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The purpose of this paper is to examine how the levels of athletic identity influenced the relationships among college experiences, career self-efficacy, and the career barriers faced by semi-professional student-athletes in Taiwan.

Survey data of athletic identity, college experiences (involvement in social, academic, career orientation activities, etc.), career self-efficacy, and career barriers (inherent difficulties in career exploration in a dual student-professional role) were collected from 345 varsity student-athletes in Taiwan sport universities. The conditional process analysis was conducted to test whether the mediating role of career self-efficacy in the association between college experiences and career barriers was dependent on athletic identity levels.

The results indicated that enriching college experiences contributed to fewer barriers regarding career exploration post graduation through high levels of career self-efficacy. Particularly for assorted and social experiences, the indirect effect reduced as the levels of athletic identity increased.

These findings may be applied to athletes who pursue early specialization and students who engage heavily in a specific sport or other co-curricular activities while neglecting the importance of scholastic learning and career exploration. Career counseling practitioners and sport psychologists are advised to take account of athletic identity and contextual specificity while providing career interventions to student-athletes.

This study extends the understanding of the impacts of athletic identity on college student-athletes’ career explorations within a specific sport context.

The purpose of this paper is to propose a fast object detection algorithm based on structural light analysis, which aims to detect and recognize human gesture and pose and then to conclude the respective commands for human-robot interaction control.

In this paper, the human poses are estimated and analyzed by the proposed scheme, and then the resultant data concluded by the fuzzy decision-making system are used to launch respective robotic motions. The RGB camera and the infrared light module aim to do distance estimation of a body or several bodies.

The modules not only provide image perception but also objective skeleton detection. In which, a laser source in the infrared light module emits invisible infrared light which passes through a filter and is scattered into a semi-random but constant pattern of small dots which is projected onto the environment in front of the sensor. The reflected pattern is then detected by an infrared camera and analyzed for depth estimation. Since the depth of object is a key parameter for pose recognition, one can estimate the distance to each dot and then get depth information by calculation of distance between emitter and receiver.

Future work will consider to reduce the computation time for objective estimation and to tune parameters adaptively.

The experimental results demonstrate the feasibility of the proposed system.

This paper achieves real-time human-robot interaction by visual detection based on structural light analysis.