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.
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Damage to the units related to driving and running of the railway vehicle may cause an inevitable accident due to defects and malfunctions in operation. In order to prevent such an accident, a non-destructive diagnostic technology that detects the damage is required. Previous researchers have researched and developed a monitoring system of the infrared thermography method to diagnose the condition of the railway vehicle driving and driving units. A system for monitoring running of the railway vehicle and temperature condition of the drive unit at a vehicle speed of 30 to 100 km/h was constructed, and a study on its applicability was conducted. In this study, a system for diagnosing an abnormal condition of the driving and running units while the vehicle is running with an infrared thermography diagnostic system was installed in the depot and operation route, and evaluation of the abnormal condition of the driving and running units was performed. The results show that the diagnosis system using infrared thermography can be used to identify abnormal conditions in the driving and running units of a railway vehicle. The diagnosis system can effectively inspect the normal and abnormal conditions in operation of a railway vehicle.
As interest in the quality of life has recently increased, there is a growing interest and demand for exercise equipment, such as indoor treadmills or cycles, which can be used at home. However, the use of such indoor exercise equipment has caused social problems by generating noise between floors and causing inconvenience to neighbors. In particular, treadmills that generate a lot of vibration during use cause more social problems in an assembly building, such as an apartment. The purpose of this study is to design dampers of various shapes and to develop dampers with high vibration damping effects through vibration analysis. The damper was installed at the lower end of the treadmill to reduce vibration from the product. Three types of dampers were designed by referring to the damper shape of the existing treadmill, and the vibration reduction effect of each damper shape was verified through structural analysis of the magnitude of vibration generated from the bottom surface of each damper.
The swing motion bogie system for a freight car is more effective regarding the vibration damping effect than other freight car bogie systems while operating, and it is a bogie system that can travel up to 120 km/h despite being a freight car. Imported in 2006 in Korea and operated for more than 10 years in the domestic railway environment, the performance and maintenance efficiency have been proven compared to the existing welding bogies. As a result, the domestic demand will continue increasing in the future, but it is now dependent on overseas imports. In the long term, it is expected to cause problems such as loss of foreign currency and delay in procurement during maintenance. For this reason, development of the localization of the swing motion bogie system is underway, and it requires accurate performance analysis and validation of operating behavior characteristics because the bogie system is one of the main devices of the railway vehicles. Thus, in this study, we could confirm the suitability of the swing motion bogie system in the domestic operating environment based on the analysis of the operating behavior characteristics, the validation at the laboratory environment, and the operating test on the track.
Previous studies on joint angle estimation have been restricted to slow-speed level walking conditions, even though slope walking and running elicit unique biomechanical characteristics. Measurements were mostly based on an optical motion capture system despite in-the-lab limitation of measurement technique. The contribution of this study is twofold: (i) to propose a joint angle estimation method by applying a state-of-the-art parallel Kalman filter based on an inertial measurement unit (IMU) that can overcome in-the-lab limitation, and (ii) to demonstrate its application to level walking condition as well as slope walking and running conditions to fill a gap in joint kinematics literature. In particular, this study focuses on knee flexion/extension and ankle dorsiflexion/plantarflexion angles at various speed variations. The parallel Kalman filter applied in the proposed method can compensate external acceleration through Markov-chain-based acceleration modeling, that may enhance joint estimation performance in high speed walking conditions. To validate the proposed estimation method, an optical motion capture system was used as reference. In addition, patterns for each condition were investigated to identify and evaluate presence of classifying features.
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