Estimating lower-limb joint torques during gait using inertial measurement units (IMUs) has attracted growing attention in biomechanics and wearable sensing. Conventional approaches rely on inverse dynamics based on segmental kinematics and ground reaction forces, requiring force sensors or full-body sensor setups. This study proposes a recurrent neural network (RNN) method to estimate lower-limb joint torques using segmental kinematic data from a limited number of IMUs.Twelve healthy participants performed treadmill walking and running under twelve different conditions to generate training data. Model inputs included center-of-mass accelerations and angular velocities of the pelvis and shank.Results demonstrated two key findings. First, a model using three IMUs achieved performance comparable to a seven-IMU model, with hip flexion torque errors of approximately 0.18 Nm/kg, demonstrating strong effectiveness with a reduced sensor configuration. Second, while inverse dynamics exhibited an error increase of 0.28 Nm/kg from the ankle to the hip, the proposed model showed only a 0.01 Nm/kg increase and achieved approximately 0.13 Nm/kg lower error at the hip.These results indicate that accurate and efficient joint torque estimation is feasible using an RNN with fewer wearable sensors.
One of the problems in inverse dynamics calculation for the inertial measurement unit (IMU)-based joint force and torque estimation is the amplified signal noises of segment kinematic data mainly due to the differentiation procedure and segmental soft tissue artifacts. In order to deal with this problem, appropriate filtering methods are often recommended for signal enhancement. Conventionally, a low-pass filter (LPF) is widely used for the kinematic data. However, the zero-phase LPF requires post-processing, while the real-time LPF causes an unignorable time lag. For this reason, it is inappropriate to use the LPF for real-time joint torque estimation. This paper proposes a Kalman filter (KF) for inverse dynamics of IMUbased joint torque estimation in real time without any time lag, while utilizing the smoothing capability of the KF. Experimental results showed that the proposed KF outperformed a real-time LPF in the estimation accuracy of hip joint force and torque during jogging on the spot by 100 and 29%, respectively. Although the proposed KF requires the process of adjusting covariance according to the dynamic conditions, it can be expected to improve the estimation performance in the field where joint force and torque need to be estimated in real time.
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Wearable Inertial Sensors-based Joint Kinetics Estimation of Lower Extremity Using a Recurrent Neural Network Ji Seok Choi, Chang June Lee, Jung Keun Lee Journal of the Korean Society for Precision Engineering.2023; 40(8): 655. CrossRef
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