Impaired behavior recognition based on multi-head-Siamese neural network
Lun Ma, Xin Liu, Bin Zhao, Ruiping WANG, Yajing ZHANG · 2022
Sensor-based human behavior recognition is a classification recognition task and is widely used in medical care, environmentally assisted living, and other fields. But multiple sensors sense the impaired behavior without considering the correlation between sensors. In this paper, a multi-head-siamese neural network, combined with weight sharing is proposed based on deep learning theory. The network hyperparameters are adjusted by Bayesian optimization. Due to the problem of over-fitting during impaired behavior recognition introduced by Adam optimizer, L2 regularization is improved by using AdamW optimizer. 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, its accuracy has increased by 6.1% and 8.8% respectively. Compared with multiple input network, its accuracy has increased by 2.4%, and reduced the number of training parameters by 92%. Verified the effectiveness of the proposed network for impaired behavior recognition.