Modelo para classificação do esforço cognitivo de atividades humanas em um sistema sensível ao contexto
Resumo
O esforço cognitivo despedido por uma pessoa durante a realização de uma atividade não é possível de ser calculado de forma direta. Assim, percebe-se a necessidade de avaliar o nível de desempenho da atividade para que seja possível a inferência do esforço cognitivo utilizado. Em um sistema sensível ao contexto, é possível a captura de informações que permitam a inferência de propriedades para a avaliação de um desempenho. Assim, propõe-se a criação de um modelo de classificação do esforço cognitivo baseado no modelo comportamental habilidade-regra-conhecimento e nas relações de propriedades de desempenho com o contexto em que a pessoa está inserida.
Referências
Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., and Steggles, P. (1999). Towards a better understanding of context and context-awareness. In Handheld and ubiquitous computing, pages 304–307. Springer.
Cook, J. R. and Salvendy, G. (1999). Job enrichment and mental workload in computerbased work: Implications for adaptive job design. International Journal of Industrial Ergonomics, 24(1):13–23.
Datta, S., Banerjee, A., Konar, A., and Tibarewala, D. (2014). Electrooculogram based cognitive context recognition. In Electronics, Communication and Instrumentation (ICECI), 2014 International Conference on, pages 1–4. IEEE.
Del Fabro Neto, A., Boufleuer, R., Romero de Azevedo, B., Augustin, I., D. Lima, J. C., and C Rocha, C. (2013). Towards a middleware to infer the risk level of an activity in context-aware environments using the srk model. In UBICOMM 2013, The Seventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pages 38–42.
Derakshan, N. and Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance. European Psychologist, 14(2):168–176.
Eysenck, M. W., Derakshan, N., Santos, R., and Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2):336.
Gray, R. (2004). Attending to the execution of a complex sensorimotor skill: expertise differences, choking, and slumps. Journal of Experimental Psychology: Applied, 10(1):42.
Lin, C. J., Shiang, W.-J., Chuang, C.-Y., and Liou, J.-L. (2014). Applying the skill-ruleknowledge framework to understanding operators’ behaviors and workload in advanced main control rooms. Nuclear Engineering and Design, 270:176–184.
Loveday, T., Wiggins, M., Festa, M., Schell, D., and Twigg, D. (2013). Pattern recognition as an indicator of diagnostic expertise. In Pattern Recognition-Applications and Methods, pages 1–11. Springer.
Rantanen, E. M. and Levinthal, B. R. (2005). Time-based modeling of human performance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 49, pages 1200–1204. SAGE Publications.
Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. Systems, Man and Cybernetics, IEEE Transactions on, (3):257–266.
Robert, G. and Hockey, J. (1997). Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework. Biological psychology, 45(1):73–93.
Skalle, P., Aamodt, A., and Laumann, K. (2014). Integrating human related errors with technical errors to determine causes behind offshore accidents. Safety Science, 63:179– 190.
Stahl, P., Donmez, B., and Jamieson, G. A. (2013). Anticipatory driving competence: motivation, definition & modeling. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 286–291. ACM.
Ujita, H., Kawano, R., and Yoshimura, S. (1995). An approach for evaluating expert performance in emergency situations. Reliability Engineering & System Safety, 47(3):163–173.
Vicente, K. J. and Rasmussen, J. (1992). Ecological interface design: Theoretical foundations. Systems, Man and Cybernetics, IEEE Transactions on, 22(4):589–606.
Woodcock, K. (2014). Human factors and use of amusement ride control interfaces. International Journal of Industrial Ergonomics, 44(1):99–106.
Cook, J. R. and Salvendy, G. (1999). Job enrichment and mental workload in computerbased work: Implications for adaptive job design. International Journal of Industrial Ergonomics, 24(1):13–23.
Datta, S., Banerjee, A., Konar, A., and Tibarewala, D. (2014). Electrooculogram based cognitive context recognition. In Electronics, Communication and Instrumentation (ICECI), 2014 International Conference on, pages 1–4. IEEE.
Del Fabro Neto, A., Boufleuer, R., Romero de Azevedo, B., Augustin, I., D. Lima, J. C., and C Rocha, C. (2013). Towards a middleware to infer the risk level of an activity in context-aware environments using the srk model. In UBICOMM 2013, The Seventh International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pages 38–42.
Derakshan, N. and Eysenck, M. W. (2009). Anxiety, processing efficiency, and cognitive performance. European Psychologist, 14(2):168–176.
Eysenck, M. W., Derakshan, N., Santos, R., and Calvo, M. G. (2007). Anxiety and cognitive performance: attentional control theory. Emotion, 7(2):336.
Gray, R. (2004). Attending to the execution of a complex sensorimotor skill: expertise differences, choking, and slumps. Journal of Experimental Psychology: Applied, 10(1):42.
Lin, C. J., Shiang, W.-J., Chuang, C.-Y., and Liou, J.-L. (2014). Applying the skill-ruleknowledge framework to understanding operators’ behaviors and workload in advanced main control rooms. Nuclear Engineering and Design, 270:176–184.
Loveday, T., Wiggins, M., Festa, M., Schell, D., and Twigg, D. (2013). Pattern recognition as an indicator of diagnostic expertise. In Pattern Recognition-Applications and Methods, pages 1–11. Springer.
Rantanen, E. M. and Levinthal, B. R. (2005). Time-based modeling of human performance. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, volume 49, pages 1200–1204. SAGE Publications.
Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. Systems, Man and Cybernetics, IEEE Transactions on, (3):257–266.
Robert, G. and Hockey, J. (1997). Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework. Biological psychology, 45(1):73–93.
Skalle, P., Aamodt, A., and Laumann, K. (2014). Integrating human related errors with technical errors to determine causes behind offshore accidents. Safety Science, 63:179– 190.
Stahl, P., Donmez, B., and Jamieson, G. A. (2013). Anticipatory driving competence: motivation, definition & modeling. In Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 286–291. ACM.
Ujita, H., Kawano, R., and Yoshimura, S. (1995). An approach for evaluating expert performance in emergency situations. Reliability Engineering & System Safety, 47(3):163–173.
Vicente, K. J. and Rasmussen, J. (1992). Ecological interface design: Theoretical foundations. Systems, Man and Cybernetics, IEEE Transactions on, 22(4):589–606.
Woodcock, K. (2014). Human factors and use of amusement ride control interfaces. International Journal of Industrial Ergonomics, 44(1):99–106.
Publicado
28/07/2014
Como Citar
DE AZEVEDO, Bruno; DEL FABRO NETO, Alfredo; BOUFLEUER, Rafael; LIMA, João Carlos; AUGUSTIN, Iara.
Modelo para classificação do esforço cognitivo de atividades humanas em um sistema sensível ao contexto. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 6. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 80-89.
ISSN 2595-6183.
