基于肌压信号与惯性测量单元的下肢运动意图识别

Recognition of Lower Limb Movement Intention Based on Force Myography Signal and Inertial Measurement Unit

  • 摘要: 对截肢者下肢运动意图的准确识别是提高下肢假肢人机交互性能,降低假肢使用者运动能耗的关键。本文通过自行设计的信号采集系统对股骨截肢者和健全受试者进行了7种不同步态模式下的下肢运动学信号采集,包括大腿残端肌压信号(FMG)和6自由度惯性测量单元(IMU)的大小腿角度、加速度等信号,同时,采用了特征融合的方法将两种信号的时域特征进行融合,并利用机器学习的方法研究了不同信息源融合下的下肢步态模式分类准确率情况。研究结果表明,在3种分类算法下,截肢受试者和健全受试者FMG-IMU信号的平均分类准确率比单一FMG信号与单一运动学信号训练模型的平均分类准确率分别最高提高了4.7%与9.5%,最高可达99.6%的平均分类准确率。这一结果表明基于FMG信号与IMU信号融合的下肢运动意图识别方案能取得良好的效果,有望进一步拓宽FMG信号在下肢运动意图识别领域的研究,同时为下肢假肢的进一步改进和优化提供理论支持和实践指导。

     

    Abstract: Accurate recognition of the lower limb movement intentions of amputees is the key to improving the human-machine interaction performance of lower limb prosthetics and reducing the energy consumption of prosthetic users. We use a self-designed signal acquisition system to collect lower limb kinematic signals from femoral amputees and healthy subjects in seven different gait modes, including thigh residual force myography signal (FMG) signals and signals such as leg angle and acceleration from the six degrees of freedom inertial measurement unit (IMU). In addition, the time-domain features of the two signals are fused using a feature fusion method. Machine learning methods are used to investigate the accuracy of lower limb gait pattern classification under the fusion of different information sources. The research results show that by using the three classification algorithms, the average classification accuracy of FMG-IMU signals for amputees and healthy subjects increase by 4.7% and 9.5%, respectively, compared with the average classification accuracy of single FMG signal and single kinematic signal training models, with a maximum average classification accuracy of 99.6%. These results indicate that the lower limb motion intention recognition scheme based on the fusion of FMG and IMU signals can achieve good results and is expected to further expand research on FMG signals in the field of lower limb motion intention recognition. Furthermore, these findings provide theoretical support and practical guidance for the further improvement and optimization of lower limb prostheses.

     

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