ABSTRACT
Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.
- Are Personal Trainers Worth the Price. http://www.telegraph.co.uk/health/dietandfitness.html.Google Scholar
- Centers for Disease Control and Prevention. Physical activity recommendations for adults. cdc.gov/physicalactivity/everyone/guidelines/adults.html.Google Scholar
- Learn How to Slim and Strengthen Your Midsection with the Best Core Exercises. http://www.askthetrainer.com/best-core-exercises/.Google Scholar
- M. Azizyan, I. Constandache, and R. Roy Choudhury. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. In Proceedings of ACM MobiSys, 2009. Google ScholarDigital Library
- I. Bilik, J. Tabrikian, and A. Cohen. GMM-based Target Classification for Ground Surveillance Doppler Radar. IEEE Transactions on Aerospace and Electronic Systems, 42(1):267--278, 2006.Google ScholarCross Ref
- Dumbbells, Barbells, and Kettlebells. http://www.bodybuilding.com/fun/grapgym9.htm.Google Scholar
- A Simple Protocol for Testing Your Work Capacity. http://breakingmuscle.com/strength-conditioning/a-simple-protocol-for-testing-your-work-capacity.Google Scholar
- A. Bulling, U. Blanke, and B. Schiele. A Tutorial on Human Activity Recognition using Body-worn Inertial Sensors. ACM Computing Surveys, 46(3):33, 2014. Google ScholarDigital Library
- K.-H. Chang, M. Y. Chen, and J. Canny. Tracking Free-weight Exercises. In Proceedings of ACM UbiComp, 2007. Google ScholarDigital Library
- S. Consolvo, D. W. McDonald, T. Toscos, M. Y. Chen, J. Froehlich, B. Harrison, P. Klasnja, A. LaMarca, L. LeGrand, R. Libby, et al. Activity Sensing in the Wild: A Field Trial of Ubifit Garden. In Proceedings of ACM CHI, 2008. Google ScholarDigital Library
- S. B. Eisenman, E. Miluzzo, N. D. Lane, R. A. Peterson, G.-S. Ahn, and A. T. Campbell. Bikenet: A mobile sensing system for cyclist experience mapping. ACM Transactions on Sensor Networks, 6(1):6, 2009. Google ScholarDigital Library
- Fitbit. https://www.fitbit.com/.Google Scholar
- W. Gu, Z. Yang, L. Shangguan, W. Sun, K. Jin, and Y. Liu. Intelligent Sleep Stage Mining Service with Smartphones. In Proceedings of ACM UbiComp, 2014. Google ScholarDigital Library
- N. Y. Hammerla, T. Plötz, P. Andras, and P. Olivier. Assessing Motor Performance with PCA. In Proceedings of IWFAR, 2011.Google Scholar
- T. Hao, G. Xing, and G. Zhou. iSleep: Unobtrusive Sleep Quality Monitoring using Smartphones. In Proceedings of ACM MobiSys, 2013.Google ScholarDigital Library
- iOS 8 HealthKit. http://www.apple.com/ios/whats-new/health/.Google Scholar
- R. E. Kalman. A new Approach to Linear Filtering and Prediction Problems. Journal of Fluids Engineering, 82(1):35--45, 1960.Google ScholarCross Ref
- C. Kennedy-Armbruster and M. Yoke. Methods of Group Exercise Instruction. Human Kinetics, 2014.Google Scholar
- Y. Kim and H. Ling. Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine. IEEE Transactions on Geoscience and Remote Sensing, 47(5):1328--1337, 2009.Google ScholarCross Ref
- J. Kruger, H. M. Blanck, and C. Gillespie. Dietary and physical activity behaviors among adults successful at weight loss maintenance. International Journal of Behavioral Nutrition and Physical Activity, 3(1):17, 2006.Google ScholarCross Ref
- Y. Lee, C. Min, C. Hwang, J. Lee, I. Hwang, Y. Ju, C. Yoo, M. Moon, U. Lee, and J. Song. Sociophone: Everyday Face-to-face Interaction Monitoring Platform using Multi-phone Sensor Fusion. In Proceeding of ACM MobiSys, 2013. Google ScholarDigital Library
- H. Li, Y. Can, and P. S. Alanson. IDSense: A Human Object Interaction Detection System Based on Passive UHF RFID. In Proceedings of ACM CHI, 2015. Google ScholarDigital Library
- Z. Li, W. Chen, C. Li, M. Li, X.-y. Li, and Y. Liu. FLIGHT: Clock Calibration Using Fluorescent Lighting. In Proceedings of ACM MobiCom, 2012. Google ScholarDigital Library
- 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 ACM MobiSys, 2009. Google ScholarDigital Library
- H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell. The Jigsaw Continuous Sensing Engine for Mobile Phone Applications. In Proceedings of ACM SenSys, 2010. Google ScholarDigital Library
- S. Masatoshi and M. Kurato. Activity Recognition Using Radio Doppler Effect for Human Monitoring Service. Journal of Information Processing, 20(2):396--405, 2012.Google ScholarCross Ref
- P. Melgarejo, X. Zhang, P. Ramanathan, and D. Chu. Leveraging Directional Antenna Capabilities for Fine-grained Gesture Recognition. In Proceedings of ACM UbiComp, 2014. Google ScholarDigital Library
- D. Morris, T. S. Saponas, A. Guillory, and I. Kelner. RecoFit: Using a Wearable Sensor to Find, Recognize, and Count Repetitive Exercises. In Proceedings of ACM CHI, 2014. Google ScholarDigital Library
- A. Parate, M.-C. Chiu, C. Chadowitz, D. Ganesan, and E. Kalogerakis. RisQ: Recognizing Smoking Gestures with Inertial Sensors on A Wristband. In Proceedings of ACM MobiSys, 2014. Google ScholarDigital Library
- Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home Gesture Recognition using Wireless Signals. In Proceedings of ACM MobiCom, 2013. Google ScholarDigital Library
- T. Rahman, A. T. Adams, M. Zhang, E. Cherry, B. Zhou, H. Peng, and T. Choudhury. BodyBeat: A Mobile System for Sensing Non-speech Body Sounds. In Proceedings of ACM MobiSys, 2014. Google ScholarDigital Library
- L. G. Roberts. ALOHA Packet System with and Without Slots and Capture. ACM SIGCOMM Computer Communnications Review, 5(2):28--42, 1975. Google ScholarDigital Library
- Y. Rubner, C. Tomasi, and L. J. Guibas. The Earth Mover's Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 40(2):99--121, 2000. Google ScholarDigital Library
- R. A. L. S. Kullback. On Information and Sufficiency. Annals of Mathematical Statistics, 1951.Google ScholarCross Ref
- S. Salvador and P. Chan. Toward Accurate Dynamic Time Warping in Linear Time and Space. Intelligent Data Analysis, 11(5):561--580, 2007. Google ScholarDigital Library
- L. Shangguan, Z. Yang, Z. Zhou, X. Zheng, C. Wu, and Y. Liu. CrossNavi: Enabling Real-time Crossroad Navigation for the Blind with Commodity Phones. In Proceedings of ACM UbiComp, 2014. Google ScholarDigital Library
- A. Stove and S. Sykes. A Doppler-based Automatic Target Classifier for a Battlefield Surveillance Radar. In Proceedings of IEEE RADAR, 2002.Google Scholar
- D. A. Tesch, E. L. Berz, and F. P. Hessel. RFID Indoor Localization based on Doppler Effect. In Proceedings of IEEE ISQED, 2015.Google ScholarCross Ref
- How to measure exercise intensity. http://www.weightwatchers.com/util/art/index_art.aspx?tabnum=1&art_id=20971.Google Scholar
- J. Wang, D. Vasisht, and D. Katabi. RF-IDraw: Virtual Touch Screen in the Air Using RF Signals. In Proceedings of ACM SIGCOMM, 2014. Google ScholarDigital Library
- Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu. E-eyes: Device-free Location-oriented Activity Identification using Fine-grained WiFi Signatures. In Proceedings of ACM MobiCom, 2014. Google ScholarDigital Library
- C. Xu, S. Li, G. Liu, Y. Zhang, E. Miluzzo, Y.-F. Chen, J. Li, and B. Firner. Crowd++: Unsupervised Speaker Count with Smartphones. In Proceedings of ACM UbiComp, 2013. Google ScholarDigital Library
- A. Zhan, M. Chang, Y. Chen, and A. Terzis. Accurate caloric expenditure of bicyclists using cellphones. In Proceedings of ACM SenSys, 2012. Google ScholarDigital Library
Index Terms
- FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs
Recommendations
A system for recognizing activities of daily living using everyday objects
KES'10: Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IVThe population is quickly ageing. It is estimated that 25 % of the European population will be made up of people aged over 65 [9]. This ageing provokes that the government has to provide more resources to manage the elderly requirements. However, its ...
Using mid-range RFID for location based activity recognition
UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous ComputingDevelopment of smarthome home application depends on the ability to identify resident activity and track occupancy of rooms as people move within a residence. Existing solutions to home activity recognition are evaluated using controlled experiments and ...
RFID-based indoor location tracking to ensure the safety of the elderly in smart home environments
In recent years, the number of elderly people living alone has grown rapidly. This increases the need for indoor healthcare services that help elderly residents live a safe and independent life. There has been increasing interest in indoor ubiquitous ...
Comments