For the problem that falls often seriously jeopardize to the health of the elderly

this paper designs a fall detection method based on EMG signals.Firstly

the feature of fuzzy entropy is extracted from the sEMG on the gastrocnemius and vastus lateralis muscle.Then

the weighted kernel Fisher linear discriminant analysis is proposed for the dataset imbalance problem that the number of activities of daily life (ADL) is far more than the fall

and the samples nuclear matrix is adjusted by the appropriate balance parameters.Finally

the fall is identified from walking

squat and sit down by this method.The experimental results show that the method has 96.7% fall and 99.4% ADL average recognition rate

and is better than the other classification methods.