Behavior Recognition Method Based On Acceleration Sensor

The field of behavioral recognition has been studied for many years.with the continuous innovation of science and technology,the scope of research has also expanded greatly.Using sensor based behavioral identification as basic service,problems,such as "Empty Nester"and children who lack of guardianship,can be solved through identification of individual behavior and statistics of behavior status by monitoring,giving reminders and alarming.The outcome of behavioral identification is highly depending on the data source.User' s behavioral data in different status can be monitored through various types of sensors equipped on a smart phone.Extracting the relevant features from the data collected to form a feature set,Up on which the classifier can be trained by making different decisions so as to realize behavioral identification.The accuracy of behavioral recognition is different by the classifier which is trained through different feature sets which are formed by using a variety of combinations.This paper presents a behavioral recognition feature selection algorithm based on genetic algorithms,which abstracts the data features collected by the smart phone and performing PrePtreatments and extraction up on them,finally picking the feature set which results in highest has brought up a solution which applies a multiple behavioral classification solely against acceleration data to justify the behavior of current status,if the status of climbing stairs isrecognized then the secondary classification would be executed against the real part of the acceleration vector so as to identify whether it is up or down.Therefore,it is obvious that multiple classification method is more effective compare to the solution only does single classification in respect of the rate of successful identification.Through the final verification of designed experiments,the proposed method is able to pick feature combinations with low dimensionality and high accuracy.And the accuracy of behavior identification can also be greatly improved through multiple classification method.