Abstract
Sports have been deemed beneficial for the better functioning of a person. Indulgence in sporting activities is a rich and rewarding experience. However, the amount of mental stress increases for athletes who compete in professional sports and academic students. The challenging characteristics in the mental state of sports students include: Lack of Proper fitness, Poor skill development, and Overtraining enrollment are considered essential factors. Hence, in this paper, a human–computer interaction-based Augmented intercommunication framework (AICF) has been proposed to enhance the sports student's current competitiveness, performance experience, and mental state analysis. The location-based strategy is introduced to improve mental strength and training in preparation for the sports student. The behaviour recognition algorithm is integrated with AICF to boost sports students' skill production, both physically and mentally. The simulation results show that an augmented intercommunications system achieves 97% accuracy, a performance of 98%, feasibility of 95%, resilience of 16%, and an efficiency of 99%.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services in a cloud computing environment. Measurement, 119, 117–128.
Al–Turjman, F. M., & Hassanein, H. S. (2012). Towards augmented connectivity with delay constraints in WSN federation. International Journal of Ad Hoc and Ubiquitous Computing, 11(2–3), 97–108.
Assaf, I., Brieteh, F., Tfaily, M., El-Baida, M., Kadry, S., & Balusamy, B. (2019). Students university healthy lifestyle practice: A quantitative analysis. Health Information Science and Systems, 7(1), 1–12.
Chang, C. C., & Hu, X. P. (2006). The effect of an environmental, nutritional intervention on knowledge and practice of college dormitory students. Nutritional Sciences Journal, 31(2), 40–48.
Chen, M. Y., Chen, K. C., Chang, C., & Hsu, J. (2013). Volume shrinkage of iliac bone blocks for alveolar ridge augmentation. International Journal of Oral and Maxillofacial Surgery, 42(10), 1343.
Chen, M., & Jiang, S. (2019). Analysis and research on the mental health of college students based on cognitive computing. Cognitive Systems Research, 56, 151–158.
Cihan, B. B. (2018). The analysis of problem-solving skills and related factors for some students studying at different schools of physical education and sports. Asian Journal of Education and Training, 4(4), 295–301.
Cubillos-Calvachi, J., Piedrahita-Gonzalez, J., Gutiérrez-Ardila, C., Montenegro-Marín, C., Gaona-García, P., & Burgos, D. (2020). Analysis of stress’s effects on cardiac dynamics: A case study on undergraduate students. International Journal of Medical Informatics, 137, 104104.
Gümüs, H., Gençoglu, C., & Sahin, T. (2020). Physical education and sports: Bibliometric analysis of the ERIC database. International Online Journal of Education and Teaching, 7(4), 1823–1837.
Huifeng, W., Shankar, A., & Vivekananda, G. N. (2020). Modelling and simulation of sprinters’ health promotion strategy based on sports biomechanics. Connection Science, 33, 1028–1046.
Ivanova, N. L., & Korostelev, A. A. (2019). The impact of the competitive approach on students’ motivation in sport. Amazonia Investiga, 8(18), 483–490.
Kumar, A. D., Thodupunoori, H., Vinayakumar, R., Soman, K. P., Poornachandran, P., Alazab, M., &Venkatraman, S. (2019). Enhanced domain generating algorithm detection based on deep neural networks. In Deep learning applications for cyber security (pp. 151–173). Springer, Cham.
Le, N. T., Wang, J. W., Wang, C. C., & Nguyen, T. N. (2019). Automatic defect inspection for coated eyeglass based on symmetrized energy analysis of color channels. Symmetry, 11(12), 1518.
Liu, B. H., Pham, V. T., & Nguyen, N. T. (2015). A virtual backbone construction heuristic for maximizing the lifetime of dual-radio wireless sensor networks. In 2015 International conference on intelligent information hiding and multimedia signal processing (IIH-MSP) (pp. 64–67). IEEE.
Lochbaum, M., Zanatta, T., & Kazak, Z. (2020). The 2× two achievement goals in sport and physical activity contexts: A meta-analytic test of context, gender, culture, socioeconomic status differences and analysis of motivations, regulations, affect, effort, and physical activity correlates. European Journal of Investigation in Health, Psychology and Education, 10(1), 173–205.
Lv, Z., Han, Y., Singh, A. K., Manogaran, G., & Lv, H. (2020). Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Transactions on Industrial Informatics, 17(2), 1496–1504.
Manogaran, G., Alazab, M., Saravanan, V., Rawal, B.S., Shakeel, P.M., Sundarasekar, R., Nagarajan, S.M., Kadry, S.N. and Montenegro-Marin, C.E. (2020). Machine learning assisted information management scheme in service concentrated IoT. IEEE Transactions on Industrial Informatics, 17(4), 2871–2879.
Nguyen, C. H., Pham, T. L., Nguyen, T. N., Ho, C. H., & Nguyen, T. A. (2021). The linguistic summarization and the interpretability, scalability of fuzzy representations of multilevel semantic structures of word-domains. Microprocessors and Microsystems, 81, 103641.
Parthiban KT, Seenivasan R, Vennila S, Anbu PV, Kumar P, Saravanan V, Kanna SU, Rajendran P, Subbulakshmi V, Durairasu P. (2011). Designing and augmenting the pulpwood supply chain through contract tree farming. Indian Journal of Ecology, 38, 41–47.
Schinke, R. J., Stambulova, N. B., Si, G., & Moore, Z. (2018). International society of sport psychology position stand: Athletes’ mental health, performance, and development. International Journal of Sport and Exercise Psychology, 16(6), 622–639.
Shannon, S., Breslin, G., Haughey, T., Sarju, N., Neill, D., Lawlor, M., & Leavey, G. (2019). Predicting student-athlete and non-athletes’ intentions to self-manage mental health: Testing an integrated behaviour change model. Mental Health & Prevention, 13, 92–99.
Soulliard, Z. A., Kauffman, A. A., Fitterman-Harris, H. F., Perry, J. E., & Ross, M. J. (2019). Examining positive body image, sport-confidence, flow state, and subjective performance among student-athletes and non-athletes. Body Image, 28, 93–100.
Verma, C., Stoffová, V., Illés, Z., Tanwar, S., & Kumar, N. (2020). Machine learning-based student’s native place identification for real-time. IEEE Access, 8, 130840–130854.
Xu, X., Chen, Y., Zhang, J., Chen, Y., Anandhan, P., & Manickam, A. (2020). A novel approach for scene classification from remote sensing images using deep learning methods. European Journal of Remote Sensing, 54, 383–395.
Zeiger, J. S., & Zeiger, R. S. (2018). Mental toughness latent profiles in endurance athletes. PLoS ONE, 13(2), e0193071.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lan, X., Cao, Z. & Yu, L. Analyzing the mental states of the sports student based on augmentative communication with human–computer interaction. Int J Speech Technol 25, 355–365 (2022). https://doi.org/10.1007/s10772-021-09947-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-021-09947-4