Impaired behavior recognition based on multi-head-Siamese neural network

Lun Ma; Xin Liu; Bin Zhao; Ruiping Wang; Yajing Zhang

doi:10.1117/12.2636827

29 April 2022 Impaired behavior recognition based on multi-head-Siamese neural network

Lun Ma, Xin Liu, Bin Zhao, Ruiping Wang, Yajing Zhang

Proceedings Volume 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022); 122471X (2022) https://doi.org/10.1117/12.2636827
Event: 2022 International Conference on Image, Signal Processing, and Pattern Recognition, 2022, Guilin, China

Abstract

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.

Citation Download Citation

Lun Ma, Xin Liu, Bin Zhao, Ruiping Wang, and Yajing Zhang "Impaired behavior recognition based on multi-head-Siamese neural network", Proc. SPIE 12247, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2022), 122471X (29 April 2022); https://doi.org/10.1117/12.2636827

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KEYWORDS

Sensors

Neural networks

Network architectures

Head

Optimization (mathematics)

Convolution

Feature extraction

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