RunBuddy: a smartphone system for running rhythm monitoring

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.

[1]  D. Bramble,et al.  Running and breathing in mammals. , 1983, Science.

[2]  Graeme A. Wood,et al.  The entrainment of ventilation frequency to exercise rhythm , 2004, European Journal of Applied Physiology and Occupational Physiology.

[3]  S. Loring,et al.  Determinants of breathing frequency during walking. , 1990, Respiration physiology.

[4]  Feng Zhao,et al.  A reliable and accurate indoor localization method using phone inertial sensors , 2012, UbiComp.

[5]  A. Bührer,et al.  Running training and co-ordination between breathing and running rhythms during aerobic and anaerobic conditions in humans , 2004, European Journal of Applied Physiology and Occupational Physiology.

[6]  T. Stukel,et al.  Ventilatory responses and entrainment of breathing during rowing. , 1991, Medicine and science in sports and exercise.

[7]  Budd Coates,et al.  Runner's World Running on Air: The Revolutionary Way to Run Better by Breathing Smarter , 2013 .

[8]  F. Nuttall,et al.  Body Mass Index , 2020, Definitions.

[9]  Gaetano Borriello,et al.  Validated caloric expenditure estimation using a single body-worn sensor , 2009, UbiComp.

[10]  P. Bernasconi,et al.  Analysis of co‐ordination between breathing and exercise rhythms in man. , 1993, The Journal of physiology.

[11]  Sébastien Villard,et al.  Dynamic stability of locomotor respiratory coupling during cycling in humans , 2005, Neuroscience Letters.

[12]  William J. McDermott,et al.  Running training and adaptive strategies of locomotor-respiratory coordination , 2003, European Journal of Applied Physiology.

[13]  J A Dempsey,et al.  Oxygen cost of exercise hyperpnea: implications for performance. , 1992, Journal of applied physiology.

[14]  Zhigang Liu,et al.  The Jigsaw continuous sensing engine for mobile phone applications , 2010, SenSys '10.

[15]  A. Papoulis Signal Analysis , 1977 .

[16]  Marcus Chang,et al.  Accurate caloric expenditure of bicyclists using cellphones , 2012, SenSys '12.

[17]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[18]  Kenichi Yamazaki,et al.  Gait analyzer based on a cell phone with a single three-axis accelerometer , 2006, Mobile HCI.

[19]  Qiang Li,et al.  MusicalHeart: a hearty way of listening to music , 2012, SenSys '12.

[20]  Eric C. Larson,et al.  SpiroSmart: using a microphone to measure lung function on a mobile phone , 2012, UbiComp.

[21]  M. Anshel,et al.  Effect of music and rhythm on physical performance. , 1978, Research quarterly.

[22]  Keiichi Yasumoto,et al.  Estimating heart rate variation during walking with smartphone , 2013, UbiComp.

[23]  Brian P. Bailey,et al.  DJogger: a mobile dynamic music device , 2006, CHI Extended Abstracts.

[24]  J. Kohl,et al.  Effect of coupling the breathing- and cycling rhythms on oxygen uptake during bicycle ergometry , 2004, European Journal of Applied Physiology and Occupational Physiology.

[25]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[26]  D. Mahler,et al.  Locomotor-respiratory coupling develops in novice female rowers with training. , 1991, Medicine and science in sports and exercise.

[27]  David W. Mizell,et al.  Using gravity to estimate accelerometer orientation , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[28]  Benoît G. Bardy,et al.  Sound Stabilizes Locomotor-Respiratory Coupling and Reduces Energy Cost , 2012, PloS one.

[29]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[30]  Rodrigo de Oliveira,et al.  TripleBeat: enhancing exercise performance with persuasion , 2008, Mobile HCI.

[31]  J. Kohl,et al.  Relation between pedalling- and breathing rhythm , 2004, European Journal of Applied Physiology and Occupational Physiology.

[32]  Agata Brajdic,et al.  Walk detection and step counting on unconstrained smartphones , 2013, UbiComp.

[33]  B. Sporer,et al.  Entrainment of breathing in cyclists and non-cyclists during arm and leg exercise , 2007, Respiratory Physiology & Neurobiology.

[34]  C. Karageorghis,et al.  Psychological Effects of Music Tempi during Exercise , 2007, International journal of sports medicine.

[35]  Nuria Oliver,et al.  MPTrain: a mobile, music and physiology-based personal trainer , 2006, Mobile HCI.