Fall detection based on improved capsule network

Yanshi Liu; Minghui Yao

doi:10.1117/12.3038203

7 August 2024 Fall detection based on improved capsule network

Yanshi Liu, Minghui Yao

Proceedings Volume 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024); 1322912 (2024) https://doi.org/10.1117/12.3038203
Event: Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 2024, Nanchang, China

Abstract

Aiming at the limitations of traditional machine learning in data feature extraction, an integrated Capsule Network and multi-layer Bidirectional Gated Recurrent Unit (Bidirectional Gated Recurrent Unit) were proposed. BiGRU and the BACN fall detection and recognition model of Attention mechanism. In this model, the capsule network is responsible for capturing the spatial features, while the bidirectional GRU module effectively extracts the hidden temporal features of the data. At the same time, the attention mechanism is used to further highlight and screen the fine-grained features to improve the accuracy of model recognition. Through repeated experimental training and model optimization, we determined the optimal hyperparameters of the network and constructed an efficient fall detection model. The accuracy of the model on Sisfall, Mobiact fall data set and self-collected data set reached 98.3%, 97.8% and 94.4% respectively, which fully proves its effectiveness in practical application.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanshi Liu and Minghui Yao "Fall detection based on improved capsule network", Proc. SPIE 13229, Seventh International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2024), 1322912 (7 August 2024); https://doi.org/10.1117/12.3038203

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