Estimating Whole-Body Walking Motion from Inertial Measurement Units at Wrist and Heels Using Deep Learning

Abstract

A recurrent-neural-network-based deep-learning model was developed to estimate the three-axis joint angles of an entire body with 17 bones during walking from three inertial measurement units (IMUs) — one each on the left wrist and heels. In this model, the acceleration and angular velocity of the previous 49 frames and current frame were considered as inputs. The architecture comprises two hidden layers (two long short-term memory layers) and a dense layer. The performance of the model was evaluated using the National Institute of Advanced Industrial Science and Technology (AIST) Gait Database 2019 public dataset. Consequently, the root mean squared error of each joint angle was less than 12.28°. A comparison of the estimation results of the same model with IMUs at the pelvis and shanks revealed that the proposed model is advantageous in terms of balanced measurement accuracy and ease of use in realizing whole-body motion capture. Although the accuracy of the model was better than those of previous models in estimating the general whole-body motion from six IMUs, it was worse than that of a previous model in estimating only the lower-limb motion from three IMUs attached to the pelvis and shanks during walking. In the proposed model, IMUs are attached to the left wrist and heels, and whole-body motion can be easily captured using a smartwatch and smart shoes.