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.

This paper proposes an IMU method for location tracking in power plants and indoor environments without GPS. IMU-based sensors use accelerometer, angular accelerometer, earth magnetometer, and altimeter. It is a method for recognizing the movement of pedestrians or moving objects. However, errors can be caused, as noise and bias increase due to long-term measurement. VIO-SLAM type sensor T265, which uses a combination of cameras and IMU, and can accurately track paths in invisible spaces, is used in this study. In addition, this type of sensor can be corrected in real time with a filter function inserted into the sensor and errors can be minimized. As a comparison experiment with the encoder, it is possible to evaluate the location of the scanner within a ±10 mm error from the actual distance in 1,500 × 700 (mm) space. The usefulness of this method is verified by measuring real specimens of boiler pipes and tubes, which are the major components of power plants.

In the case of dynamic sports activities such as skiing and sprints, it is difficult to apply optical motion capture systems because of measurement volume limitation. Alternatively, the use of inertial measurement unit (IMU) as a motion sensor has gained attention. This paper proposes a drift reduction method in the IMU-based joint angle estimation for dynamic motion-involved sports applications. To resolve the problem of conventional IMU-based methods significantly reducing performance under highly dynamic conditions, the proposed method applies a correction method using joint constraint. The proposed method is the complementary filter based on the previous drift reduction technique using the joint constraint, but performs in real time. The proposed method was validated by comparing the estimation accuracy with conventional methods under various dynamic conditions. The results showed that the proposed method was superior to the methods that did not use the constraint. While the proposed method was 0.19° less accurate than the non-realtime method of the reference, it is more practical due to its realtime correction capability.