Spike-IMU: An Accurate and Low-Power Spiking Neural Network for Pedestrian Velocity Estimation

Abstract

Accurate pedestrian navigation on edge devices is a critical problem. While artificial neural networks (ANNs) have been shown to effectively solve this problem with acceptable accuracy, their energy consumption limits applications on low-power computation platforms. Spiking neural networks (SNNs) are promising alternatives, while their applicability in using noisy, high-frequency IMU data is hindered by two key issues: information loss during spike encoding and simplistic neuron dynamics that fail to capture complex motion. This paper introduces Spike-IMU, an SNN-based velocity estimation network designed to overcome these issues for the pedestrian navigation problem. In particular, a dynamic spiking neuron (DSN) is introduced based on the integer firing mechanism. In addition, a temporal feature fusion spike encoder (TFFSE) and a dynamic spiking long short-term memory network (DSLSTM) are proposed to encode and process IMU data into spike sequences. Our experiments on the RoNIN dataset show that Spike-IMU surpasses classical ANNs, reducing positioning error by 20% while consuming 70.3% less energy. This work demonstrates a novel pipeline to design SNNs that achieves both superior accuracy and energy efficiency, pushing applications of IMU-based pedestrian navigation to real-world low-power edge devices.