Revolutionizing Human Activity Recognition: Energy-Efficient AI with Spiking Neural Networks

This research presents an exciting advancement in human activity recognition (HAR) by leveraging Spiking Neural Networks (SNNs). The proposed spiking convolutional autoencoder (SCAE) shows impressive performance while dramatically reducing energy consumption. This approach promises to unlock efficient HAR on edge devices.

• •Utilizes Spiking Neural Networks (SNNs) for energy-efficient human activity recognition.
• •Introduces a Spiking Convolutional Autoencoder (SCAE) for efficient spike-encoded representations.
• •Achieves high accuracy with significantly reduced energy consumption compared to other methods.