TY - JOUR
T1 - Estimating 3D Ground Reaction Forces During Daily Activities
T2 - A Reduced Sensor Setup and Virtual Pivot Point Approach
AU - Castellaz, Alessandro
AU - Wouda, Frank J.
AU - van Beijnum, Bert-Jan F.
N1 - Financial transaction number:
2500211953
PY - 2025/10/17
Y1 - 2025/10/17
N2 - Ground reaction forces (GRF) during daily activities are critical for assessing joint loading, particularly in individuals with osteoarthritis (OA). Traditional GRF measurements rely on force plates, which restrict their use to laboratory environments. This study presents a novel method for estimating 3D GRF using a minimal sensor setup comprising three inertial measurement units (IMUs) and pressure insoles (PI), and exploiting the biomechanical concept of Virtual Pivot Point (VPP) to distribute the total GRF between the feet. Data were collected during various activities of daily living (ADL), including walking tasks, stair ascent/descent, and sit-to-stand movements. The proposed system demonstrates high accuracy, achieving relative root mean squared errors (rRMSE) below 15% and correlation coefficients exceeding 0.7 for all tasks, except sit-to-stand movements during Timed Up and Go test (TUG). This approach significantly reduces the sensor burden while maintaining performance comparable to more extensive setups. By combining the estimated 3D GRF with kinematics, joint loading can be estimated, enabling a portable setup for monitoring healthy subjects during ADL in real-world settings. The open-source MATLAB code and dataset are available in the 4TU Research Data repository.
AB - Ground reaction forces (GRF) during daily activities are critical for assessing joint loading, particularly in individuals with osteoarthritis (OA). Traditional GRF measurements rely on force plates, which restrict their use to laboratory environments. This study presents a novel method for estimating 3D GRF using a minimal sensor setup comprising three inertial measurement units (IMUs) and pressure insoles (PI), and exploiting the biomechanical concept of Virtual Pivot Point (VPP) to distribute the total GRF between the feet. Data were collected during various activities of daily living (ADL), including walking tasks, stair ascent/descent, and sit-to-stand movements. The proposed system demonstrates high accuracy, achieving relative root mean squared errors (rRMSE) below 15% and correlation coefficients exceeding 0.7 for all tasks, except sit-to-stand movements during Timed Up and Go test (TUG). This approach significantly reduces the sensor burden while maintaining performance comparable to more extensive setups. By combining the estimated 3D GRF with kinematics, joint loading can be estimated, enabling a portable setup for monitoring healthy subjects during ADL in real-world settings. The open-source MATLAB code and dataset are available in the 4TU Research Data repository.
UR - https://www.scopus.com/pages/publications/105019604285
U2 - 10.1109/TNSRE.2025.3622734
DO - 10.1109/TNSRE.2025.3622734
M3 - Article
SN - 1534-4320
VL - 33
SP - 4277
EP - 4287
JO - IEEE transactions on neural systems and rehabilitation engineering
JF - IEEE transactions on neural systems and rehabilitation engineering
ER -