ABSTRACT
As one of the most popular exercises, running is accomplished through a tight cooperation between the respiratory and locomotor systems. Research has suggested that a proper running rhythm -- the coordination between breathing and strides -- helps improve exercise efficiency and postpone fatigue. This paper presents RunBuddy -- the first smartphone-based system for continuous running rhythm monitoring. RunBuddy is designed to be a convenient and unobtrusive exercise feedback system, and only utilizes commodity devices including smartphone and Bluetooth headset. A key challenge in designing RunBuddy is that the sound of breathing typically has very low intensity and is susceptible to interference. To reliably measure running rhythm, we propose a novel approach that integrates ambient sensing based on accelerometer and microphone, and a physiological model called Locomotor Respiratory Coupling (LRC), which indicates possible ratios between the stride and breathing frequencies. We evaluate RunBuddy through experiments involving 13 subjects and 39 runs. Our results show that, by leveraging the LRC model, RunBuddy correctly measures the running rhythm for indoor/outdoor running 92:7% of the time. Moreover, RunBuddy also provides detailed physiological profile of running that can help users better understand their running process and improve exercise self-efficacy.
- EA Aaron, KC Seow, BD Johnson, and JA Dempsey. 1992. Oxygen cost of exercise hyperpnea: implications for performance. Journal of Applied Physiology 72, 5 (1992), 1818--1825.Google ScholarCross Ref
- Mark H Anshel and Dan Q Marisi. 1978. Effect of music and rhythm on physical performance. Research Quarterly. American Alliance for Health, Physical Education and Recreation 49, 2 (1978), 109--113.Google Scholar
- P Bernasconi, P Bürki, A Bührer, EA Koller, and J Kohl. 1995. Running training and co-ordination between breathing and running rhythms during aerobic and anaerobic conditions in humans. European journal of applied physiology and occupational physiology 70, 5 (1995), 387--393.Google Scholar
- P Bernasconi and J Kohl. 1993. Analysis of co-ordination between breathing and exercise rhythms in man. The Journal of physiology 471, 1 (1993), 693--706.Google ScholarCross Ref
- Jacob T Biehl, Piotr D Adamczyk, and Brian P Bailey. 2006. Djogger: a mobile dynamic music device. In CHI'06 Extended Abstracts on Human Factors in Computing Systems. ACM, 556--561. Google ScholarDigital Library
- Body Mass Index 2004. (2004). http://apps.who.int/bmi.Google Scholar
- Agata Brajdic and Robert Harle. 2013. Walk detection and step counting on unconstrained smartphones. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, 225--234. Google ScholarDigital Library
- Dennis M Bramble and David R Carrier. 1983. Running and breathing in mammals. Science 219, 4582 (1983), 251--256.Google Scholar
- Budd Coates and Claire Kowalchik. 2013. Runner's World Running on Air: The Revolutionary Way to Run Better by Breathing Smarter. Rodale.Google Scholar
- Rodrigo De Oliveira and Nuria Oliver. 2008. TripleBeat: enhancing exercise performance with persuasion. In Proceedings of the 10th international conference on Human computer interaction with mobile devices and services. ACM, 255--264. Google ScholarDigital Library
- F Garlando, J Kohl, EA Koller, and P Pietsch. 1985. Effect of coupling the breathing-and cycling rhythms on oxygen uptake during bicycle ergometry. European journal of applied physiology and occupational physiology 54, 5 (1985), 497--501.Google Scholar
- Charles P Hoffmann, Gérald Torregrosa, and Benoît G Bardy. 2012. Sound stabilizes locomotor-respiratory coupling and reduces energy cost. PloS one 7, 9 (2012), e45206.Google ScholarCross Ref
- Toshiki Iso and Kenichi Yamazaki. 2006. Gait analyzer based on a cell phone with a single three-axis accelerometer. In Proceedings of the 8th conference on Human-computer interaction with mobile devices and services. ACM, 141--144. Google ScholarDigital Library
- Jabra Wave Mobile Bluetooth Headset 2015. (2015). http://www.jabra.com/products/bluetooth/jabra_wave/jabra_wave.Google Scholar
- CI Karageorghis, Leighton Jones, and DP Stuart. 2007. Psychological effects of music tempi during exercise. (2007).Google Scholar
- J Kohl, EA Koller, and M Jäger. 1981. Relation between pedalling-and breathing rhythm. European journal of applied physiology and occupational physiology 47, 3 (1981), 223--237.Google Scholar
- Eric C Larson, Mayank Goel, Gaetano Boriello, Sonya Heltshe, Margaret Rosenfeld, and Shwetak N Patel. 2012. Spirosmart: Using a microphone to measure lung function on a mobile phone. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 280--289. Google ScholarDigital Library
- Jonathan Lester, Carl Hartung, Laura Pina, Ryan Libby, Gaetano Borriello, and Glen Duncan. 2009. Validated caloric expenditure estimation using a single body-worn sensor. In Proceedings of the 11th international conference on Ubiquitous computing. ACM, 225--234. Google ScholarDigital Library
- LG Nexus 5 2015. (2015). https://www.google.com/nexus/5/.Google Scholar
- Fan Li, Chunshui Zhao, Guanzhong Ding, Jian Gong, Chenxing Liu, and Feng Zhao. 2012. A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing. ACM, 421--430. Google ScholarDigital Library
- Stephen H Loring, Jere Mead, and Thomas B Waggener. 1990. Determinants of breathing frequency during walking. Respiration physiology 82, 2 (1990), 177--188.Google Scholar
- Hong Lu, Jun Yang, Zhigang Liu, Nicholas D Lane, Tanzeem Choudhury, and Andrew T Campbell. 2010. The Jigsaw continuous sensing engine for mobile phone applications. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems. ACM, 71--84. Google ScholarDigital Library
- Donald A Mahler, Bernadette Hunter, Timothy Lentine, and Joseph Ward. 1991a. Locomotor-respiratory coupling develops in novice female rowers with training. Medicine and science in sports and exercise 23, 12 (1991), 1362--1366.Google Scholar
- Donald A Mahler, Christopher R Shuhart, Elizabeth Brew, and Therese A Stukel. 1991b. Ventilatory responses and entrainment of breathing during rowing. Medicine and science in sports and exercise 23, 2 (1991), 186--192.Google Scholar
- William J McDermott, Richard EA Van Emmerik, and Joseph Hamill. 2003. Running training and adaptive strategies of locomotor-respiratory coordination. European journal of applied physiology 89, 5 (2003), 435--444.Google Scholar
- David Mizell. 2003. Using gravity to estimate accelerometer orientation. In 2012 16th International Symposium on Wearable Computers. IEEE Computer Society, 252--252. Google ScholarDigital Library
- Motorola Moto G 2015. (2015). http://www.motorola.com/us/consumers/moto-g/Moto-G/moto-g-pdp.html.Google Scholar
- Nexus 4 2015. (2015). http://www.google.com/intl/ALL/nexus/4/.Google Scholar
- Shahriar Nirjon, Robert F Dickerson, Qiang Li, Philip Asare, John A Stankovic, Dezhi Hong, Ben Zhang, Xiaofan Jiang, Guobin Shen, and Feng Zhao. 2012. MusicalHeart: A hearty way of listening to music. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. ACM, 43--56. Google ScholarDigital Library
- Nuria Oliver and Fernando Flores-Mangas. 2006. MPTrain: a mobile, music and physiology-based personal trainer. In Proceedings of the 8th conference on Human-computer interaction with mobile devices and services. ACM, 21--28. Google ScholarDigital Library
- Oxycon Mobile 2015. (2015). http://www.carefusion.com.Google Scholar
- Jiapu Pan and Willis J Tompkins. 1985. A real-time QRS detection algorithm. Biomedical Engineering, IEEE Transactions on 3 (1985), 230--236.Google ScholarCross Ref
- David J Paterson, Graeme A Wood, Alan R Morton, and John D Henstridge. 1986. The entrainment of ventilation frequency to exercise rhythm. European journal of applied physiology and occupational physiology 55, 5 (1986), 530--537.Google Scholar
- Rangaraj M Rangayyan. 2002. Biomedical signal analysis. IEEE press New York.Google Scholar
- Samsung Galaxy Nexus 2015. (2015). http://www.samsung.com/us/mobile/cell-phones/SPH-L700ZKASPR.Google Scholar
- Ben C Sporer, Glen E Foster, A William Sheel, and Donald C McKenzie. 2007. Entrainment of breathing in cyclists and non-cyclists during arm and leg exercise. Respiratory physiology & neurobiology 155, 1 (2007), 64--70.Google Scholar
- Mayu Sumida, Teruhiro Mizumoto, and Keiichi Yasumoto. 2013. Estimating heart rate variation during walking with smartphone. In Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM, 245--254. Google ScholarDigital Library
- Sébastien Villard, Jean-François Casties, and Denis Mottet. 2005. Dynamic stability of locomotor respiratory coupling during cycling in humans. Neuroscience letters 383, 3 (2005), 333--338.Google Scholar
- Voyager Legend Mobile Bluetooth Headset 2015. (2015). http://www.plantronics.com/us/product/voyager-legend.Google Scholar
- Voyager Pro HD Mobile Bluetooth Headset 2015. (2015). http://www.plantronics.com/us/product/voyager-pro-hd.Google Scholar
- Andong Zhan, Marcus Chang, Yin Chen, and Andreas Terzis. 2012. Accurate caloric expenditure of bicyclists using cellphones. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems. ACM, 71--84. Google ScholarDigital Library
Index Terms
- RunBuddy: a smartphone system for running rhythm monitoring
Recommendations
Detecting breathing frequency and maintaining a proper running rhythm
AbstractRunning is a kind of whole body movement, which enables the whole body muscle rhythmic contraction and relaxation. A stable and harmonic running rhythm cannot only postpone runners’ fatigue but also improve their exercise ...
Classifying obstructive sleep apnea using smartphones
Display Omitted We propose a smartphone application that diagnose Obstructive Sleep Apnea.The design takes advantage of a smatrphone's built-in sensors to screen OSA.We examine the system's ability to screen OSA as compared to the golden standard. ...
Detecting repackaged smartphone applications in third-party android marketplaces
CODASPY '12: Proceedings of the second ACM conference on Data and Application Security and PrivacyRecent years have witnessed incredible popularity and adoption of smartphones and mobile devices, which is accompanied by large amount and wide variety of feature-rich smartphone applications. These smartphone applications (or apps), typically organized ...
Comments