Multi-mode complex activity recognition method based on deep learning model

Abstract

The invention discloses a multi-mode complex activity recognition method based on a deep learning model. To be specific, the method comprises: step one, classifying different-mode time sequence data into different types and carrying out expression extraction by using convolutional neural networks (CNN) with different structures; step two, carrying out fusion on expressions in different modes by using a longitudinal splicing layer and the convolutional layers; and step three, extracting sequence features further by using an LSTM network to obtain a complex activity tag. According to the invention, complex activities are identified by using a deep learning model. The multi-mode complex activity recognition method has the broad application prospects in fields of health care, industrial assistance, skill evaluation and the like.

Description

technical field

[0001] The invention belongs to the field of activity recognition, and in particular relates to a multimodal complex activity recognition method based on a deep learning model. Background technique

[0002] Activity recognition is a basic and important research direction in the field of ubiquitous computing. With the development and popularization of wearable devices, activity recognition has been widely used in elderly assistance, newborn monitoring, and skill assessment.

[0003] According to whether the activity label contains advanced semantics, activity recognition can be divided into simple activity recognition and complex activity recognition. Simple activities usually consist of periodic movements or single postures of the human body, such as standing, sitting, walking, running, etc. Complex activities are usually composed of simpler activities that last longer and have high-level semantics, such as eating, working, shopping, etc. The current metho...

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