Estimating energy expenditure is essential in monitoring the intensity of physical activity and health status. Energy expenditure can be estimated based on wearable sensors such as inertial measurement unit (IMU). While a variety of methods have been developed to estimate energy expenditure during day-to-day activities, their performances have not been thoroughly evaluated under walking conditions according to various speeds and inclines. This study investigated IMU-based neural network models for energy expenditure estimation under various walking conditions and comparatively analyzed their performances in terms of sensor attachment locations and training/testing datasets. In this study, two neural network models were selected based on a previous study (Slade et al., 2019): (M1) a multilayer perceptron using sensor signals during each gait cycle, and (M2) a recurrent neural network using sensor signal sequences of a fixed window size. The results revealed the following: (i) the performance of the foot attachment model was the best among the five sensor attachment locations (0.89 W/kg for M1 and 1.14 W/kg for M2); and (ii) although the performance of M1 was superior to that of M2, M1 requires accurate gait detection for data segmentation by each stride, which hinders the usefulness of M2.

In this study, the Inertial Measurement Unit (IMU) signals and clinical evaluation scales for Parkinson"s disease were correlated. The study included 16 patients diagnosed with Parkinson"s disease. Each subject was evaluated based on Korean Mini-Mental State Examination (KMMSE), Unified Parkinson"s Disease Rating Scale (UPDRS) part 3, New Freezing of Gait Questionnaire (NFOGQ) parts 2 & 3, and Hoehn & Yahr Scale (H&Y). All subjects performed the Time Up and Go test by attaching IMU sensors to both ankles and torso. Based on the tilting angle of torso and the time of first step, the freezing and non-freezing windows were determined. Seven IMU features involving the ankle signals were calculated in the specific window. Spearman’s correlation analysis of clinical evaluation scales was performed. As a result, the freezing index and power of locomotion band (0.3-3 Hz) were recommended to determine UPDRS part 3. Also, the intensity of the locomotion band facilitated evaluation of NFOGQ part 3 regardless of freezing of gait.

Inertial measurement unit (IMU)-based 3D joint angle estimation have a wide range of important applications, among them, in gait analysis and exoskeleton robot control. Conventionally, the joint angle was determined via the estimation of 3D orientation of each body segment using 9-axis IMUs including 3-axis magnetometers. However, a magnetometer is limited by magnetic disturbance in the vicinity of the sensor, which highly affects the accuracy of the joint angle. Accordingly, this study aims to estimate the joint angle using the 6-axis IMU signals composed of a 3-axis accelerometer and a 3-axis gyroscope without a magnetometer. This paper proposes a recurrent neural network (RNN) model, which indirectly utilizes the joint kinematic constraint and thus estimates joint angles based on 6-axis IMUs without using a magnetometer signal. The performance of the proposed model was validated for a mechanical joint and human elbow joint, under magnetically disturbed environments. Experimental results showed that the proposed RNN approach outperformed the conventional approach based on a Kalman filter (KF), i.e., RNN 3.48° vs. KF 10.01° for the mechanical joint and RNN 7.39° vs. KF 21.27° for the elbow joint.

In this study, an insole-type ground reaction force (GRF) measurement system using a load cell was manufactured and configured as a system that can measure joint angle and GRF, when walking in conjunction with a commercialized inertial sensor. The data acquisition device was used to acquire synchronized data, between the inertial measurement unit (IMU) sensor and the load cell insole. A three-dimensional motion analysis system comprising six infrared cameras and two ground reaction forces, was used to check the accuracy of the gait measurement system, comprising an inertial sensor and a load cell insole. The motion and force data were acquired while performing five times six-meter walking test by five young adult male subjects (Age: 26.0±1.8, Height: 171.4±6.8 cm, Weight: 62.2±10.8 kg). It was measured and as a result of comparing the calculated sagittal joint angle with the vertical GRF, the sagittal lower extremity joint angle correlation coefficient (Pearson’s r) was 0.40 to 0.94, and the vertical GRF to be 0.98 to 0.99. It is necessary to upgrade the joint angle calculation algorithm through future research. Additionally, the possibility of clinical application for actual stroke patients will be reviewed.

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.