ABSTRACT
Physical activity monitoring has recently become an important field in wearable computing research. However, there is a lack of a commonly used, standard dataset and established benchmarking problems. In this work, a new dataset for physical activity monitoring --- recorded from 9 subjects, wearing 3 inertial measurement units and a heart rate monitor, and performing 18 different activities --- is created and made publicly available. Moreover, 4 classification problems are benchmarked on the dataset, using a standard data processing chain and 5 different classifiers. The benchmark shows the difficulty of the classification tasks and exposes some challenges, defined by e.g. a high number of activities and personalization.
- B. E. Ainsworth, W. L. Haskell, M. C. Whitt, M. L. Irwin, a. M. Swartz, S. J. Strath, W. L. O'Brien, D. R. Bassett, K. H. Schmitz, P. O. Emplaincourt, D. R. Jacobs, and a. S. Leon. Compendium of physical activities: an update of activity codes and MET intensities. Medicine and science in sports and exercise, 32(9):498--504, Sept. 2000.Google Scholar
- L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. In Proc. 2nd Int. Conf. Pervasive Comput, pages 1--17, 2004.Google ScholarCross Ref
- BM-innovations. http://www.bm-innovations.com.Google Scholar
- M. Ermes, J. Pärkkä, and L. Cluitmans. Advancing from offline to online activity recognition with wearable sensors. In 30th Annual International IEEE EMBS Conference, pages 4451--4454, Jan. 2008.Google ScholarCross Ref
- M. Ermes, J. Pärkkä, J. Mäntyjärvi, and I. Korhonen. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans. Inf. Technol. Biomed., 12(1):20--26, Jan. 2008. Google ScholarDigital Library
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA Data Mining Software: an Update. SIGKDD Explorations, 11(1), 2009. Google ScholarDigital Library
- T. Huynh and B. Schiele. Analyzing features for activity recognition. In sOc-EUSAI '05, pages 159--163. ACM Press, 2005. Google ScholarDigital Library
- S. Intille, K. Larson, E. Tapia, J. Beaudin, P. Kaushik, J. Nawyn, and R. Rockinson. Using a live-in laboratory for ubiquitous computing research. Proc. Int. Conf. on Pervasive Computing, pages 349--365, 2006. Google ScholarDigital Library
- P. Lukowicz, G. Pirkl, D. Bannach, F. Wagner, A. Calatroni, K. Förster, T. Holleczek, M. Rossi, D. Roggen, G. Tröster, and Others. Recording a complex, multi modal activity data set for context recognition. In 23rd International Conference on Architecture of Computing Systems (ARCS), pages 1--6. VDE, 2010.Google Scholar
- PAMAP (Physical Activity Monitoring for Aging People). http://www.pamap.org.Google Scholar
- J. Pärkkä, L. Cluitmans, and M. Ermes. Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree. IEEE Trans. Inf. Technol. Biomed., 14(5):1211--5, Sept. 2010. Google ScholarDigital Library
- J. Pärkkä, M. Ermes, K. Antila, M. van Gils, A. Mänttäri, and H. Nieminen. Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations. 29th Annual International IEEE EMBS Conference, pages 1511--4, 2007.Google ScholarCross Ref
- S. Patel, C. Mancinelli, P. Bonato, J. Healey, and M. Moy. Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance. In Body Sensor Networks, pages 236--241, 2009. Google ScholarDigital Library
- N. Ravi, N. Dandekar, P. Mysore, and M. Littman. Activity recognition from accelerometer data. In 17th Conference on Innovative Applications of Artificial Intelligence (IAAI), pages 1541--1546, 2005. Google ScholarDigital Library
- A. Reiss and D. Stricker. Introducing a Modular Activity Monitoring System. In 33rd Annual International IEEE EMBS Conference, pages 5621--5624, 2011.Google ScholarCross Ref
- A. Reiss and D. Stricker. Towards Global Aerobic Activity Monitoring. In 4th International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), 2011. Google ScholarDigital Library
- A. Reiss and D. Stricker. Introducing a New Benchmarked Dataset for Activity Monitoring. In 16th IEEE International Symposium on Wearable Computers (ISWC), 2012. Google ScholarDigital Library
- A. Reiss, M. Weber, and D. Stricker. Exploring and Extending the Boundaries of Physical Activity Recognition. In IEEE SMC Workshop on Robust Machine Learning Techniques for Human Activity Recognition, pages 46--50, 2011.Google Scholar
- D. Roggen, A. Calatroni, M. Rossi, T. Holleczek, K. Forster, G. Troster, P. Lukowicz, D. Bannach, G. Pirkl, A. Ferscha, and Others. Collecting complex activity datasets in highly rich networked sensor environments. In Seventh Int. Conf. on Networked Sensing Systems (INSS), pages 233--240. IEEE, 2010.Google ScholarCross Ref
- D. Roggen, S. Magnenat, M. Waibel, and G. Tröster. Wearable Computing: Designing and Sharing Activity Recognition Systems Across Platforms. IEEE Robotics & Automation Magazine, 18(2):83--95, 2011.Google ScholarCross Ref
- M. Saar-Tsechansky and F. Provost. Handling Missing Values when Applying Classification Models. Journal of Machine Learning Research, 8:1625--1657, 2007. Google ScholarDigital Library
- H. Sagha, S. T. Digumarti, R. Chavarriaga, A. Calatroni, D. Roggen, and G. Tr. Benchmarking classification techniques using the Opportunity human activity dataset. In IEEE SMC Workshop on Robust Machine Learning Techniques for Human Activity Recognition, pages 36--40, 2011.Google ScholarCross Ref
- Trivisio. http://www.trivisio.com.Google Scholar
- T. van Kasteren, H. Alemdar, and C. Ersoy. Effective Performance Metrics for Evaluating Activity Recognition Methods. In ARCS 2011 - 24th International Conference on Architecture of Computing Systems, 2011.Google Scholar
- T. van Kasteren, A. Noulas, G. Englebienne, and B. Kröse. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing (UbiComp), pages 1--9. ACM Press, 2008. Google ScholarDigital Library
- Viliv-S5. http://www.myviliv.com/ces/main_s5.html.Google Scholar
- J. A. Ward and H. W. Gellersen. Performance Metrics for Activity Recognition. ACM Transactions on Intelligent Systems and Technology, 2(1), 2011. Google ScholarDigital Library
- Y. Xue and L. Jin. A Naturalistic 3D Acceleration-based Activity Dataset & Benchmark Evaluations. In International Conference on Systems, Man and Cybernetics (SMC), pages 4081--4085, 2010.Google Scholar
Index Terms
- Creating and benchmarking a new dataset for physical activity monitoring
Recommendations
Towards global aerobic activity monitoring
PETRA '11: Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive EnvironmentsWith recent progress in wearable sensing it becomes reasonable for individuals to wear different sensors all day, thus global activity monitoring is establishing. The goals in global activity monitoring systems are amongst others to tell the type of ...
Aerobic activity monitoring: towards a long-term approach
With recent progress in wearable sensing, it becomes reasonable for individuals to wear different sensors all day, and thus, global activity monitoring is establishing. The goals in global activity monitoring systems are among others to tell the type of ...
An Integrated Mobile System for Long-Term Aerobic Activity Monitoring and Support in Daily Life
TRUSTCOM '12: Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and CommunicationsThis paper presents a mobile and unobtrusive platform that enables the accurate monitoring of physical activities in daily life, and is integrated into a healthcare system supporting out-of-hospital services. The main focus of the paper is to describe ...
Comments