Energy-efficient fall detection based on spiking neural networks

Xiao Xiao; Liwei Meng; Yian Liu; Shaogang Hu; Guanchao Qiao

doi:10.1117/12.2681636

1 June 2023 Energy-efficient fall detection based on spiking neural networks

Xiao Xiao, Liwei Meng, Yian Liu, Shaogang Hu, Guanchao Qiao

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181N (2023) https://doi.org/10.1117/12.2681636
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Falls have become an essential factor affecting life safety in recent years. Given the high energy consumption associated with running models over extended periods in practical engineering, this research presents a network architecture, Spiking-Rep-YOLOv3, which is more suitable for transformation into Spiking neural networks (SNNs). We redesign the RepVGG-based backbone network by improving the YOLOv3-tiny network. Second, we speed up inference by employing neuron normalization to reduce information loss during inference after neural network transformation. We experimented with Spiking-Rep-YOLOv3 on the E-FPDS public dataset and showed that the network achieved similar accuracy as YOLOv3-tiny and only 40.7% of the energy consumption of the latter.

Citation Download Citation

Xiao Xiao, Liwei Meng, Yian Liu, Shaogang Hu, and Guanchao Qiao "Energy-efficient fall detection based on spiking neural networks", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181N (1 June 2023); https://doi.org/10.1117/12.2681636

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