Nhận dạng hoạt động của người bằng điện thoại thông minh

  • Nguyễn Thắng Ngọc
  • Phạm Văn Cường

Abstract

In this paper we propose a method and system for recognizing everyday human activities by utilizing the acceleration sensing data from the accelerometer instrumented in smart phones. In our method, human activities are recognized in four steps: data processing, data segmentation, feature extraction, and classification. We rigorously experimented on a dataset consisting of 6 everyday activities collected from 17 users using several machine learning algorithms, including support vector machine, Naïve Bayesian networks, k-Nearest Neighbors, Decision Tree C4.5, Rule Induction, and Neutral networks. The best accuracies are achieved by Decision Tree C4.5 that demonstrates the human activities can be distinguished with 82% precision and 83% recall under the leave-one-subject out evaluation protocol. These results have shown the feasibility of smart phone based real-time activity recognition. In addition, the proposed method based on Decision Tree has been deployed on Samsung smart phones and is able to recognize 6 human activities in real-time.

References

V. OSMANI, S. BALASUBRAMANIAM, D. BOTVICH, “Human Activity Recognition in Pervasive Health-care: Supporting Efficient Remote Collaboration”, Journal of Network and Computer Applications (Elsevier), vol. 31, no. 4, pp. 628-655, 2008.

C.PHAM, D. JACKSON, J. SCHONING, T. BAR., T.PLOETZ, P.OLIVIER “FoodBoard: Surface Contact Imaging for Food Recognition”, In Proc. of the 15th ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 749-752, 2013.

F. ALBINALI, S. S. INTILLE, W. HASKELL, M. ROSENBERGER “Using Wearable Activity Type Detection to Improve Physical Activity Energy Expenditure Estimation”, In Proc. of the 12th ACM International Conference on Ubiquitous Computing (UbiComp), pp. 311-320, 2010.

S. S. INTILLE, J. NAWYN, B. LOGAN, G. D. ABOWD “Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research”, In Proc. of the ACM International Conference on Computer and Human Factors (CHI) Extended Abstracts, pp. 4763-4766, 2009.

Digital Trends: http://www.digitaltrends.com/mobile/ mobile-phone-world-population-2014/#!Cs565

K. R. JENIFER, G. M. WEISS, S. MOORE “Activity Recognition Using Cell Phone Accelerometers”, SIGKDD Explorations, vol. 12, no. 2, pp. 74-82, 2010.

D. ANGUITA, A. GHIO, L. ONETO, X. PARRA, J. R. ORTIZ, “Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine”, In Proc. of the 4th International Workshop on Ambient Assisted Living, pp. 216-223, 2012.

L. Bao, S. S. INTILLE, “Activity Recognition from User-Annotated Acceleration Data”, In Proc. of the 2nd International Conference on Pervasive Computing (Pervasive’2004), pp. 1-17, 2004.

N. RAVI, N. DANDEKAR, P. MYSORE, M. L. LITTMAN, “Activity Recognition from Accelerometer Data”, In Proc. of the 17th Conference on Innovative Applications of Artificial Intelligence (IAAI), pp. 1541-1546, 2005.

T. HUYNH, U. BLANKE, B. SCHIELE, “Scalable Recognition of Daily Activities with Wearable Sensors”, In Proc. of the 3rd International Conference on Location-and Context-Awareness (LoCA), pp. 50-67, 2005.

J. WU, A OSUNTOGUN, T. CHOUDHURY, M. PHILIPOSE, J. M. REHG, “A Scalable Approach to Activity Recognition based on Object Use”, In Proc. of the 11th International Conference on Computer vision (ICCV), pp. 1-8, 2007.

T. V. DUONG, H. H. BUI, P. Q.DINH, S. VENKATESH, “Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model”, In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 838-845, 2005

C. PHAM, P. OLIVIER, “Slice&Dice: Recognizing Food Preparation Activities using Embedded Accelerometers”, In Proc. of European Conference on Ambient Intelligence (AmI), pp. 34—43, 2009.

M. BUETTNER, R. PRASAD, Ma. PHILIPOSE, D. WETHERER, “Recognizing Daily Activities with RFID-based Sensors”, In Proc. of the 11th International Conference on Ubiquitous Computing (UbiComp), pp. 51-60, 2009.

E. M. TAPIA, S. S. INTILLE, K. LARSON, “Activity Recognition in the Home Using Simple and Ubiquitous Sensors”, In Proc. of the 2nd International Conference on Pervasive Computing (Pervasive), pp. 158-175, 2004.

C.J. HOOPER, A. PRESTON, M. BALAAM, P. SEEDHOUSE, D. JACKSON, C. PHAM, C. LADHA, K. LADHA, T. PLOETZ, P. OLIVIER, “The French Kitchen: Task-Based Learning in an Instrumented Kitchen”, In Proc. of the 14th ACM International Conference on Ubiquitous Computing (UbiComp) pp. 193-202, 2012.

T. V. KASTEREN, A. K. NOULAS, G. ENGLEBIENNE, B. J. A. KROSE, “Accurate Activity Recognition in a Home Setting”, In Proc. of the 10th international conference on Ubiquitous Computing (UbiComp), pp. 1-9, 2008.

C. PHAM, D. N. NGUYEN, P. M. TU, “A Wearable Sensor based Approach to Real-Time Fall Detection and Fine-Grained Activity Recognition”, Journal of Mobile Multimedia, vol. 9, no. 1&2, pp. 15-26, 2013.

T. LYCHE, L.L. SCHUMAKER, "On the Convergence of Cubic Interpolating Splines" A. Meir (ed.) A. Sharma (ed.) Spline Functions and Approximation Theory, Birkhäuser (1973) pp. 169–189

T. V. KASTEREN, G. ENGLEBIENNE, B. J. A. KROSE, “Hierarchical Activity Recognition Using Automatically Clustered Actions”, In Proc. of European Conference on Ambient Intelligence (AmI), pp. 82-91, 2011.

I. WITTEN, E. FRANK, M. A. HALL, “Data Mining: Practical Machine Learning Tools and Techniques” Morgan Kaufmann (2011).

B. A. SSHENOI, “Introduction to Digital Signal Processing and Filter Design”, John Wiley and Sons, 2006.

S. I. AHAMED, M. RAHMAN, R. O. SMITH, M. KHAN, "A Feature Extraction Method for Realtime Human Activity Recognition on Cell Phones," in Proc. of International Symposium on Quality of Life Technologies, 2011.

SVMlib: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

RapidMiner: http://rapidminer.com/

G. SAEED, “Fundamentals of Probability”, Prentice Hall: New Jersey, 2000.

C. PHAM, C. HOOPER, S. LINDSAY, D. JACKSON, J. SHEARER, J. WAGNER, C. LADHA, K. LADHA, T. PLOETZ, P. OLIVIER, “The Ambient Kitchen: A Pervasive Sensing Environment for Situated Services,” in Proc. of ACM Conference on Designing Interactive Systems (DIS), 2012.

Published
2015-12-31
Section
Bài báo