Human motion state recognition based on acceleration sensor

Abstract

The invention provides a human body motion state recognition method and system based on an acceleration sensor, which is divided into an offline stage and an online stage; wherein, the offline stage adopts the K-Means clustering method to construct a human body motion state recognition model, based on the existing tagged The data is used for training and research, and a classification strategy is proposed; then in the online stage, a real-time system for human motion state recognition is designed based on Android mobile phones, and the design is carried out from five functions: data collection, data processing, motion recognition, model update, and data display; finally, it is proved by experiments The effectiveness of the clustering algorithm, the experimental results show that it is feasible to build a human motion recognition model based on the clustering method, and the model has the advantages of good real-time performance, light weight and easy adjustment.

Description

Technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a design and classification method of a clustering algorithm in data mining. Background technique [0002] With the rise of mobile Internet technology and the development of wireless sensor technology, sensor data is always being generated. These data contain rich information and have far-reaching research significance. The widespread use of pedometers is one of the research results. Motion recognition is currently one of the more popular directions. By analyzing acceleration sensor data, a fixed model of human motion is found. So far, the research has mainly used traditional classification techniques, and has rarely involved clustering methods. Since this method takes a sampling process, all data sets must be known in advance, so it is only suitable for static time series data. And because time series are fluid and unpredictable, data is generated over time, and th...

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Problems solved by technology

[0005] (1) Motion state recognition based on sound: This method has an obvious defect: the applicable scenarios are very limited and the cost of using this method is relatively high

In the field of smart home research, some people have proposed a sensor network system. By installing various sensors at home, the data is uniformly input to the control platform, and then the specific movements of people at home are analyzed and identified; others fix an acceleration sensor on the waist of the human body. It is very good at identifying 9 kinds of sports such as walking, running, and standing; in some studies, in order to ensure the efficiency of data collection and the reliability of transmission, the acceleration sensor and the storage device are connected through the USB data cable and worn on the body In the end, although it can reach 94% accuracy, it is very inconvenient to wear and cannot be accepted by general users; because people's daily movements are diverse and complex, in order to identify more movement patterns very accurately, some researches Through experiments, personnel placed acceleration sensors on five human body parts including the waist, hips, wrists, thighs, and ankles. The final experimental results can accurately identify more than 20 types of sports. However, due to too many sensors worn, the user experience is extremely poor. Poor, it is difficult to be promoted, but it also shows that multiple sensor data can improve the recognition accuracy

[0010] Since most of the current research methods only consider prior knowledge and ignore the characteristics of dynamic changes in streaming data, resulting in problems such as high accuracy of the constructed classification model under static data and poor actual experience

In addition, current research methods do not take user differences into account, and the trained classification model is heavily personalized.

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