Jennifer R. Kwapisz
Gary M. Weiss
Abstract
Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise.
- Anderson, I., Maitland, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., and Muller, H. 2007. Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones. In Mobile Networks and Applications. 12(2--3). Google Scholar
Digital Library
- Apple iPhone and Apple iPod Touch. 2009. Apple Inc. www.apple.com.Google Scholar
- Bao, L. and Intille, S. 2004. Activity Recognition from User-Annotated Acceleration Data. Lecture Notes Computer Science 3001, 1--17.Google ScholarCross Ref
- Brezmes, T., Gorricho, J.L., and Cotrina, J. 2009. Activity Recognition from accelerometer data on mobile phones. In IWANN '09: Proceedings of the 10th International Work-Conference on Artificial Neural Networks, 796--799. Google ScholarDigital Library
- Cho, Y., Nam, Y., Choi, Y-J., and Cho, W-D. 2008. Smart-Buckle: human activity recognition using a 3-axis accelerometer and a wearable camera. In HealthNet. Google ScholarDigital Library
- Choudhury, T., Consolvo, S., Harrison, B., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Borriello, G., Hemingway, B., Klasnja, P., Koscher, K., Landay, J., Lester, J., Wyatt, D., and Haehnel, D. 2008. The mobile sensing platform: An embedded activity recognition system. In IEEE Pervasive Computing, 7(2), 32--41. Google ScholarDigital Library
- Gyorbiro, N., Fabian, A., and Homanyi, G. 2008. An activity recognition system for mobile phones. In Mobile Networks and Applications, 14(1), 82--91. Google ScholarDigital Library
- Inooka, H., Ohtaki, Y. Hayasaka, H. Suzuki, A., and Nagatomi, R. 2006. Development of advanced portable device for daily physical assessment. In SICE-ICASE, International Joint Conference, 5878--5881.Google Scholar
- Krishnan, N., Colbry, D., Juillard, C., and Panchanathan, S. 2008. Real time human activity recognition using tri-Axial accelerometers. In Sensors, Signals and Information Processing Workshop.Google Scholar
- Krishnan, N. and Panchanathan, S. 2008. Analysis of Low Resolution Accelerometer Data for Continuous Human Activity Recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP 2008). Pages 3337--3340.Google Scholar
- Lester, J., Choudhury, T. and Borriello, G. 2006. A practical approach to recognizing physical activities. Lecture Notes in Computer Science: Pervasive Computing, 1--16. Google ScholarDigital Library
- Mathie, M., Celler B., Lovell N., and Coster A. 2004. Classification of basic daily movements using a triaxial accelerometer. In Medical & Biological Engineering and Computing, 42.Google Scholar
- Maurer, U., Smailagic, A., Siewiorek, D., & Deisher, M. 2006. Activity recognition and monitoring using multiple sensors on different body positions. In IEEE Proceedings on the International Workshop on Wearable and Implantable Sensor Networks, 3(5). Google ScholarDigital Library
- Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S., Zheng, X. and Campbell, A. 2008. Sensing meets mobile social networks: The design, implementation and evaluation of the CenceMe application. In The 6th ACM Conference on Embedded Networked Sensor Systems, 337--350. Google ScholarDigital Library
- Ravi, N., Dandekar, N. 2005. Activity recognition from accelerometer data. In Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence. Google ScholarDigital Library
- Tapia, E.M., Intille, S.S. et al. 2007. Real-Time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers, 1--4. Google ScholarDigital Library
- Unwired View.com. 2009. Google wants to make your Android phone much smarter with accelerometer and other sensors. Stasys Bielinis.http://www.unwiredview.com/2009/05/21/google-wants-to-make-your-android-phone-muchsmarter-with-accelerometer-and-other-sensors/Google Scholar
- Weiss, G. M., and Hirsh, H. 1998. Learning to predict rare events in event sequences, In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, 359--363.Google Scholar
- WISDM (Wireless Sensor Data Mining) Project. Fordham University, Department of Computer and Information Science, http://storm.cis.fordham.edu/~gweiss/wisdm/Google Scholar
- Witten, I. H. and Frank, E. Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, June 2005. Google ScholarDigital Library
- Yang, J. 2009. Toward physical activity diary: Motion recognition using simple acceleration features with mobile phones, In First International Workshop on Interactive Multimedia for Consumer Electronics at ACM Multimedia. Google ScholarDigital Library
- Long, X., Yin, B., and Aarts, R.M. 2009. Single accelerometer-based daily physical activity classification. In 31st Annual International Conference of the IEEE EMBS, 6107--6110.Google Scholar
- Mannini, A. and Sabatini A.M. 2010. Machine learning methods for classifying human physical activity from onbody accelerometers. In Sensors 2010, 10, 1154--1175.Google ScholarCross Ref
- Lee, M., Kim, J., Kim, K., Lee, I., Jee, S.H., and Yoo, S.K. 2009. Physical activity recognition using a single tri-axis accelerometer. In Proceedings of the World Congress on Engineering and Computer Science 2009, 1.Google Scholar
- Lee, S.-W. and Mase, K. 2002. Activity and location recognition using wearable sensors. In IEEE Pervasive Computing, 1(3):24--32. Google ScholarDigital Library
- Subramanya, A., Raj, A., Bilmes, J., and Fox, D. 2006. Recognizing activities and spatial context using wearable sensors. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence.Google Scholar
- Parkka, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., and Korhonen, I. 2006. Activity classification using realistic data from wearable sensors. In IEEE Transactions on Information Technology in Biomedicine, 10(1), 119--128. Google ScholarDigital Library
- Foerster F. and Fahrenberg J. 2000. Motion pattern and posture: correctly assessed by calibrated accelerometers. In Behavior Research Methods, Instruments, & Computers, 32(3), 450--7.Google ScholarCross Ref
Index Terms
- Activity recognition using cell phone accelerometers
Recommendations
Design considerations for the WISDM smart phone-based sensor mining architecture
SensorKDD '11: Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor DataSmart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), ...
Human activity recognition from sensor-based large-scale continuous monitoring of Parkinson's disease patients
CHASE '17: Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering TechnologiesSmartphone-based assessments have been considered a potential solution to passively monitor gait and mobility in early-stage Parkinson's disease (PD) patients. In the Multiple Ascending Dose clinical trial of PRX002/RG7935, 44 PD patients and 35 age- ...
Evaluation function of sensor position for activity recognition considering wearability
UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publicationIn the wearable computing environment, a computer provides many kinds of services by using the values from wearable sensors to recognize the user's movements or situations. In the research on activity recognition, accelerometers are attached on the user'...
Comments