Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.

One of the UNESCO intangible cultural heritages Bunraku puppets can play one of the most beautiful puppet motions in the world. The Bunraku puppet motions can express emotions without the so-called ‘Uncanny Valley.’ We try to convert these emotional motions into robot affective motions so that robots can interact with human beings more comfortable. In so doing, in the present paper, we present a robot motion design framework using Bunraku affective motions that are based on the so-called ‘Jo-Ha-Kyū,’ and convert a few simple Bunraku motions into a robot motions using one of deep learning methods. Our primitive experiments show that Jo-Ha-Kyū can be incorporated into robot motion design smoothly, and some simple affective robot motions can be designed using our proposed framework.

In golf swing analysis, high-speed cameras and Trackman devices are traditionally used to collect data about the club, ball, and putt. However, these tools are costly and often inaccessible to golfers. This research proposes an alternative solution, employing an affordable inertial motion capture system to record golf swing movements accurately. The focus is discerning the differences between motions producing straight and slice trajectories. Commonly, the opening motion of the body’s left half and the head-up motion are associated with a slice trajectory. We employ the Hilbert–Huang transform (HHT) to examine these motions in detail to conduct a biomechanical analysis. The gathered data are then processed through HHT, calculating their instantaneous frequency and amplitude. The research found discernible differences between straight and slice trajectories in the golf swing’s moment of impact within the instantaneous frequency domain. An average golfer, a single handicapper, and three beginner golfers were selected as the subjects in this study and analyzed using the proposed method, respectively. For the average golfer, the head and the left leg amplitudes of the swing motions increase at the moment of impact of the swings, resulting in the slice trajectory. These results indicate that an opening of the legs and head-up movements have been detected and extracted as non-linear frequency components, reviewing the biomechanical meaning in slice trajectory motion. For the single handicapper, the hip and left arm joints could be the target joints to detect the biomechanical motion that triggered the slice trajectory. For the beginners, since their golf swing forms were not finalized, the biomechanical motions regarding slice trajectory were different from each swing, indicating that beginner golfers need more practice to fix their golf swing form first. These results revealed that our proposed framework applied to different golf levels and could help golfers to improve their golf swing skills to achieve straight trajectories.

We conducted an online national survey using the South Oaks Gambling Screen (SOGS) on 42,880 people in Japan to identify gambling addiction prevalence differences with regard to gambling type and region. This study included 14,780 individuals (valid responses) who engaged in gambling activities in the past year. There was a large difference in mean SOGS score by gambling type: public gambling and casinos score ≈ 4.0, whereas lotteries (including numbers, etc.) ≈ 1.43. SOGS scores were much higher for those who participated in more than one gambling type than for those who participated in only one type. Regional differences in SOGS scores were also confirmed, with more populous prefectures having lower and less populous having higher SOGS scores. Integrating SOGS scores with existing data concerning hobbies and recreational behaviors across regions indicated that regions characterized by lower SOGS scores were also characterized by greater activity for hobbies and recreational behaviors.

In the mountainous areas of Japan, the weeds on the slopes of terraced rice paddies still need to be cut by the elderly manually. Therefore, more attention should be given to maintain proper postures while performing mowing actions (especially the pre-cutting actions) to reduce the risk of accidents. Given that complex mowing actions can be decomposed into different sub-actions, we proposed a joint angular calculation-based body movement analysis model based on the Hilbert–Huang transform to analyze the pre-cutting actions. We found that the two most important sub-actions were fast pre-cutting and slow pre-cutting. Based on field experiments, we analyzed the pre-cutting actions of workers with different experience levels and identified the factors that affected their falling risk (stability). The results showed differences and similarities in the actions’ frequency and amplitude in the sub-actions of workers with different mowing experience, confirmed the influence of body characteristics (body height, etc.) on body stability, and showed that workers should pay attention to their age and ankle part while mowing. The analysis results have identified factors for the mowing workers’ training and the development of equipment for use in complicated geographical conditions.