In recent years, research has been tried in various ways to incorporate sensitivity (emotion, feeling) in music content service. At home and abroad in a number of commercial music services, concept of user sensitivity has been incorporated in order to make their users feel more friendly and comfortable. However, the number of emotion words applied to those services was too small to express the varied and complex emotions user wants. In addition, the number of emotional contents was not sufficient. In this paper, we proposed the music sensitivity model and evaluation method, as a key component of customized service which provides personalized content search and social recommendation features. The proposed model provides a number of emotional words, built based on a musical vocabulary freely expressed on the Web, and intuitive classification of emotional words including mood (human feeling), emotion (content feeling), situation, and property categories. The proposed evaluation method provides an intuitive user interface for users to easily express their sensitivity, what they feels after listening music contents. The experiment was performed on 20 subjects, tested for lexical classification and sensitivity evaluation tasks, and the results showed the proposed method are meaningfully close to user intuition.

The validity of the results from observational methods such as RULA, REBA, OWAS has been one of major concerns due to their subjective characteristics in determining the posture of interests. There have been many studies regarding validity of the results from each checklist. However, most studies provided only fragmentary rather than comprehensive results in nature. This study specifically tried to analyze consistency of novice user based on intra-observer consistency and sensitivity of industrial types during MSDs(Musculoskekltal Disorders) evaluation with major checklists. In this study, twenty two novice subjects were participated to conduct MSDs evaluation for the forty five jobs from three types of industries(automobile, electronics, hospital). The main results for this study were summarized as follows; 1) The action level based on RULA was always higher than that from REBA and OWAS for all three types of industries., 2) The order of consistency from novice users was OWAS(72.7%(kappa=0.57)) RULA(54.3%(kappa=0.41)), REBA(41.0%(kappa=0.34))., 3) The percentage of agreement between 2nd and 3rd trials was higher than those between 1st and 2nd trials and between 1st and 3rd trials irrespective of industrial types during using RULA and REBA., 4) The average score of automobile industry was higher than those of hospital and electronics industries., 5) The types of jobs associated with five body parts(A1(Front), A2(Interior), A3(Rear), A4(Lower), A5(Door)) in automobile industry showed statistically significant differences in terms of MSDs scores for the body parts considered in each checklists.