Deep Learning-driven Front-Following within Close Proximity: a Hands-Free Control Model on a Smart Walker

Abstract

With the ever-increasing elderly population, elder walking assistance is in strong demand. Instead of receiving assistance from a human carer, a smart walker can bring an elder user a more convenient and autonomous walking experience. Towards intelligent and safe walking assistance, we propose a close-proximity front-following model for smart walkers, which analyzes the walking gait and detects the walking intention of the user, and intelligently follows the user in the front to provide walking support, without the user pushing the walker. We design a deep learning model named Front-Following Net (FFLNet), consisting of CNN and LSTM networks to extract spatial and temporal features of the elder walking gait, collected in time windows through a thermal camera and a 2D LiDAR, for effective walking intention detection. As compared to other walking intention detection approaches, our model can explore more effective information in the gait data within a short walking period, and achieve accurate hands-free tracking of the user. Experiments show that our FFLNet can achieve over 77% detection accuracy among six representative walking intentions and more than 90% accuracy for turning intentions. Combined with a carefully designed walker control policy, our smart walker can achieve high front-following correctness with the user.