混合CNN-HMM的人体动作识别方法

针对当前人体动作识别算法检测精度不佳和实验场景多样性的问题，提出了一种混合卷积神经网络?隐马尔可夫模型(CNN-HMM)的人体动作识别方法。建立了抬腿、深蹲和仰卧臀桥3组分别包含1个标准动作姿态和5个非标准动作姿态的人体康复训练动作模型库，结合可穿戴式惯性动作捕捉系统PN2.0获取实验数据。最后从准确率、灵敏度和特异性3个方面进行性能评估。实验结果表明，该方法能够以较高识别率将6种不同动作姿态区分开，其平均识别准确率为97.00%，相较于单一CNN方法提高了5.78%。

Human Motion Recognition Method Using Hybrid CNN-HMM

Aiming at the problems of poor detection accuracy of current human motion recognition algorithms and the diversity of experimental scenes, a new human motion recognition method based on hybrid convolutional neural network-hidden Markov model (CNN-HMM) is proposed. In order to verify the effectiveness of the method, we establish three sets of human rehabilitation training motion models including one standard motion posture and five non-standard motion postures for leg-lifting, squat and hip bridge, respectively. The experimental data are obtained by the wearable inertial motion capture system, Perception Neuron 2.0 (PN2.0). Finally, the performance of the proposed method is evaluated in terms of accuracy, sensitivity and specificity. Three groups of the experimental results show that the proposed method can distinguish the six different motion gestures with a high average recognition rate of 97.00%, which is 5.78% higher than the single CNN method.