Recognizing human activities from smartphone sensor signals

MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Volumn , Issue , 2014, Pages 865-868

  Ghosh, Arindam ^a   Riccardi, Giuseppe ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Human Activity Recognition; Signal fusion; Smartphone sensing

Indexed keywords

PATTERN RECOGNITION; SMARTPHONES;

ACTIVITY RECOGNITION; HUMAN ACTIVITIES; HUMAN ACTIVITY RECOGNITION; ON-BOARD SENSORS; SAMPLING RATES; SENSOR SIGNALS; SIGNAL FUSIONS; SMARTPHONE SENSING;

SIGNAL SAMPLING;

EID: 84913554811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2647868.2655034     Document Type: Conference Paper

References (17)

1. D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz. Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In Ambient Assisted Living and Home Care, pages 216-223. Springer, 2012.
2. V. Bellotti, B. Begole, E. H. Chi, N. Ducheneaut, J. Fang, E. Isaacs, T. King, M. W. Newman, K. Partridge, B. Price, et al. Activity-based serendipitous recommendations with the magitti mobile leisure guide. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1157-1166. ACM, 2008.
3. G. Bieber, A. Luthardt, C. Peter, and B. Urban. The hearing trousers pocket: activity recognition by alternative sensors. In Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments, page 44. ACM, 2011.
4. Y. Chon, E. Talipov, H. Shin, and H. Cha. Mobility prediction-based smartphone energy optimization for everyday location monitoring. In Proceedings of the 9th ACM conference on embedded networked sensor systems, pages 82-95. ACM, 2011.
5. S. Chu, S. Narayanan, and C.-C. Kuo. Environmental sound recognition with time-frequency audio features. Audio, Speech, and Language Processing, IEEE Transactions on, 17(6):1142-1158, 2009.
6. H. F. Durrant-Whyte. Sensor models and multisensor integration. The International Journal of Robotics Research, 7(6):97-113, 1988.
7. F. Eyben, M. Wöllmer, and B. Schuller. Opensmile: the munich versatile and fast open-source audio feature extractor. In Proceedings of the international conference on Multimedia, pages 1459-1462. ACM, 2010.
8. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10-18, 2009.
9. A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta, D. Siewiorek, A. Smailagic, M. Deisher, and U. Sengupta. Trading off prediction accuracy and power consumption for context-aware wearable computing. In Wearable Computers, 2005. Proceedings. Ninth IEEE International Symposium on, pages 20-26. IEEE, 2005.
10. H. Lu, W. Pan, N. D. Lane, T. Choudhury, and A. T. Campbell. Soundsense: scalable sound sensing for people-centric applications on mobile phones. In Proceedings of the 7th international conference on Mobile systems, applications, and services, pages 165-178. ACM, 2009.
11. U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher. Activity recognition and monitoring using multiple sensors on different body positions. In Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on, pages 4-pp. IEEE, 2006.
12. M. F. McKinney and J. Breebaart. Features for audio and music classification. In ISMIR, volume 3, pages 151-158, 2003.
13. G. Perolle, P. Fraisse, M. Mavros, and I. Etxeberria. Automatic fall detection and activity monitoring for elderly. Proceedings of MEDETEL, 2006.
14. A. Reiss, G. Hendeby, and D. Stricker. A competitive approach for human activity recognition on smartphones. In ESANN 2013, pages 455-460. ESANN, 2013.
15. B. Schuller, F. Pokorny, S. Ladstatter, M. Fellner, F. Graf, and L. Paletta. Acoustic geo-sensing: Recognising cyclists' route, route direction, and route progress from cell-phone audio. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 453-457. IEEE, 2013.
16. D. Weinland, R. Ronfard, and E. Boyer. A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding, 115(2):224-241, 2011.
17. C. Zhu and W. Sheng. Human daily activity recognition in robot-assisted living using multi-sensor fusion. In Robotics and Automation, 2009. ICRA'09. IEEE International Conference on, pages 2154-2159. IEEE, 2009.