User behavior monitoring method and wearable equipment

Abstract

The invention discloses a user behavior monitoring method and wearable equipment. The method comprises that the wearable equipment is internally provided with an inertia sensor; in the beginning of each data index obtaining stage, the inertia sensor is used for monitoring, and historical motion data of the user in a preset statistical period is collected; a prediction index is obtained according to the change trend of the historical motion data; during real-time monitoring, real-time motion data of the user is collected, and whether the user has an abnormal behavior is determined according to the real-time motion data, the obtained prediction index and a preset strategy; and an alarming notification is sent when it is determined that the user has the abnormal behavior. Aimed at different users, the personal historical motion data of each user serves as self-learning template data, the prediction index for motion of the user later is obtained by learning the template data continuously, the theoretical prediction index is combined with the real-time motion data collection practically at present to analyze and discover the abnormal behavior of the user, and customized high-precision behavior monitoring is realized.

Description

technical field [0001] The invention relates to the field of wearable smart devices, in particular to a user behavior monitoring method and wearable devices. Background technique [0002] With the rapid development of mobile Internet technology, wearable devices are changing with each passing day, setting off new upsurges in the Internet field, and their attention and demand are constantly increasing. Among them, in order to conform to the trend that modern people pay more and more attention to healthy sports, various wearable devices have launched user behavior monitoring functions one after another. [0003] However, the existing methods for monitoring user behavior with wearable devices are generally to extract features from big data information of a similar group of people's normal behavior (such as walking, running), and train a unified classification model. The behavior of the classification model will be judged as abnormal behavior, such as falling, falling, etc. In...

AI Technical Summary

Problems solved by technology

In this existing technical solution, the individual differences of each person are not considered, and the instantaneous characteristic information of individual normal behaviors (such as running, descending stairs) is also similar to the characteristic information of abnormal behaviors, which is easy to cause false positives

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