A genetic algorithm for image reconstruction in electrical impedance tomography for gesture recognition

Hafsa, Mariem; Atitallah, Bilel Ben; Salah, Taha Ben; Amara, Najoua Essoukri Ben; Kanoun, Olfa

doi:10.1515/teme-2021-0126

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…By using non-blind search methods, this model could achieve image reconstruction. This model could use CNN for classification and achieve 92% accuracy [14]. Assisted by deep learning method, Yang J et al proposed a wearable tactile sensor, which could realize gesture recognition and interaction.…”

Section: Related Workmentioning

confidence: 99%

Product Design for the Elderly Based on Human-Computer Interaction in the Era of Big Data

Liu

2024

SCPE

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Since 21st century, social aging trend has become more and more serious. It needs accurately identify the nursing needs of elderly disabled people, who are characterized by inconvenient movement and unclear speech. How to solve these problems has become a key point in the field of elderly care and medical care. In response to this problem, this research has designed a human-computer interactive gesture recognition system for elderly nursing beds in the context of big data. The recognition rate of the fusion feature + support vector machine (SVM) classifier adopted in this study is higher than 90% for each category of gesture. On the test set, this method has an average recognition rate of 96.35%, which is much higher than that of single feature + SVM classifier. While other methods’ recognition rate is lower than 90%. The recognition rate of tag c (nursing bed posture turning left) with obvious gesture feature information is as high as 99.28%, and that of tag h (nursing bed posture bedpan lowering) with weak gesture feature is 93.65%. The human-computer interaction system has well realized the recognition intention of user’s dynamic and static gestures, achieved the goal set by the research, and the interaction form is natural and reliable. In the later research, we can further realize a more comprehensive, accurate and natural human-computer interaction product design through the multi-channel joint decision-making scheme to meet the needs of the elderly.

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Section: Related Workmentioning

confidence: 99%

Product Design for the Elderly Based on Human-Computer Interaction in the Era of Big Data

Liu

2024

SCPE

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“…Compared with the traditional method, GA achieved significantly better image quality. It had been implemented as an image reconstruction algorithm for gesture recognition [ 5 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many researchers have studied and discussed this issue. Hafsa et al (2022) proposed a GA for image reconstruction using a non-blind search method that considered prior knowledge about possible conductivity distributions in the initial search space. The algorithm's performance was evaluated regarding image quality and processing time and minimized the corresponding quality function to 0.0505 in 100 generations using non-blind search and uniform crossover/random variation.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Lin,

Hu,

Cheng

et al. 2024

PLoS ONE

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This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection’s images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system’s superiority. The results show that in all models, the sample output results’ actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model’s predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

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Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

Al‐Bashir,

Al‐Bataiha,

Hafsa

et al. 2024

Healthcare Tech Letters

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Electrical impedance tomography (EIT) is a promising non‐invasive imaging technique that visualizes the electrical conductivity of an anatomic structure to form based on measured boundary voltages. However, the EIT inverse problem for the image reconstruction is nonlinear and highly ill‐posed. Therefore, in this work, a simulated dataset that mimics the human thorax was generated with boundary voltages based on given conductivity distributions. To overcome the challenges of image reconstruction, an ensemble learning method was proposed. The ensemble method combines several convolutional neural network models, which are the simple Convolutional Neural Network (CNN) model, AlexNet, AlexNet with residual block, and the modified AlexNet model. The ensemble models’ weights selection was based on average technique giving the best coefficient of determination (R2 score). The reconstruction quality is quantitatively evaluated by calculating the root mean square error (RMSE), the coefficient of determination (R2 score), and the image correlation coefficient (ICC). The proposed method's best performance is an RMSE of 0.09404, an R2 score of 0.926186, and an ICC of 0.95783 using an ensemble model. The proposed method is promising as it can construct valuable images for clinical EIT applications and measurements compared to previous studies.

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A genetic algorithm for image reconstruction in electrical impedance tomography for gesture recognition

Cited by 5 publications

References 11 publications

Product Design for the Elderly Based on Human-Computer Interaction in the Era of Big Data

Product Design for the Elderly Based on Human-Computer Interaction in the Era of Big Data

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums

Electrical impedance tomography image reconstruction for lung monitoring based on ensemble learning algorithms1

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