What are the kinematic characteristics of the world champion couple in competitive ballroom dance during the waltz's spin movement?

To evaluate competitive ballroom dancing, one of the official events of the World Games, spin movements used by advanced dancers who ranked high in actual competition may be employed. The purpose of this study was to investigate the kinematic characteristics of spin movements in competitive ballroom dancing performed by world champion couples, especially holding posture and lower limb movements. A champion couple and 13 national-level competitive ballroom dancers as the control group participated in this study. An inertial measurement unit (IMU) system (MVN Link; Xsens, Netherlands) consisting of 17 IMUs, attached to feet, shanks, thighs, pelvis, sternum, head, upper arms, forearms, and hands of the dancers, was used to obtain three-dimensional kinematic data at 240 Hz. The overall trend was that the actual and normalized stride lengths of the champion male and female dancers tended to be longer than those of the male and female dancers in the control group. Further, large differences were observed in the pelvic and rib cage segments movements, and the relative angle of the rib cage segment to the pelvic segment. The champion male dancer started to move from the pelvic segment, whereas the champion female dancer and the national-level top male and female dancers started to move from the rib cage segment. During the spin movement, the champion male dancer was in a position where the rib cage segment was rotated to the left with respect to the pelvic segment, whereas the other dancers were in a position where the rib cage segment was rotated to the right. Although limited to technical aspects, these dance kinematic characteristics of the world champion couples and their differences from those of the control group dancers, will be helpful to competitive ballroom dancers and coaches in their daily practice

RWC Music Database: Music Genre Database and Musical Instrument Sound Database

This paper describes the design policy and specifications of the RWC Music Database, a copyright-cleared music database (DB) compiled specifically for research purposes. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. We therefore recently constructed the RWC Music Database comprising four original component DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database (15 pieces), Classical Music Database (50 pieces), and Jazz Music Database (50 pieces). In this paper we report the construction of two additional component DBs: the Music Genre Database (100 pieces) and Musical Instrument Sound Database (50 instruments). For all 315 musical pieces, we prepared original audio signals, corresponding standard MIDI files, and text files of lyrics (for songs). For all 50 instruments, we recorded individual sounds at half-tone intervals with several variations of playing styles, dynamics, instrument manufacturers, and musicians. It is our hope that our DB will make a significant contribution to future advances in the field of music information processing

Social Issues of Setting and Context in `Real-life' Ubicomp

This paper explores some of the issues, primarily the problem of `context', that accompany extending the reach of ubiquitous computing out of the lab or demonstration space and into the `real-world', via an initial analysis of observations from one such exercise, a conference information support system used at the JSAI 2005 annual conferenc

Robust Estimation of Google Counts for Social Network Extraction

Various studies within NLP and Semantic Web use the so-called Google count, which is the hit count on a query returned by a search engine (not only Google). However, sometimes the Google count is unreliable, especially when the count is large, or when advanced operators such as OR and NOT are used. In this paper, we propose a novel algorithm that estimates the Google count robustly. It (i) uses the co-occurrence of terms as evidence to estimate the occurrence of a given word, and (ii) integrates multiple evidence for robust estimation. We evaluated our algorithm for more than 2000 queries on three datasets using Google, Yahoo! and MSN search engine. Our algorithm also provides estimate counts for any classifier that judges a web page as positive or negative. Consequently, we can estimate the number of documents with included references of a particular person (among namesakes) on the entire web