Reference¶
Activity Recognition¶
| [Cook2009] | Cook, D.J. and Schmitter-Edgecombe, M., 2009. Assessing the quality of activities in a smart environment. Methods of information in medicine, 48(5), p.480. |
| [Kim2010] | Kim, E., Helal, S. and Cook, D., 2010. Human activity recognition and pattern discovery. IEEE Pervasive Computing, 9(1). |
| [Cook2012] | Cook, D.J., 2012. Learning setting-generalized activity models for smart spaces. IEEE intelligent systems, 27(1), pp.32-38. |
| [Krishnan2014] | Krishnan, N.C. and Cook, D.J., 2014. Activity recognition on streaming sensor data. Pervasive and mobile computing, 10, pp.138-154. |
Performance Metrics¶
| [Minnen2006] | Minnen, D., Westeyn, T., Starner, T., Ward, J. and Lukowicz, P., 2006. Performance metrics and evaluation issues for continuous activity recognition. Performance Metrics for Intelligent Systems, 4. |
| [Sokolova2009] | Sokolova, M. and Lapalme, G., 2009. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), pp.427-437. |
| [Ward2011] | Ward, J.A., Lukowicz, P. and Gellersen, H.W., 2011. Performance metrics for activity recognition. ACM Transactions on Intelligent Systems and Technology (TIST), 2(1), p.6. |
| [Hammerla2015] | Hammerla, N.Y. and Plötz, T., 2015, September. Let’s (not) stick together: pairwise similarity biases cross-validation in activity recognition. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1041-1051). ACM. |