Estimating 3D Ground Reaction Forces During Gait Using a Deep Learning Model with IMU and Plantar Pressure Data

Abstract

Approximately 40% of the long-term care requirements of the elderly are related to their declining walking ability, making early detection crucial. However, conventional ground reaction force (GRF) measurements are limited to laboratory environments, making daily measurements difficult. In this paper, we propose a method for estimating three-dimensional GRFs using deep learning with plantar pressure sensor and inertial measurement unit (IMU) data obtained from shoe-type devices. Gait data (13,542 strides) were collected from 12 healthy males (23.5 ± 1.2 years) under three speed conditions, and three models (1D-convolutional neural network, bidirectional long short-term memory, transformer) were compared. The transformer model achieved the highest estimation accuracy (average normalized root mean square error: 5.91%; average mean absolute error: 2.92%; body weight: average R²: 0.842). Furthermore, the introduction of a weighted loss function improved the overall accuracy, and we confirmed that the IMU data improved the estimation accuracy of the horizontal components (Fx, Fy). This method enables the continuous monitoring of walking ability in daily living environments.

Keywords: 3D Ground Reaction Forces, Deep Learning, IMU, Plantar Pressure

DOI: 10.54941/ahfe1007334