research-article

FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs

Authors:

Longfei Shangguan,

Jizhong ZhaoAuthors Info & Claims

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

Pages 141 - 154

https://doi.org/10.1145/2809695.2809708

Published: 01 November 2015 Publication History

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.

References

[1]

Are Personal Trainers Worth the Price. http://www.telegraph.co.uk/health/dietandfitness.html.

[2]

Centers for Disease Control and Prevention. Physical activity recommendations for adults. cdc.gov/physicalactivity/everyone/guidelines/adults.html.

[3]

Learn How to Slim and Strengthen Your Midsection with the Best Core Exercises. http://www.askthetrainer.com/best-core-exercises/.

[4]

M. Azizyan, I. Constandache, and R. Roy Choudhury. SurroundSense: Mobile Phone Localization via Ambience Fingerprinting. In Proceedings of ACM MobiSys, 2009.

Digital Library

[5]

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.

[6]

Dumbbells, Barbells, and Kettlebells. http://www.bodybuilding.com/fun/grapgym9.htm.

[7]

A Simple Protocol for Testing Your Work Capacity. http://breakingmuscle.com/strength-conditioning/a-simple-protocol-for-testing-your-work-capacity.

[8]

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.

Digital Library

[9]

K.-H. Chang, M. Y. Chen, and J. Canny. Tracking Free-weight Exercises. In Proceedings of ACM UbiComp, 2007.

Digital Library

[10]

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.

Digital Library

[11]

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.

Digital Library

[12]

Fitbit. https://www.fitbit.com/.

[13]

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.

Digital Library

[14]

N. Y. Hammerla, T. Plötz, P. Andras, and P. Olivier. Assessing Motor Performance with PCA. In Proceedings of IWFAR, 2011.

[15]

T. Hao, G. Xing, and G. Zhou. iSleep: Unobtrusive Sleep Quality Monitoring using Smartphones. In Proceedings of ACM MobiSys, 2013.

Digital Library

[16]

iOS 8 HealthKit. http://www.apple.com/ios/whats-new/health/.

[17]

R. E. Kalman. A new Approach to Linear Filtering and Prediction Problems. Journal of Fluids Engineering, 82(1):35--45, 1960.

[18]

C. Kennedy-Armbruster and M. Yoke. Methods of Group Exercise Instruction. Human Kinetics, 2014.

[19]

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.

[20]

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.

[21]

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.

Digital Library

[22]

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.

Digital Library

[23]

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.

Digital Library

[24]

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.

Digital Library

[25]

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.

Digital Library

[26]

S. Masatoshi and M. Kurato. Activity Recognition Using Radio Doppler Effect for Human Monitoring Service. Journal of Information Processing, 20(2):396--405, 2012.

[27]

P. Melgarejo, X. Zhang, P. Ramanathan, and D. Chu. Leveraging Directional Antenna Capabilities for Fine-grained Gesture Recognition. In Proceedings of ACM UbiComp, 2014.

Digital Library

[28]

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.

Digital Library

[29]

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.

Digital Library

[30]

Q. Pu, S. Gupta, S. Gollakota, and S. Patel. Whole-home Gesture Recognition using Wireless Signals. In Proceedings of ACM MobiCom, 2013.

Digital Library

[31]

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.

Digital Library

[32]

L. G. Roberts. ALOHA Packet System with and Without Slots and Capture. ACM SIGCOMM Computer Communnications Review, 5(2):28--42, 1975.

Digital Library

[33]

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.

Digital Library

[34]

R. A. L. S. Kullback. On Information and Sufficiency. Annals of Mathematical Statistics, 1951.

[35]

S. Salvador and P. Chan. Toward Accurate Dynamic Time Warping in Linear Time and Space. Intelligent Data Analysis, 11(5):561--580, 2007.

Digital Library

[36]

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.

Digital Library

[37]

A. Stove and S. Sykes. A Doppler-based Automatic Target Classifier for a Battlefield Surveillance Radar. In Proceedings of IEEE RADAR, 2002.

[38]

D. A. Tesch, E. L. Berz, and F. P. Hessel. RFID Indoor Localization based on Doppler Effect. In Proceedings of IEEE ISQED, 2015.

[39]

How to measure exercise intensity. http://www.weightwatchers.com/util/art/index_art.aspx?tabnum=1&art_id=20971.

[40]

J. Wang, D. Vasisht, and D. Katabi. RF-IDraw: Virtual Touch Screen in the Air Using RF Signals. In Proceedings of ACM SIGCOMM, 2014.

Digital Library

[41]

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.

Digital Library

[42]

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.

Digital Library

[43]

A. Zhan, M. Chang, Y. Chen, and A. Terzis. Accurate caloric expenditure of bicyclists using cellphones. In Proceedings of ACM SenSys, 2012.

Digital Library

Cited By

Wang FLv YZhu MDing HHan J(2024)XRF55Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435438:1(1-34)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643543
Yang YChen THuang YGuo XShangguan L(2024)MAF: Exploring Mobile Acoustic Field for Hand-to-Face Gesture InteractionsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642437(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642437
Wang PJiang RGuo ZLiu C(2024)Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion ImagesACM Transactions on Sensor Networks10.1145/359261220:4(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3592612
Show More Cited By

Index Terms

FEMO: A Platform for Free-weight Exercise Monitoring with RFIDs
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering

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 IV

The 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 Computing

Development 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

Information & Contributors

Information

Published In

cover image ACM Conferences

SenSys '15: Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

November 2015

526 pages

ISBN:9781450336314

DOI:10.1145/2809695

General Chair:
Junehwa Song
KAIST, South Korea
,
Program Chairs:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Cecilia Mascolo
University of Cambridge, UK

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSFC
Specialized Research Fund for the Doctoral Program of Higher Education
National Basic Research Program of China (973 Program)
Natural Science Basic Research Plan in Shaanxi Province of China

Conference

SenSys '15

Sponsor:

SenSys '15: The 13th ACM Conference on Embedded Network Sensor Systems

November 1 - 4, 2015

Seoul, South Korea

Acceptance Rates

SenSys '15 Paper Acceptance Rate 27 of 132 submissions, 20%;

Overall Acceptance Rate 174 of 867 submissions, 20%

Upcoming Conference

SenSys '24

Sponsor:
sigbed
sigbed
sigbed
sigbed
sigbed

The 22nd ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2024

Hangzhou , China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

156
Total Citations
View Citations
1,155
Total Downloads

Downloads (Last 12 months)81
Downloads (Last 6 weeks)15

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wang FLv YZhu MDing HHan J(2024)XRF55Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435438:1(1-34)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643543
Yang YChen THuang YGuo XShangguan L(2024)MAF: Exploring Mobile Acoustic Field for Hand-to-Face Gesture InteractionsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642437(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642437
Wang PJiang RGuo ZLiu C(2024)Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion ImagesACM Transactions on Sensor Networks10.1145/359261220:4(1-24)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3592612
Kaur HRani VKumar M(2024)Human activity recognition: A comprehensive reviewExpert Systems10.1111/exsy.1368041:11Online publication date: 27-Jul-2024
https://doi.org/10.1111/exsy.13680
Chen YYu JKong LZhu YTang F(2024)Sensing Human Gait for Environment-Independent user Authentication using Commodity RFID DevicesIEEE Transactions on Mobile Computing10.1109/TMC.2023.3318753(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3318753
Ahmad IUllah AChoi W(2024)WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and OpportunitiesIEEE Open Journal of the Communications Society10.1109/OJCOMS.2024.34115295(3595-3623)Online publication date: 2024
https://doi.org/10.1109/OJCOMS.2024.3411529
Zhou SLu ZHan ZGuo LDeng JWen XYang XZhao J(2024)MuSense: Multiperson Continuous Activity Sensing Using Commodity Wi-FiIEEE Internet of Things Journal10.1109/JIOT.2023.334537611:8(15000-15021)Online publication date: 15-Apr-2024
https://doi.org/10.1109/JIOT.2023.3345376
Liu XLi FCao YZhai SYang SWang Y(2024)PPGSpotter: Personalized Free Weight Training Monitoring Using Wearable PPG SensorIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621212(2468-2477)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621212
Liu MChen YZheng HHuang LZhao KZhao Y(2024)Skeleton-Based Human Activities Fine-grained Recognition with RFID Technology2024 9th International Conference on Signal and Image Processing (ICSIP)10.1109/ICSIP61881.2024.10671507(6-12)Online publication date: 12-Jul-2024
https://doi.org/10.1109/ICSIP61881.2024.10671507
Hussain AAdeel Zahid MAhmed UNazeer SZafar KRauf Baig A(2024)Time-Series Data to Refined Insights: A Feature Engineering-Driven Approach to Gym Exercise RecognitionIEEE Access10.1109/ACCESS.2024.342830912(100343-100354)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3428309
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents