of both ice creams.

Originality/value

Soy ice cream with prebiotic elements protected the growing and activity of probiotic bacteria. The results showed that L. casei is a good probiotic for soy ice cream.

Details

Nutrition & Food Science , vol. 51 no. 1
Type: Research Article
ISSN: 0034-6659

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Article
Publication date: 10 November 2020

Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity…

728

Abstract

Purpose

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

Design/methodology/approach

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

Findings

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

Research limitations/implications

The research data were limited to only one e-clothing store.

Practical implications

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

Originality/value

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

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