Due to the declining population and the aging of the farmer population in the hilly and mountainous areas of Japan, it is necessary for the elderly to carry out the mowing work on the ridges and slopes, which is traditionally regarded as heavy labor as part of paddy farming. One of the most important causes of these accidents is an incorrect mowing posture; therefore, it is important and necessary to identify effective and safe working patterns during inclined plane mowing. In this paper, we designed and implemented a set of mowing experiments in a terraced field area in Hiroshima to collect information on the body motion of experienced elderly mowing workers via a high-precision motion capture device that supports the collection of information from 23 joints. According to an analysis that calculated the angles of the workers' joints during mowing, we confirmed the characteristics of the mowing workers' working patterns in three different situations (typical inclined plane mowing (TI), top-down mowing (TD) and bottom-up mowing (BU)). The comparative analysis indicates that the basic actions "c" (cutting) and "t" (throwing) are basically the same in terms of body posture for the situation TI, but for situation TD, the difference was observed with respect to the workers' right ankle. Moreover, based on the comparation analysis for mowing action "c" (cutting), we confirmed that mowing workers should: keep their lower bodies as still as possible to keep balance for ensuring safety while working on inclined plane (TI); keep careful even if they are working on the flat ground (TD); and do not exert their utmost strength to mow, unless they are standing on the flat ground (BU). The findings of this work should be emphasized in the future development of mowing support systems and training programs for new mowing workers. INDEX TERMS motion measurement, motion analysis, experimental design, human behavior analysis, elderly support, mowing patterns comparison.

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The prebraking-related actions typically studied are the main maneuvers carried out to avoid collision. Especially for those braking actions taken when turning or parking, accidents often occur because of human errors such as the incorrect choice of pedal. However, regarding these daily braking-related driving behaviors, the effects of the driver characteristics, such as driving experience and gender, on the prebraking behaviors remain unknown. Therefore, defining prebraking behaviors as the movements of a driver's body before his or her foot touches the brake pedal, this paper identifies the details of drivers' driving behaviors while prebraking by analyzing the data collected from a wearable high-precision 23-joint motion capture device and further confirms the effects of driver experience, gender and stature on these behaviors. According to two-way analyses of variance (ANOVAs) that were performed on 100 sets of motion data collected from a set of driving experiments involving two different tasks, drivers perform similar prebraking body actions even under different braking scenarios. Moreover, the results of an interaction effects analysis confirmed the impact of drivers' experiences, gender and stature on their prebraking actions. The results of this study can serve as guidelines for future self-driving and advanced driver assistance system (ADAS) development and provide useful insights for the identification and training of new drivers.

Cloud computing is generally considered as a special energy-efficient form for the Internet of Things (IoT) resource usage. Dedicated server systems for cloud services, better capacity utilization and economies of scale because of the use of larger and more energy-efficient data centers are the reasons why cloud solutions typically use less energy than traditional on-premise systems. To scientifically and rationally configure the hardware and software resources of the cloud computing, the research on forecasting a cloud computing resource load becomes a research focus. However, the widely-used single forecasting model cannot contain all the characteristics of the cloud computing resource load sequence, resulting in inaccurate forecasting results. In this paper, a combined forecasting approach of cloud computing resource load based on wavelet decomposition is proposed, which combined the grey model and cubic exponential smoothing model. It can well preserve details and reduce noise. Firstly, the cloud computing resource load sequence is decomposed into several frequencies by the wavelet decomposition method. The decomposed load sequences with different characteristics are divided into different resolution scale subspaces in deferent frequencies. The noise of the load sequences is reduced by the wavelet threshold denoising method. And then, the load sequences are reconstructed according to the wavelet coefficients. The reconstructed load sequence not only contains less noise but also reserves detailed information. Consequently, it is closer to the real data and more regular. Experimental results show that our proposed combined forecasting model with wavelet decomposition can provide more accurate forecasting results than each single forecasting model or the combined forecasting model without using the wavelet decomposition method. Thus, our proposal is demonstrated to be efficient for forecasting the cloud computing resource load and helping to reduce energy consumption.INDEX TERMS Cloud computing resource load, wavelet decomposition, combined forecasting model, grey model, cubic exponential smoothing model.