基于足压力的多尺度交叉注意融合异常步态识别。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Menghao Yuan;Yan Wang;Xiaohu Zhou;Meijiang Gui;Aihui Wang;Chen Wang;Guotao Li;Hongnian Yu;Lin Meng;Zengguang Hou
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引用次数: 0

摘要

异常步态识别在医疗保健中起着至关重要的作用,特别是对于神经和肌肉骨骼疾病的早期诊断和持续监测,如帕金森病和骨科损伤。本研究提出了mscaf -步态,这是一种多尺度交叉注意融合网络,专门用于使用足压传感器识别异常步态。mscaf -步态集成了具有通道和空间注意机制的多尺度卷积模块,可以有效地捕获跨越时间、通道和空间维度的特征。一种新的交叉注意融合模块进一步增强了特征表示,能够精确识别各种异常步态模式。为了促进这项研究,我们引入了压力-鞋垫异常步态(PIAG)数据集,包括与常见神经和肌肉骨骼异常相关的步态数据。在公开可用的帕金森病步态(GaitinPD)数据集和我们自己构建的PIAG数据集上进行了大量实验,验证了mscaf -步态的有效性。具体来说,该模型在帕金森步态识别中准确率达到99.61%,在帕金森严重程度分类中准确率达到98.88%。在包含多种异常步态模式的PIAG数据集上,mscaf -步态的准确率高达99.42%。值得注意的是,这些结果是通过减少FLOPs和参数计数的轻量级架构获得的,表明mscaf -步态具有高精度和计算效率,非常适合在可穿戴平台上进行实时部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Foot Pressure-Based Abnormal Gait Recognition With Multi-Scale Cross-Attention Fusion
Abnormal gait recognition plays a critical role in healthcare, particularly for the early diagnosis and continuous monitoring of neurological and musculoskeletal disorders, such as Parkinson’s disease and orthopedic injuries. This study proposes MSCAF-Gait, a Multi-Scale Cross-Attention Fusion Network designed specifically for abnormal gait recognition using foot pressure sensors. MSCAF-Gait incorporates multi-scale convolutional modules with channel and spatial attention mechanisms to effectively capture features across temporal, channel, and spatial dimensions. A novel cross-attention fusion module further enhances feature representation, enabling precise recognition of diverse abnormal gait patterns. To facilitate this research, we introduce the Pressure-Insole Abnormal Gait (PIAG) dataset, comprising gait data associated with common neurological and musculoskeletal abnormalities. Extensive experiments on the publicly available Gait in Parkinson’s Disease (GaitinPD) dataset and our self-constructed PIAG dataset validate the effectiveness of MSCAF-Gait. Specifically, the model achieves 99.61% accuracy in Parkinsonian gait recognition and 98.88% accuracy in Parkinson’s severity classification. On the PIAG dataset, which includes multiple abnormal gait patterns, MSCAF-Gait attains a high accuracy of 99.42%. Notably, these results are obtained with a lightweight architecture characterized by reduced FLOPs and parameter count, demonstrating that MSCAF-Gait offers both high accuracy and computational efficiency, making it well-suited for real-time deployment on wearable platforms.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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