一种新的基于CNN、双向长短期记忆和门控循环单元的人类活动识别混合方法

Narina Thakur, Sunil K. Singh, Akash Gupta, Kunal Jain, Rachna Jain, D. Peraković, N. Nedjah, M. Rafsanjani
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引用次数: 0

摘要

人类活动识别(HAR)是一项至关重要且具有挑战性的分类任务,适用于从监视到援助的一系列应用。现有的基于传感器的HAR系统训练数据的可用性有限,并且缺乏快速准确的方法来进行鲁棒和快速的活动识别。本文提出了一种基于CNN、双向长短期记忆和门控循环单元的混合HAR技术,该技术可以在有限的训练集和较高的准确率下准确快速地识别新的人类活动。实验在UCI机器学习存储库的MHEALTH数据集上进行,以分析所提出方法的有效性。利用混淆矩阵和准确率分数来衡量模型的性能。实验表明,将CNN、双向LSTM和门控递归相结合的人类活动识别混合方法具有较好的计算复杂度和效率。总体结果表明,所提出的混合模型具有优异的性能,准确率提高了94.68%。
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A Novel CNN, Bidirectional Long-Short Term Memory, and Gated Recurrent Unit-Based Hybrid Approach for Human Activity Recognition
Human activity recognition (HAR) is a crucial and challenging classification task for a range of applications from surveillance to assistance. Existing sensor-based HAR systems have limited training data availability and lack fast and accurate methods for robust and rapid activity recognition. In this paper, a novel hybrid HAR technique based on CNN, bi-directional long short-term memory, and gated recurrent units is proposed that can accurately and quickly recognize new human activities with a limited training set and high accuracy. The experiment was conducted on UCI Machine Learning Repository's MHEALTH dataset to analyze the effectiveness of the proposed method. The confusion matrix and accuracy score are utilized to gauge the performance of the presented model. Experiments indicate that the proposed hybrid approach for human activity recognition integrating CNN, bi-directional LSTM, and gated recurrent outperforms computing complexity and efficiency. The overall findings demonstrate that the proposed hybrid model performs exceptionally well, with enhanced accuracy of 94.68%.
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