Recognition of Human Activities using the User’s Context and the Activity Theory for Risk Prediction

Alfredo Del Fabro Neto, Bruno Romero de Azevedo, Rafael Boufleuer, João Carlos D. Lima, Alencar Machado, Iara Augustin, Márcia Pasin

Abstract

Some of the activities performed daily by people may harm them physically. The performance of such activities in an inadequate manner or in an adverse environment can increase the risk of accidents. The development of context-aware systems capable of predicting these risks is important for human damage prevention. In this sense, we are developing an approach based on the Activity Theory and the Skill, Rule and Knowledge model for risk prediction of human activities in a context-aware middleware. To predict the risk in the activities, we identify the probability for the next actions and compare the current physiological context with its future state. In order to concept proving the proposed model, we developed a prototype and tested it with a public and a private dataset. The results show that the proposed model can assign an appropriate risk factor to the tested activities.

References

  1. Cook, D. (2011). Learning setting-generalized activity models for smart spaces. IEEE Intelligent Systems.
  2. Craven, P. L., Belov, N., Tremoulet, P., Thomas, M., Berka, C., Levendowski, D., and Davis, G. (2007). Cognitive workload gauge development: comparison of real-time classification methods. Foundations of Augmented Cognition, pages 75-84.
  3. Curone, D., Tognetti, A., Secco, E. L., Anania, G., Carbonaro, N., De Rossi, D., and Magenes, G. (2010). Heart rate and accelerometer data fusion for activity assessment of rescuers during emergency interventions. Information Technology in Biomedicine, IEEE Transactions on, 14(3):702-710.
  4. da Rocha, C. C., Lima, J. C. D., Viera, M., Capretz, M. A., Bauer, M. A., Augustin, I., and Dantas, M. A. (2010). A context-aware authentication approach based on behavioral definitions. In IKE, pages 178-184.
  5. Gil-Quijano, J. and Sabouret, N. (2010). Prediction of humans' activity for learning the behaviors of electrical appliances in an intelligent ambient environment. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, volume 2, pages 283-286. IEEE.
  6. Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., and Jaffe, M. W. (1963). Studies of illness in the aged: the index of adl: a standardized measure of biological and psychosocial function. Jama, 185(12):914-919.
  7. Kofod-Petersen, A. and Cassens, J. (2006). Using activity theory to model context awareness. In Modeling and Retrieval of Context, pages 1-17. Springer.
  8. Kriegel, H.-P., Kr öger, P., Schubert, E., and Zimek, A. (2009). Loop: local outlier probabilities. pages 1649- 1652.
  9. Kuutti, K. (1996). Activity theory as a potential framework for human-computer interaction research. pages 17- 44.
  10. Mikalsen, M. and Kofod-Petersen, A. (2004). Representing and reasoning about context in a mobile environment. pages 25-35.
  11. Naeem, U., Bigham, J., and Wang, J. (2007). Recognising activities of daily life using hierarchical plans. In Smart Sensing and Context, pages 175-189. Springer.
  12. Neto, A. D. F., Boufleuer, R., Romero de Azevedo, B., Augustin, I., Carlos D. Lima, J., 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.
  13. Neto, A. D. F., de Azevedo, B. R., Boufleuer, R., Lima, J. C. D., Augustin, I., and Pasin, M. (2014). An approach based on activity theory and the srk model for risk and performance evaluation of human activities in a context-aware middleware. In Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia, pages 40-47. ACM.
  14. ODonnell, R.D. & Eggemeier, F. (1986). Workload assessment methodology. In Boff, K., Kaufman, L., and Thomas, J., editors, Handbook of perception and human performance., volume 2, pages 42/1-42/49. John Wiley & Sons.
  15. Paas, F., Tuovinen, J. E., Tabbers, H., and Van Gerven, P. W. (2003). Cognitive Load Measurement as a Means to Advance Cognitive Load Theory. Educational Psychologist, 38(1):63-71.
  16. Rasch, K. (2013). Smart assistants for smart homes.
  17. Rasmussen, J. (1983). Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. Number 3, pages 257-266. IEEE.
  18. Wang, C., Zheng, Q., Peng, Y., De, D., and Song, W.- Z. (2014). Distributed abnormal activity detection in smart environments. International Journal of Distributed Sensor Networks, 2014.
Download


Paper Citation


in Harvard Style

Neto A., Azevedo B., Boufleuer R., Lima J., Machado A., Augustin I. and Pasin M. (2016). Recognition of Human Activities using the User’s Context and the Activity Theory for Risk Prediction . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-187-8, pages 282-289. DOI: 10.5220/0005832202820289


in Bibtex Style

@conference{iceis16,
author={Alfredo Del Fabro Neto and Bruno Romero de Azevedo and Rafael Boufleuer and João Carlos D. Lima and Alencar Machado and Iara Augustin and Márcia Pasin},
title={Recognition of Human Activities using the User’s Context and the Activity Theory for Risk Prediction},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2016},
pages={282-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005832202820289},
isbn={978-989-758-187-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Recognition of Human Activities using the User’s Context and the Activity Theory for Risk Prediction
SN - 978-989-758-187-8
AU - Neto A.
AU - Azevedo B.
AU - Boufleuer R.
AU - Lima J.
AU - Machado A.
AU - Augustin I.
AU - Pasin M.
PY - 2016
SP - 282
EP - 289
DO - 10.5220/0005832202820289