Deeptha R, Ramkumar K, Venkateswaran Sri, Hassan Mohammad Mehedi, Hassan Md Rafiul, Noori Farzan M, Uddin Md Zia
Department of Information Technology, SRM Institute of Science and Technology, Chennai, Tamilnadu, India.
Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamilnadu, India.
Front Neuroinform. 2024 Nov 19;18:1454583. doi: 10.3389/fninf.2024.1454583. eCollection 2024.
Elderly and individuals with disabilities can greatly benefit from human activity recognition (HAR) systems, which have recently advanced significantly due to the integration of the Internet of Things (IoT) and artificial intelligence (AI). The blending of IoT and AI methodologies into HAR systems has the potential to enable these populations to lead more autonomous and comfortable lives. HAR systems are equipped with various sensors, including motion capture sensors, microcontrollers, and transceivers, which supply data to assorted AI and machine learning (ML) algorithms for subsequent analyses. Despite the substantial advantages of this integration, current frameworks encounter significant challenges related to computational overhead, which arises from the complexity of AI and ML algorithms. This article introduces a novel ensemble of gated recurrent networks (GRN) and deep extreme feedforward neural networks (DEFNN), with hyperparameters optimized through the artificial water drop optimization (AWDO) algorithm. This framework leverages GRN for effective feature extraction, subsequently utilized by DEFNN for accurately classifying HAR data. Additionally, AWDO is employed within DEFNN to adjust hyperparameters, thereby mitigating computational overhead and enhancing detection efficiency. Extensive experiments were conducted to verify the proposed methodology using real-time datasets gathered from IoT testbeds, which employ NodeMCU units interfaced with Wi-Fi transceivers. The framework's efficiency was assessed using several metrics: accuracy at 99.5%, precision at 98%, recall at 97%, specificity at 98%, and F1-score of 98.2%. These results then were benchmarked against other contemporary deep learning (DL)-based HAR systems. The experimental outcomes indicate that our model achieves near-perfect accuracy, surpassing alternative learning-based HAR systems. Moreover, our model demonstrates reduced computational demands compared to preceding algorithms, suggesting that the proposed framework may offer superior efficacy and compatibility for deployment in HAR systems designed for elderly or individuals with disabilities.
老年人和残疾人可以从人类活动识别（HAR）系统中受益匪浅，由于物联网（IoT）和人工智能（AI）的集成，该系统最近取得了显著进展。将物联网和人工智能方法融入HAR系统有可能使这些人群过上更自主、舒适的生活。HAR系统配备了各种传感器，包括运动捕捉传感器、微控制器和收发器，这些传感器将数据提供给各种人工智能和机器学习（ML）算法进行后续分析。尽管这种集成有诸多显著优势，但当前框架面临与计算开销相关的重大挑战，这是由人工智能和机器学习算法的复杂性导致的。本文介绍了一种新颖的门控循环神经网络（GRN）和深度极端前馈神经网络（DEFNN）的集成，其超参数通过人工水滴优化（AWDO）算法进行了优化。该框架利用GRN进行有效的特征提取，随后由DEFNN用于准确分类HAR数据。此外，在DEFNN中采用AWDO来调整超参数，从而减轻计算开销并提高检测效率。使用从物联网测试平台收集的实时数据集进行了广泛实验，以验证所提出的方法，该测试平台采用与Wi-Fi收发器接口的NodeMCU单元。使用几个指标评估了该框架的效率：准确率为99.5%，精确率为98%，召回率为97%，特异性为98%，F1分数为98.2%。然后将这些结果与其他当代基于深度学习（DL）的HAR系统进行了基准比较。实验结果表明，我们的模型实现了近乎完美的准确率，超过了其他基于学习的HAR系统。此外，与之前的算法相比，我们的模型展示出更低的计算需求，这表明所提出的框架可能为部署在为老年人或残疾人设计的HAR系统中提供更高的功效和兼容性。
