Human behavior recognition method based on fusion of respiratory signal and motion signal
Human behavior recognition technology has been widely used in many fields,but there are still some problems such as single sensor type,limited applicable scenes and insufficient fea-tures when capturing human activities.This paper proposes a wearable,multi module sensor human behavior recognition scheme,using inertial sensor and tensile strain sensor.The tensile strain sen-sor is worn on the chest,which can effectively capture the human respiratory signals under different behaviors.At the same time,this paper uses a multi-scale one-dimensional convolutional neural network,which can effectively extract the characteristics of sensor data.The experimental results show that the recognition rate of multi-scale one-dimensional convolutional neural network reaches 96.89%in the seven activities proposed in this paper,which shows that the method proposed in this paper can be well used in the task of human behavior recognition.
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