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Open Access

SmartCare: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones

Hui LiuRui LiSicong LiuShibian TianJunzhao Du( )
School of Software and Institute of Software Engineering, Xidian University, and Science and Technology on Infomation Transmission and Dissemination in Communication Networks Lab., Xi’an 710126, China.
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Abstract

Lack of physical activity is becoming a killer of our healthy life. As a solution for this negative impact, we propose SmartCare to help users to set up a healthy physical activity habit. SmartCare can monitor a user’s activities over a long time, and then provide activity quality assessment and suggestion. SmartCare consists of three parts, activity recognition, energy saving, and health feedback. Activity recognition can recognize nine kinds of daily activities. A hybrid classifier that uses less power and memory with satisfactory accuracy was designed and implemented by utilizing the periodicity of target activity. In addition, a learning-based energy saver was introduced to reduce energy consumption by adjusting sampling rates and the set of features adaptively. Based on the type and duration of the activity recorded, health feedback in terms of the calorie burned was given. The system could provide quantitative activity quality assessment and recommend future physical activity plans. Through extensive real-life testing, the system is shown to achieve an average recognition accuracy of 98.0% with a minimized energy expenditure.

References

[1]
Lane N. D., Lin M., Mohammod M., Yang X., Lu H., Cardone G., Ali S., Doryab A., Berke E., T Campbell A., et al., Bewell: Sensing sleep, physical activities and social interactions to promote wellbeing, Mobile Networks and Applications, vol. 19, no. 3, pp. 345-359, 2014.
[2]
Gummeson J., Priyantha B., and Liu J., An energy harvesting wearable ring platform for gestureinput on surfaces, in Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2014, pp. 162-175.
[3]
Parate A., Chiu M.-C., Chadowitz C., Ganesan D., and Kalogerakis E., Risq: Recognizing smoking gestures with inertial sensors on a wristband, in Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2014, pp. 149-161.
[4]
Han M., Bang J. H., Nugent C., McClean S., and Lee S., A lightweight hierarchical activity recognition framework using smartphone sensors, Sensors, vol. 14, no. 9, pp. 16181-16195, 2014.
[5]
Ermes M., Parkka J., Mantyjarvi J., and Korhonen I., Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions, IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 1, pp. 20-26, 2008.
[6]
Suutala J., Pirttikangas S., and Röning J., Discriminative temporal smoothing for activity recognition from wearable sensors, in Ubiquitous Computing Systems. Springer, 2007, pp. 182-195.
[7]
Ha K., Chen Z., Hu W., Richter W., Pillai P., and Satyanarayanan M., Towards wearable cognitive assistance, in Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2014, pp. 68-81.
[8]
AppleWatch, http://www.apple.com/watch/, 2015.
[9]
Eric, fitbit, http://www.fitbit.com/, 2015.
[10]
Yan Z., Subbaraju V., Chakraborty D., Misra A., and Aberer K., Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach, in 16th International Symposium on Wearable Computers (ISWC), IEEE, 2012, pp. 17-24.
[11]
Sankaran K., Zhu M., Guo X. F., Ananda A. L., Chan M. C., and Peh L.-S., Using mobile phone barometer for low-power transportation context detection, in Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, ACM, 2014, pp. 191-205.
[12]
Lane N. D., Li P., Zhou L., and Zhao F.. Connecting personal-scale sensing and networked community behavior to infer human activities, in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, 2014, pp. 595-606.
[13]
Karjalainen S., Moves, http://www.moves-app.com/, 2015.
[14]
[15]
Lu H., Yang J., Liu Z., Lane N. D., Choudhury T., and Campbell A. T., The jigsaw continuous sensing engine for mobile phone applications, in Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, ACM, 2010, pp. 71-84.
[16]
Wang Y., Lin J., Annavaram M., Jacobson Q. A., Hong J., Krishnamachari B., and Sadeh N., A framework of energy efficient mobile sensing for automatic user state recognition, in Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, ACM, 2009, pp. 179-192.
[17]
Chu D., Lane N. D., Lai T. T.-T., Pang C., Meng X., Guo Q., Li F., and Zhao F., Balancing energy, latency and accuracy for mobile sensor data classification, in Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, ACM, 2011, pp. 54-67.
[18]
Tanasescu M., Leitzmann M. F., Rimm E. B., Willett W. C., Stampfer M. J., and Hu F. B., Exercise type and intensity in relation to coronary heart disease in men, Jama, vol. 288, no. 16, pp. 1994-2000, 2002.
[19]
Manson J. E., Greenland P., LaCroix A. Z., Stefanick M. L., Mouton C. P., Oberman A., Perri M. G., Sheps D. S., Pettinger M. B., and Siscovick D. S., Walking compared with vigorous exercise for the prevention of cardiovascular events in women, New England Journal of Medicine, vol. 347, no. 10, pp. 716-725, 2002.
[20]
Hakim A. A., Curb J. D., Petrovitch H., Rodriguez B. L., Yano K., Ross G. W., White L. R., and Abbott R. D., Effects of walking on coronary heart disease in elderly men the honolulu heart program, Circulation, vol. 100, no. 1, pp. 9-13, 1999.
[21]
Jeon C. Y., Lokken R. P., Hu F. B., and Van Dam R. M., Physical activity of moderate intensity and risk of type 2 diabetes a systematic review, Diabetes Care, vol. 30, no. 3, pp. 744-752, 2007.
[22]
Wishnofsky M., Caloric equivalents of gained or lost weight, The American Journal of Clinical Nutrition, vol. 6, no. 5, pp. 542-546, 1958.
[23]
Dickey R. A., Bartuska D. G., Bray G. W., Callaway C. W., Davidson E. T., Feld S., Ferraro R. T., Hodgson S. F., Jellinger P. S., Kennedy F. P., et al., Aace/ace position statement on the prevention, diagnosis, and treatment of obesity (1998 revision), Endocr. Pract., vol. 4, no. 5, pp. 297-350, 1998.
[24]
Incel O. D., Kose M., and Ersoy C., A review and taxonomy of activity recognition on mobile phones, BioNanoScience, vol. 3, no. 2, pp. 145-171, 2013.
[25]
Bao L. and Intille S. S., Activity recognition from user-annotated acceleration data, in Pervasive Computing. Springer, 2004, pp. 1-17.
[26]
Cornelius C., Peterson R., Skinner J., Halter R., and Kotz D., A wearable system that knows who wears it, in Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2014, pp. 55-67.
[27]
Mayberry A., Hu P., Marlin B., Salthouse C., and Ganesan D., ishadow: Design of a wearable, real-time mobile gaze tracker, in Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2014, pp. 82-94.
[30]
Anjum A. and Usman Ilyas M., Activity recognition using smartphone sensors, in Consumer Communications and Networking Conference (CCNC), 2013 IEEE, 2013, pp. 914-919.
[31]
Guiry J. J., van de Ven P., Nelson J., Warmerdam L., and Riper H., Activity recognition with smartphone support, Medical Engineering & Physics, vol. 36, no. 6, pp. 670-675, 2014.
[32]
Ju Y., Lee Y., Yu J., Min C., Shin I., and Song J., Symphoney: A coordinated sensing flow execution engine for concurrent mobile sensing applications, in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 2012, pp. 211-224.
[33]
Gordon D., Czerny J., Miyaki T., and Beigl M., Energy-efficient activity recognition using prediction, in 16th International Symposium on Wearable Computers (ISWC), IEEE, 2012, pp. 29-36.
[34]
Denning T., Andrew A., Chaudhri R., Hartung C., Lester J., Borriello G., and Duncan G., Balance: Towards a usable pervasive wellness application with accurate activity inference, in Proceedings of the 10th Workshop on Mobile Computing Systems and Applications, 2009, p. 5.
Tsinghua Science and Technology
Pages 348-363
Cite this article:
Liu H, Li R, Liu S, et al. SmartCare: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones. Tsinghua Science and Technology, 2015, 20(4): 348-363. https://doi.org/10.1109/TST.2015.7173451

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Received: 10 April 2015
Revised: 15 June 2015
Accepted: 29 June 2015
Published: 03 August 2015
© The authors 2015
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