Abstract:

Impaired behavior recognition is an important branch of human activity recognition,which refers to harmful behavior of people with special needs.Aiming at the problem that the correlation between sensors is not taken into account when recognizing the impaired behavior by using multi-sensor devices equipped on different parts of the human body,based on deep learning theory,this paper proposes a multi-head-siamese neural network to characterize the relation between sensors,which builds multiple sub-networks for consistent feature extraction.The extracted features are fused and recognized by the classifier on the basis of the weight sharing idea.In the presented network,the upsampling operation is first employed to fill the missing collected data,and the data is then standardized to improve the recognition accuracy.Besides,the network hyperparameters are adjusted by the Bayesian optimization.In addition,due to the over-fitting problem when recognizing impaired behavior by introducing the Adam optimizer,L2 regularization is performed by using the AdamW optimizer,thus further improving the recognition accuracy.Processing results of raw data show that the network achieves a classification accuracy of 96.0%.Compared with the baseline network and single input network,the accuracy of the proposed network increases by 6.1% and 8.8%,respectively,and it could reduce the possibility of incorrect prediction.Compared with the multiple input network,its accuracy increases by 2.4%,and it reduces the number of training parameters by 92%.It is proved that this network is effective for impaired behavior recognition in terms of utilizing the relationship between sensors.

Key words: behavior recognition, deep learning, neural network, weight sharing, feature extraction