Unlocking precision using k-means++- improved genetic algorithm-radial basis function neural network: data-driven evolution of smart gloves for gesture recognition
DOI:
https://doi.org/10.4025/actascitechnol.v47i1.70901Palavras-chave:
Data classification; human-computer interaction; adaptive; training accuracy; intelligent sensors.Resumo
Human-computer interaction technologies have been used since the 1970s but have only gained growing popularity in recent years with new design paradigms. Ongoing research and development in gesture recognition systems with broad application prospects have focused on improving accuracy and real-time performance as well as the robustness of specific machine learning algorithms against environmental conditions. This paper addresses the accuracy enhancement of a novel Fifth Dimension Technologies data-glove-based gesture recognition system using a genetic-algorithm (GA)-trained k-means++-improved radial basis function (RBF) or GK-RBF neural network. First, we analyzed and modeled the sensor distribution in the data glove and proposed joint constraints based on the finger joint angle and sensor mapping. Then, we trained the model and conducted experimental verification to demonstrate the model´s excellent real-time performance. Our results showed a training accuracy of 100%, a reduction in training error rate by 89.3%, and an accuracy rate improvement of at least 3.5% between the different static gestures, even with different operators. Specifically, the GK-RBF neural network outperforms the RBF and GA-modified models by 4.36 and 2.21 abs.%, respectively, in terms of recognition accuracy. The 99.85-% accuracy rate of 10-fold cross validation proves a high degree of compatibility with data-glove-based recognition systems.
Downloads
Não há dados estatísticos.
Referências
Alnuaim, A., Zakariah, M., Hatamleh, W. A., Tarazi, H., Tripathi, V., & Amoatey, E. T. (2022). Human-Computer Interaction with Hand Gesture Recognition Using ResNet and MobileNet. Computational Intelligence and Neuroscience, 8777355. https://doi.org/10.1155/2022/8777355
Arkenbout, E. A., De Winter, J. C. F., & Breedveld, P. (2015). Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements. Sensors, 15(12), 31644-31671. https://www.mdpi.com/1424-8220/15/12/29868
Bawazeer, S. A., Baakeem, S. S., & Mohamad, A. (2019). A New Radial Basis Function Approach Based on Hermite Expansion with Respect to the Shape Parameter. Mathematics, 7(10), 979. https://www.mdpi.com/2227-7390/7/10/979
Bouzit, M., Burdea, G., Popescu, G., & Boian, R. (2002). The Rutgers Master II-new design force-feedback glove. IEEE/ASME Transactions on Mechatronics, 7(2), 256-263. https://doi.org/10.1109/TMECH.2002.1011262
Broomhead, D. S., & Lowe, D. (1988). Multivariable Functional Interpolation and Adaptive Networks. Complex Systems, 2.
Byun, S.-W., & Lee, S.-P. (2019). Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array. Micromachines, 10(10), 692. https://www.mdpi.com/2072-666X/10/10/692
Chen, H., & Bakshi, B. R. (2009). 3.12 - Linear Approaches for Nonlinear Modeling. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics (pp. 453-462). Elsevier. https://doi.org/https://doi.org/10.1016/B978-044452701-1.00060-0
Chevtchenko, S. F., Vale, R. F., & Macario, V. (2018). Multi-objective optimization for hand posture recognition. Expert Systems with Applications, 92, 170-181. https://doi.org/https://doi.org/10.1016/j.eswa.2017.09.046
Cruz, P. J., Vásconez, J. P., Romero, R., Chico, A., Benalcázar, M. E., Ílvarez, R., Barona López, L. I., & Valdivieso Caraguay, Í. L. (2023). A Deep Q-Network based hand gesture recognition system for control of robotic platforms. Scientific Reports, 13(1), 7956. https://doi.org/10.1038/s41598-023-34540-x
Dang, X. K., Do, V. D., & Nguyen, X. P. (2020). Robust Adaptive Fuzzy Control Using Genetic Algorithm for Dynamic Positioning System. IEEE Access, 8, 222077-222092. https://doi.org/10.1109/ACCESS.2020.3043453
Domashova, J. V., Emtseva, S. S., Fail, V. S., & Gridin, A. S. (2021). Selecting an optimal architecture of neural network using genetic algorithm. Procedia Computer Science, 190, 263-273. https://doi.org/https://doi.org/10.1016/j.procs.2021.06.036
Ekemeyong Awong, L. E., & Zielinska, T. (2023). Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification. Sensors, 23(18), 7925. https://www.mdpi.com/1424-8220/23/18/7925
Feng, X., Zhao, J., & Kita, E. (2021). Genetic Algorithm-based Optimization of Deep Neural Network Ensemble. The Review of Socionetwork Strategies, 15(1), 27-47. https://doi.org/10.1007/s12626-021-00074-9
Fu, Y., Yuan, K., Zhu, H., & Du, Q. (2004). Motion Modeling and Software System Design for CAS-Glove Data Glove. Journal of System Simulation, 16(4), 660-662.
He, B., Wang, Y., Song, W., & Tang, W. (2014). Design resource management for virtual prototyping in product collaborative design. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(12), 2284-2300. https://doi.org/10.1177/0954405414551106
Hicks, S. C., Liu, R., Ni, Y., Purdom, E., & Risso, D. (2021). mbkmeans: Fast clustering for single cell data using mini-batch k-means. PLOS Computational Biology, 17(1), e1008625. https://doi.org/10.1371/journal.pcbi.1008625
Huang, P., Yao, P., Hao, Z., Peng, H., & Guo, L. (2021). Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge. Mathematics, 9(19), 2390. https://www.mdpi.com/2227-7390/9/19/2390
Huang, S. C., Jiau, M. K., & Lin, C. H. (2015). Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm. IEEE Transactions on Fuzzy Systems, 23(5), 1698-1712. https://doi.org/10.1109/TFUZZ.2014.2374194
Huang, Z., Peng, B., & Wu, J. (2013). Research and application of human-computer interaction system based on gesture recognition technology. In D. Li, & Y. Chen (Eds.), Computer and Computing Technologies in Agriculture (IFIP Advances in Information and Communication Technology, Vol. 392). Springer. https://doi.org/10.1007/978-3-642-36124-1_26
Huu, P. N., & Phung Ngoc, T. (2021). Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System. Journal of Robotics, 2021, 3986497. https://doi.org/10.1155/2021/3986497
Jia, W., Zhao, D., Shen, T., Su, C., Hu, C., & Zhao, Y. (2014). A New Optimized GA-RBF Neural Network Algorithm. Computational Intelligence and Neuroscience, 982045. https://doi.org/10.1155/2014/982045
Kapuscinski, T., Oszust, M., Wysocki, M., & Warchol, D. (2015). Recognition of hand gestures observed by depth cameras. International Journal of Advanced Robotic Systems, 12(4), 36. https://doi.org/10.5772/60091
Kim, H., Kim, H. K., & Cho, S. (2020). Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling. Expert Systems with Applications, 150, 113288. https://doi.org/10.1016/j.eswa.2020.113288
Lindner, T., Wyrwał, D., & Milecki, A. (2023). An Autonomous Humanoid Robot Designed to Assist a Human with a Gesture Recognition System. Electronics, 12(12), 2652. https://www.mdpi.com/2079-9292/12/12/2652
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281-297). University of California Press. http://projecteuclid.org/euclid.bsmsp/1200512992
Mahbub, U., Imtiaz, H., Roy, T., Rahman, M. S., & Rahman Ahad, M. A. (2013). A template matching approach of one-shot-learning gesture recognition. Pattern Recognition Letters, 34(15), 1780-1788. https://doi.org/10.1016/j.patrec.2012.09.014
Pisharady, P. K., & Saerbeck, M. (2015). Recent methods and databases in vision-based hand gesture recognition: A review. Computer Vision and Image Understanding, 141, 152-165. https://doi.org/10.1016/j.cviu.2015.08.004
Rautaray, S. S., & Agrawal, A. (2015). Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review, 43(1), 1-54. https://doi.org/10.1007/s10462-012-9356-9
Saha, S., Konar, A., & Roy, J. (2015). Single Person Hand Gesture Recognition Using Support Vector Machine. In K. Maharatna, G. K. Dalapati, P. K. Banerjee, A. K. Mallick, & M. Mukherjee, Computational Advancement in Communication Circuits and Systems New Delhi.
Siddiqui, U. A., Ullah, F., Iqbal, A., Khan, A., Ullah, R., Paracha, S., Shahzad, H., & Kwak, K.-S. (2021). Wearable-sensors-based platform for gesture recognition of autism spectrum disorder children using machine learning algorithms. Sensors, 21(10), 3319. https://www.mdpi.com/1424-8220/21/10/3319
Siegert, I., Haase, M., Prylipko, D., & Wendemuth, A. (2014). Discourse particles and user characteristics in naturalistic human-computer interaction. Human-Computer Interaction; Advanced Interaction Modalities and Techniques, Cham.
Smith, J. W., Thiagarajan, S., Willis, R., Makris, Y., & Torlak, M. (2021). Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique. IEEE Access, 9, 10893-10902. https://doi.org/10.1109/ACCESS.2021.3051454
Soltani, A. A., & El-Hag, A. (2021). A new radial basis function neural network-based method for denoising of partial discharge signals. Measurement, 172, 108970. https://doi.org/10.1016/j.measurement.2021.108970
Song, C. (2014). Android-based remote-control with real-time video surveillance for wi-fi robot. In Y. Yuan, X. Wu, & Y. Lu, Trustworthy computing and services (Communications in Computer and Information Science, Vol. 426). Springer. https://doi.org/10.1007/978-3-662-43908-1_47
Sturman, D. J., & Zeltzer, D. (1994). A survey of glove-based input. IEEE Computer Graphics and Applications, 14(1), 30-39. https://doi.org/10.1109/38.250916
Tubaiz, N., Shanableh, T., & Assaleh, K. (2015). Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode. IEEE Transactions on Human-Machine Systems, 45(4), 526-533. https://doi.org/10.1109/THMS.2015.2406692
Wang, D., Ohnishi, K., & Xu, W. (2020). Multimodal Haptic Display for Virtual Reality: A Survey. IEEE Transactions on Industrial Electronics, 67(1), 610-623. https://doi.org/10.1109/TIE.2019.2920602
Wang, S., Gittens, A., & Mahoney, M. W. (2019). Scalable kernel k-means clustering with Nystrí¶m approximation: relative-error bounds. Journal of Machine Learning Research, 20(1), 431-479.
Yang, X., Xi, W., Sun, Y., Zeng, T., Long, T., & Sarkar, T. K. (2015). Optimization of Subarray Partition for Large Planar Phased Array Radar Based on Weighted K-Means Clustering Method. IEEE Journal of Selected Topics in Signal Processing, 9(8), 1460-1468. https://doi.org/10.1109/JSTSP.2015.2465306
Yang, Y., Wang, P., & Gao, X. (2022). A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling. Processes, 10(1), 140. https://www.mdpi.com/2227-9717/10/1/140
Zhang, J., & Qu, S. (2021). Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm. Complexity, 1718234. https://doi.org/10.1155/2021/1718234
Zhang, X., & Li, X. (2019). Dynamic Gesture Recognition Based on MEMP Network. Future Internet, 11(4), 91. https://www.mdpi.com/1999-5903/11/4/91
Zhang, X., Shen, X., & Ouyang, T. (2022). Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models. Applied Sciences, 12(19), 9402. https://www.mdpi.com/2076-3417/12/19/9402
Zhao, P., Zhang, Y., Ma, Y., Zhao, X., & Fan, X. (2023). Discriminatively embedded fuzzy K-Means clustering with feature selection strategy. Applied Intelligence, 53(16), 18959-18970. https://doi.org/10.1007/s10489-022-04376-5
Zhou, H., Wang, F., & Tao, P. (2018). t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations. Journal of Chemical Theory and Computation, 14(11), 5499-5510. https://doi.org/10.1021/acs.jctc.8b00652
Zimmerman, T. G. (1985). Optical flex sensor (Patent Number: 4,542,291). https://patentimages.storage.googleapis.com/50/2c/96/c80d0bd9571d92/US4542291.pdf
Arkenbout, E. A., De Winter, J. C. F., & Breedveld, P. (2015). Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements. Sensors, 15(12), 31644-31671. https://www.mdpi.com/1424-8220/15/12/29868
Bawazeer, S. A., Baakeem, S. S., & Mohamad, A. (2019). A New Radial Basis Function Approach Based on Hermite Expansion with Respect to the Shape Parameter. Mathematics, 7(10), 979. https://www.mdpi.com/2227-7390/7/10/979
Bouzit, M., Burdea, G., Popescu, G., & Boian, R. (2002). The Rutgers Master II-new design force-feedback glove. IEEE/ASME Transactions on Mechatronics, 7(2), 256-263. https://doi.org/10.1109/TMECH.2002.1011262
Broomhead, D. S., & Lowe, D. (1988). Multivariable Functional Interpolation and Adaptive Networks. Complex Systems, 2.
Byun, S.-W., & Lee, S.-P. (2019). Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array. Micromachines, 10(10), 692. https://www.mdpi.com/2072-666X/10/10/692
Chen, H., & Bakshi, B. R. (2009). 3.12 - Linear Approaches for Nonlinear Modeling. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics (pp. 453-462). Elsevier. https://doi.org/https://doi.org/10.1016/B978-044452701-1.00060-0
Chevtchenko, S. F., Vale, R. F., & Macario, V. (2018). Multi-objective optimization for hand posture recognition. Expert Systems with Applications, 92, 170-181. https://doi.org/https://doi.org/10.1016/j.eswa.2017.09.046
Cruz, P. J., Vásconez, J. P., Romero, R., Chico, A., Benalcázar, M. E., Ílvarez, R., Barona López, L. I., & Valdivieso Caraguay, Í. L. (2023). A Deep Q-Network based hand gesture recognition system for control of robotic platforms. Scientific Reports, 13(1), 7956. https://doi.org/10.1038/s41598-023-34540-x
Dang, X. K., Do, V. D., & Nguyen, X. P. (2020). Robust Adaptive Fuzzy Control Using Genetic Algorithm for Dynamic Positioning System. IEEE Access, 8, 222077-222092. https://doi.org/10.1109/ACCESS.2020.3043453
Domashova, J. V., Emtseva, S. S., Fail, V. S., & Gridin, A. S. (2021). Selecting an optimal architecture of neural network using genetic algorithm. Procedia Computer Science, 190, 263-273. https://doi.org/https://doi.org/10.1016/j.procs.2021.06.036
Ekemeyong Awong, L. E., & Zielinska, T. (2023). Comparative Analysis of the Clustering Quality in Self-Organizing Maps for Human Posture Classification. Sensors, 23(18), 7925. https://www.mdpi.com/1424-8220/23/18/7925
Feng, X., Zhao, J., & Kita, E. (2021). Genetic Algorithm-based Optimization of Deep Neural Network Ensemble. The Review of Socionetwork Strategies, 15(1), 27-47. https://doi.org/10.1007/s12626-021-00074-9
Fu, Y., Yuan, K., Zhu, H., & Du, Q. (2004). Motion Modeling and Software System Design for CAS-Glove Data Glove. Journal of System Simulation, 16(4), 660-662.
He, B., Wang, Y., Song, W., & Tang, W. (2014). Design resource management for virtual prototyping in product collaborative design. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 229(12), 2284-2300. https://doi.org/10.1177/0954405414551106
Hicks, S. C., Liu, R., Ni, Y., Purdom, E., & Risso, D. (2021). mbkmeans: Fast clustering for single cell data using mini-batch k-means. PLOS Computational Biology, 17(1), e1008625. https://doi.org/10.1371/journal.pcbi.1008625
Huang, P., Yao, P., Hao, Z., Peng, H., & Guo, L. (2021). Improved Constrained k-Means Algorithm for Clustering with Domain Knowledge. Mathematics, 9(19), 2390. https://www.mdpi.com/2227-7390/9/19/2390
Huang, S. C., Jiau, M. K., & Lin, C. H. (2015). Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm. IEEE Transactions on Fuzzy Systems, 23(5), 1698-1712. https://doi.org/10.1109/TFUZZ.2014.2374194
Huang, Z., Peng, B., & Wu, J. (2013). Research and application of human-computer interaction system based on gesture recognition technology. In D. Li, & Y. Chen (Eds.), Computer and Computing Technologies in Agriculture (IFIP Advances in Information and Communication Technology, Vol. 392). Springer. https://doi.org/10.1007/978-3-642-36124-1_26
Huu, P. N., & Phung Ngoc, T. (2021). Hand Gesture Recognition Algorithm Using SVM and HOG Model for Control of Robotic System. Journal of Robotics, 2021, 3986497. https://doi.org/10.1155/2021/3986497
Jia, W., Zhao, D., Shen, T., Su, C., Hu, C., & Zhao, Y. (2014). A New Optimized GA-RBF Neural Network Algorithm. Computational Intelligence and Neuroscience, 982045. https://doi.org/10.1155/2014/982045
Kapuscinski, T., Oszust, M., Wysocki, M., & Warchol, D. (2015). Recognition of hand gestures observed by depth cameras. International Journal of Advanced Robotic Systems, 12(4), 36. https://doi.org/10.5772/60091
Kim, H., Kim, H. K., & Cho, S. (2020). Improving spherical k-means for document clustering: Fast initialization, sparse centroid projection, and efficient cluster labeling. Expert Systems with Applications, 150, 113288. https://doi.org/10.1016/j.eswa.2020.113288
Lindner, T., Wyrwał, D., & Milecki, A. (2023). An Autonomous Humanoid Robot Designed to Assist a Human with a Gesture Recognition System. Electronics, 12(12), 2652. https://www.mdpi.com/2079-9292/12/12/2652
MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281-297). University of California Press. http://projecteuclid.org/euclid.bsmsp/1200512992
Mahbub, U., Imtiaz, H., Roy, T., Rahman, M. S., & Rahman Ahad, M. A. (2013). A template matching approach of one-shot-learning gesture recognition. Pattern Recognition Letters, 34(15), 1780-1788. https://doi.org/10.1016/j.patrec.2012.09.014
Pisharady, P. K., & Saerbeck, M. (2015). Recent methods and databases in vision-based hand gesture recognition: A review. Computer Vision and Image Understanding, 141, 152-165. https://doi.org/10.1016/j.cviu.2015.08.004
Rautaray, S. S., & Agrawal, A. (2015). Vision based hand gesture recognition for human computer interaction: a survey. Artificial Intelligence Review, 43(1), 1-54. https://doi.org/10.1007/s10462-012-9356-9
Saha, S., Konar, A., & Roy, J. (2015). Single Person Hand Gesture Recognition Using Support Vector Machine. In K. Maharatna, G. K. Dalapati, P. K. Banerjee, A. K. Mallick, & M. Mukherjee, Computational Advancement in Communication Circuits and Systems New Delhi.
Siddiqui, U. A., Ullah, F., Iqbal, A., Khan, A., Ullah, R., Paracha, S., Shahzad, H., & Kwak, K.-S. (2021). Wearable-sensors-based platform for gesture recognition of autism spectrum disorder children using machine learning algorithms. Sensors, 21(10), 3319. https://www.mdpi.com/1424-8220/21/10/3319
Siegert, I., Haase, M., Prylipko, D., & Wendemuth, A. (2014). Discourse particles and user characteristics in naturalistic human-computer interaction. Human-Computer Interaction; Advanced Interaction Modalities and Techniques, Cham.
Smith, J. W., Thiagarajan, S., Willis, R., Makris, Y., & Torlak, M. (2021). Improved Static Hand Gesture Classification on Deep Convolutional Neural Networks Using Novel Sterile Training Technique. IEEE Access, 9, 10893-10902. https://doi.org/10.1109/ACCESS.2021.3051454
Soltani, A. A., & El-Hag, A. (2021). A new radial basis function neural network-based method for denoising of partial discharge signals. Measurement, 172, 108970. https://doi.org/10.1016/j.measurement.2021.108970
Song, C. (2014). Android-based remote-control with real-time video surveillance for wi-fi robot. In Y. Yuan, X. Wu, & Y. Lu, Trustworthy computing and services (Communications in Computer and Information Science, Vol. 426). Springer. https://doi.org/10.1007/978-3-662-43908-1_47
Sturman, D. J., & Zeltzer, D. (1994). A survey of glove-based input. IEEE Computer Graphics and Applications, 14(1), 30-39. https://doi.org/10.1109/38.250916
Tubaiz, N., Shanableh, T., & Assaleh, K. (2015). Glove-Based Continuous Arabic Sign Language Recognition in User-Dependent Mode. IEEE Transactions on Human-Machine Systems, 45(4), 526-533. https://doi.org/10.1109/THMS.2015.2406692
Wang, D., Ohnishi, K., & Xu, W. (2020). Multimodal Haptic Display for Virtual Reality: A Survey. IEEE Transactions on Industrial Electronics, 67(1), 610-623. https://doi.org/10.1109/TIE.2019.2920602
Wang, S., Gittens, A., & Mahoney, M. W. (2019). Scalable kernel k-means clustering with Nystrí¶m approximation: relative-error bounds. Journal of Machine Learning Research, 20(1), 431-479.
Yang, X., Xi, W., Sun, Y., Zeng, T., Long, T., & Sarkar, T. K. (2015). Optimization of Subarray Partition for Large Planar Phased Array Radar Based on Weighted K-Means Clustering Method. IEEE Journal of Selected Topics in Signal Processing, 9(8), 1460-1468. https://doi.org/10.1109/JSTSP.2015.2465306
Yang, Y., Wang, P., & Gao, X. (2022). A Novel Radial Basis Function Neural Network with High Generalization Performance for Nonlinear Process Modelling. Processes, 10(1), 140. https://www.mdpi.com/2227-9717/10/1/140
Zhang, J., & Qu, S. (2021). Optimization of Backpropagation Neural Network under the Adaptive Genetic Algorithm. Complexity, 1718234. https://doi.org/10.1155/2021/1718234
Zhang, X., & Li, X. (2019). Dynamic Gesture Recognition Based on MEMP Network. Future Internet, 11(4), 91. https://www.mdpi.com/1999-5903/11/4/91
Zhang, X., Shen, X., & Ouyang, T. (2022). Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models. Applied Sciences, 12(19), 9402. https://www.mdpi.com/2076-3417/12/19/9402
Zhao, P., Zhang, Y., Ma, Y., Zhao, X., & Fan, X. (2023). Discriminatively embedded fuzzy K-Means clustering with feature selection strategy. Applied Intelligence, 53(16), 18959-18970. https://doi.org/10.1007/s10489-022-04376-5
Zhou, H., Wang, F., & Tao, P. (2018). t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations. Journal of Chemical Theory and Computation, 14(11), 5499-5510. https://doi.org/10.1021/acs.jctc.8b00652
Zimmerman, T. G. (1985). Optical flex sensor (Patent Number: 4,542,291). https://patentimages.storage.googleapis.com/50/2c/96/c80d0bd9571d92/US4542291.pdf
Downloads
Publicado
2025-03-25
Como Citar
Ding, L. X., Chee, K.-W. (Guanghui) ., Lu, H., Paul, A., Kim, J., & Lee, J.-M. (2025). Unlocking precision using k-means++- improved genetic algorithm-radial basis function neural network: data-driven evolution of smart gloves for gesture recognition. Acta Scientiarum. Technology, 47(1), e70901. https://doi.org/10.4025/actascitechnol.v47i1.70901
Edição
Seção
Engenharia Elétrica
Licença
Copyright (c) 2025 Acta Scientiarum. Technology
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
DECLARAÇíO DE ORIGINALIDADE E DIREITOS AUTORAIS
Declaro que o presente artigo é original, não tendo sido submetido í publicação em qualquer outro periódico nacional ou internacional, quer seja em parte ou em sua totalidade.
Os direitos autorais pertencem exclusivamente aos autores. Os direitos de licenciamento utilizados pelo periódico é a licença Creative Commons Attribution 4.0 (CC BY 4.0): são permitidos o compartilhamento (cópia e distribuição do material em qualqer meio ou formato) e adaptação (remix, transformação e criação de material a partir do conteúdo assim licenciado para quaisquer fins, inclusive comerciais.
Recomenda-se a leitura desse link para maiores informações sobre o tema: fornecimento de créditos e referências de forma correta, entre outros detalhes cruciais para uso adequado do material licenciado.
0.8
2019CiteScore
36th percentile
Powered by 
0.8
2019CiteScore
36th percentile
Powered by
