Biomedical Engineering Department, College of Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.
Professorship of Measurement and Sensor Technology, Technische Universitaet Chemnitz, Chemnitz, 09126, Germany.
Comput Biol Med. 2024 Sep;179:108817. doi: 10.1016/j.compbiomed.2024.108817. Epub 2024 Jul 15.
Force myography (FMG) is increasingly gaining importance in gesture recognition because of it's ability to achieve high classification accuracy without having a direct contact with the skin. In this study, we investigate the performance of a bracelet with only six commercial force sensitive resistors (FSR) sensors for classifying many hand gestures representing all letters and numbers from 0 to 10 in the American sign language. For this, we introduce an optimized feature selection in combination with the Extreme Learning Machine (ELM) as a classifier by investigating three swarm intelligence algorithms, which are the binary grey wolf optimizer (BGWO), binary grasshopper optimizer (BGOA), and binary hybrid grey wolf particle swarm optimizer (BGWOPSO), which is used as an optimization method for ELM for the first time in this study. The findings reveal that the BGWOPSO, in which PSO supports the GWO optimizer by controlling its exploration and exploitation using inertia constant to improve the convergence speed to reach the best global optima, outperformed the other investigated algorithms. In addition, the results show that optimizing ELM with BGWOPSO for feature selection can efficiently improve the performance of ELM to enhance the classification accuracy from 32% to 69.84% for classifying 37 gestures collected from multiple volunteers and using only a band with 6 FSR sensors.
力肌电图（FMG）因其无需与皮肤直接接触即可实现高精度分类的能力，在手势识别中越来越受到重视。在这项研究中，我们研究了仅带有六个商用力敏电阻（FSR）传感器的手镯在分类代表从 0 到 10 的美国手语所有字母和数字的许多手势时的性能。为此，我们引入了一种优化的特征选择，结合极限学习机（ELM）作为分类器，通过研究三种群智能算法，即二进制灰狼优化器（BGWO）、二进制蚱蜢优化器（BGOA）和二进制混合灰狼粒子群优化器（BGWOPSO），首次将其用作 ELM 的优化方法。研究结果表明，BGWOPSO 表现优于其他研究算法，其中 PSO 通过使用惯性常数控制 GWO 优化器的探索和利用来提高收敛速度，以达到最佳全局最优值。此外，结果表明，使用 BGWOPSO 对 ELM 进行特征选择优化可以有效地提高 ELM 的性能，从而将 37 个由多个志愿者采集的手势的分类准确率从 32%提高到 69.84%，仅使用带有 6 个 FSR 传感器的一个带。
