Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data
Max Schröder, Sebastian Bader, Frank Krüger, Thomas Kirste
2016
Abstract
The reconstruction of human activities is an important prerequisite to provide assistance. In this paper, we present an activity and plan recognition approach which is based on causal models of human activities. We show, that it is possible to estimate current activities, the underlying goal of the user, and context information about the state of the environment from noisy sensor data. Therefore we use real world data obtained from a smart home system while observing unrestricted activities of daily living in an inhabited flat. We evaluate the accuracy of the recognition for simulated data of different granularity and data obtained from the smart home system. We furthermore show that performance measures solely based on action sequences are not sufficient to evaluate a recognition system.
References
- Baker, C. L., Saxe, R., and Tenenbaum, J. B. (2009). Action understanding as inverse planning. Cognition, 113(3):329-349.
- Bao, L. and Intille, S. (2004). Activity recognition from user-annotated acceleration data. In Ferscha, A. and Mattern, F., editors, Pervasive Computing, volume 3001 of LNCS, pages 1-17. Springer.
- Blaylock, N. and Allen, J. (2014). Hierarchical goal recognition. In Sukthankar, G., Goldman, R. P., Geib, C., Pynadath, D. V., and Bui, H. H., editors, Plan, activity, and intent recognition, pages 3-32. Elsevier, A'dam.
- Bui, H. H., Venkatesh, S., and West, G. A. W. (2002). Policy Recognition in the Abstract Hidden Markov Model. J. of Artificial Intelligence Research , 17:451-499.
- Fikes, R. E. and Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. In Proc. of the second Int. Joint Conf. on Artificial Intelligence (IJCAI) , pages 608-620, San Francisco. Morgan Kaufmann.
- Hiatt, L., Harrison, A., and Trafton, G. (2011). Accommodating human variability in human-robot teams through theory of mind. In Proc. of the 22nd Int. Joint Conf. on Artificial Intelligence (IJCAI) , pages 2077- 2071, Barcelona, Spain.
- Krüger, F., Nyolt, M., Yordanova, K., Hein, A., and Kirste, T. (2014). Computational state space models for activity and intention recognition. a feasibility study. PLoS ONE, 9(11):e109381.
- Lee, S.-W. and Mase, K. (2002). Activity and location recognition using wearable sensors. Pervasive Computing, IEEE, 1(3):24-32.
- Liao, L., Patterson, D. J., Fox, D., and Kautz, H. (2007). Learning and inferring transportation routines. AI, 171(5-6):311-331.
- Mcdermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., and Wilkins, D. (1998). Pddl - the planning domain definition language. Technical Report TR-98-003, Yale Center for Computational Vision and Control,.
- McEwen, A. and Cassimally, H. (2014). Designing the Internet of Things. John Wiley and Sons, Ltd.
- Nyolt, M., Kr üger, F., Yordanova, K., Hein, A., and Kirste, T. (2015). Marginal filtering in large state spaces. Int. J. of Approximate Reasoning.
- Ramírez, M. and Geffner, H. (2011). Goal recognition over POMDPs: inferring the intention of a POMDP agent. In Proc. of the 22nd IJCAI, pages 2009-2014. AAAI.
- Sukthankar, G., Goldman, R. P., Geib, C., Pynadath, D. V., and Bui, H. H. (2014). Plan, activity, and intent recognition. Elsevier, A'dam.
- van Kasteren, T. L. M. (2011). Activity Recognition for Health Monitoring Elderly using Temporal Probabilistic Models. PhD thesis, Universiteit van A'dam.
- Wilson, D. H. and Atkeson, C. (2005). Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors. In Pervasive Computing, volume 3468, pages 62-79. Springer.
- Yordanova, K. and Kirste, T. (2016). Learning Models of Human Behaviour from Textual Instructions. In Proc. of ICAART 2016, Rome, Italy.
Paper Citation
in Harvard Style
Schröder M., Bader S., Krüger F. and Kirste T. (2016). Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 430-437. DOI: 10.5220/0005756804300437
in Bibtex Style
@conference{icaart16,
author={Max Schröder and Sebastian Bader and Frank Krüger and Thomas Kirste},
title={Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={430-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005756804300437},
isbn={978-989-758-172-4},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Reconstruction of Everyday Life Behaviour based on Noisy Sensor Data
SN - 978-989-758-172-4
AU - Schröder M.
AU - Bader S.
AU - Krüger F.
AU - Kirste T.
PY - 2016
SP - 430
EP - 437
DO - 10.5220/0005756804300437