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Fusion motion and physiological sensing data layered complex activity identification method

A sensing data and motion sensing technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as the inability to effectively integrate complex activity recognition, slow changes in physiological sensing data, etc., and achieve high practicability. , Improve the recognition performance, good universality

Active Publication Date: 2017-06-23
ZHEJIANG UNIV
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  • Claims
  • Application Information

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Problems solved by technology

However, due to the different characteristics of motion sensing data and physiological sensing data, motion sensing data mainly reflect transient changes, while physiological sensing data changes relatively slowly, and there are many kinds of complex activities, feature-level fusion cannot effectively integrate two different characteristics. class data for complex activity recognition

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  • Fusion motion and physiological sensing data layered complex activity identification method
  • Fusion motion and physiological sensing data layered complex activity identification method
  • Fusion motion and physiological sensing data layered complex activity identification method

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Embodiment Construction

[0039] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0040] Such as figure 1 As shown, the hierarchical complex activity recognition method of the present invention, which combines motion and physiological sensor data, specifically includes a data processing stage and a model training stage.

[0041] In the data processing stage, the collected data needs to be processed. The specific process is as follows:

[0042] In step a-1, smart devices and wearable devices are used to collect user data during complex activities, where the data includes motion sensor data and physiological sensor data.

[0043] In this step, the specific method of data collection is as follows:

[0044]First, smart devices are used to record various motion sensor data during complex activities, and wearable devices are used to record...

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Abstract

The invention discloses a fusion motion and physiological sensing data layered complex activity identification method. The method comprises steps of 1) using intelligent equipment and wearable equipment to collect motion and physiological sensing data; 2) extracting statistic features of motion sensing data, and extracting structural features and transient features of the physiological sensing data; 3) performing K-Means clustering and LDA theme extraction to the statistic features of the motion sensing data so as to obtain the theme distribution of the motion sensing data; 4) based on the theme distribution of the motion sensing data and the features of the physiological sensing data, establishing corresponding classifiers respectively; and 5) using a fractional order fusion method to fuse output of the classifiers to obtain a complex motion classification model. The method uses the clustering and the theme model to express the layered structure of the complex activity, fuses motion and physiological sensing data to realize the accurate complex activity identification, and has wide application prospects in the fields of intelligent home, medical care and the elderly assistance and the like.

Description

technical field [0001] The invention relates to the fields of pattern recognition and pervasive computing, in particular to a hierarchical complex activity recognition method for fusing motion and physiological sensor data. Background technique [0002] With the development of smart devices (such as smartphones, smart watches, etc.) The acquisition of data has become increasingly convenient, and how to use these data for activity recognition has become the focus of the industry. The corresponding technology has broad application prospects in the fields of smart home, medical care, and assistance for the elderly. [0003] Simple activities usually consist of periodic movements or single postures of the human body, such as standing, sitting, walking, running, etc. Compared with simple activities, complex activities are usually less regular, last longer, and have high-level semantics, such as eating, working, shopping, etc. Traditional activity recognition methods based on sm...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/424G06F18/23213G06F18/24323
Inventor 陈岭彭梁英
Owner ZHEJIANG UNIV