Abidi Mustufa Haider
Advanced Manufacturing Institute, King Saud University, Riyadh, 11421, Saudi Arabia.
King Salman Center for Disability Research, Riyadh, 11614, Saudi Arabia.
Sci Rep. 2024 Dec 24;14(1):30633. doi: 10.1038/s41598-024-82624-z.
Recently, social demands for a good quality of life have increased among the elderly and disabled people. So, biomedical engineers and robotic researchers aimed to fuse these techniques in a novel rehabilitation system. Moreover, these models utilized the biomedical signals acquired from the human body's particular organ, cells, or tissues. The human motion intention prediction mechanism plays an essential role in various applications, such as assistive and rehabilitation robots, that execute specific tasks among elders and physically impaired individuals. However, more complications are increased in the human-machine-based interaction techniques, creating more scope for personalized assistance for the human motion intention prediction system. Therefore, in this paper, an Adaptive Hybrid Network (AHN) is implemented for effective human motion intention prediction. Initially, multimodal data like electroencephalogram (EEG)/Electromyography (EMG) signals and sensor measures data are collected from the available data resource. The gathered EEG/EMG signals are then converted into spectrogram images and sent to AH-CNN-LSTM, which is the integration of an Adaptive Hybrid Convolution Neural Network (AH-CNN) with a Long Short-Term Memory (LSTM) network. Similarly, the data details of sensor measures are directly subjected to AH-CNN-Res-LSTM, which is the combination of Adaptive Hybrid CNN with Residual Network and LSTM (Res-LSTM) to get the predictive result. Further, to enhance the prediction, the parameters in both the AH-CNN-LSTM and AH-CNN-Res-LSTM techniques are optimized using the Improved Yellow Saddle Goatfish Algorithm (IYSGA). The efficiency of the implemented model is computed by conducting the comparison experiment of the proposed technique with other standard models. The performance outcome of the developed method outperformed the other traditional methods.
最近，老年人和残疾人对高质量生活的社会需求有所增加。因此，生物医学工程师和机器人研究人员旨在将这些技术融合到一种新型康复系统中。此外，这些模型利用了从人体特定器官、细胞或组织获取的生物医学信号。人体运动意图预测机制在各种应用中起着至关重要的作用，例如辅助和康复机器人，这些机器人在老年人和身体有缺陷的个体中执行特定任务。然而，基于人机交互的技术中出现了更多复杂情况，为人的运动意图预测系统的个性化辅助创造了更多空间。因此，在本文中，实现了一种自适应混合网络（AHN）用于有效的人体运动意图预测。首先，从可用数据资源中收集多模态数据，如脑电图（EEG）/肌电图（EMG）信号和传感器测量数据。然后将收集到的EEG/EMG信号转换为频谱图图像，并发送到AH-CNN-LSTM，它是自适应混合卷积神经网络（AH-CNN）与长短期记忆（LSTM）网络的集成。同样，传感器测量的数据细节直接输入AH-CNN-Res-LSTM，它是自适应混合CNN与残差网络和LSTM（Res-LSTM）的组合，以获得预测结果。此外，为了提高预测效果，使用改进的黄斑海鲷算法（IYSGA）对AH-CNN-LSTM和AH-CNN-Res-LSTM技术中的参数进行优化。通过将所提出的技术与其他标准模型进行对比实验来计算所实现模型的效率。所开发方法的性能结果优于其他传统方法。
