Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN

Abstract

Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies have focused on improving gait detection accuracy while neglecting model complexity in the deep learning domain, making them unsuitable for low-power wearable devices. Therefore, inference from these models results in latency due to calculation overhead. This study proposes an efficient network suitable for wearable devices without sacrificing prediction performance. We have modified the residual block and accumulated it in shallow convolutional neural networks with five weighted layers only for gait recognition and proved the efficacy of all the architectural components with extensive experiments over publicly available IMU-based datasets: whuGait and OU-ISIR. Our proposed model outperforms all the state-of-the-art methods regarding recognition accuracy and is more than 85 percent efficient on average in terms of model parameters and memory consumption.