The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%.

中文翻译：

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基于力学与姿态角信号融合的步态模式识别研究

下肢运动的研究在许多领域都发挥着重要作用，例如残疾患者的康复和治疗、日常生活的检测和监测，以及人与机器的交互，如智能假肢的应用。本文利用无线设备采集四块大腿肌肉（股直肌、股外侧肌、股内侧肌和半腱肌）的肌力图（MMG）信号和股直肌的姿态角。11个步态动作达到了高精度，其中静态活动3个，动态过渡活动4个，动态活动4个。经验证，隐马尔可夫模型（HMM）不仅可以应用于基于MMG的步态识别，而且具有较高的准确性，还可以支持支持向量机（SVM）和二次判别分析（QDA）的比较分析。此外，从特征选择、通道组合和肌肉贡献率的角度进行了实验。结果表明，基于MMG的动态动作平均分类准确率为98.27%，而基于姿态角的静态动作和动态过渡动作的平均识别率分别可以达到98.33%和100%。一般11个步态动作的平均识别率为98.91%。通道组合和肌肉贡献率。结果表明，基于MMG的动态动作平均分类准确率为98.27%，而基于姿态角的静态动作和动态过渡动作的平均识别率分别可以达到98.33%和100%。一般11个步态动作的平均识别率为98.91%。通道组合和肌肉贡献率。结果表明，基于MMG的动态动作平均分类准确率为98.27%，而基于姿态角的静态动作和动态过渡动作的平均识别率分别可以达到98.33%和100%。一般11个步态动作的平均识别率为98.91%。