CHI Xiaowen,NI Youcong,DU Xin,et al.Smartphone-based speed acquisition and evolutionary modeling for fitness running[J].CAAI Transactions on Intelligent Systems,2017,12(05):702-709.[doi:10.11992/tis.201706045]





Smartphone-based speed acquisition and evolutionary modeling for fitness running
池小文1 倪友聪1 杜欣1 叶鹏2 吴燕丹3
1. 福建师范大学 软件学院, 福建 福州 350117;
2. 武汉纺织大学 数学与计算机学院, 湖北 武汉 430200;
3. 福建师范大学 体育科学学院, 福建 福州 350117
CHI Xiaowen1 NI Youcong1 DU Xin1 YE Peng2 WU Yandan3
1. Faculty of Software, Fujian Normal University, Fuzhou 350117, China;
2. School of Mathematics and Computer, Wuhan Textile University, Wuhan 430200, China;
3. Physical Science School, Fujian Normal University, Fuzhou 350117, China
fitness runningsmart phonemedian filterthree-axis accelerationorientation sensorspeed acquisitionevolutionary modeling
Due to the low precision of speed acquisition and the limited search ability of modeling algorithms, it is difficult to obtain a highly precise fitness running model by applying the Brzostowski method. In this paper, we propose a speed acquisition and evolutionarymodeling method for fitness running that is based on the smart phone. First,we introduce a fitness running speed acquisition method based on multiple smartphone sensors and a median filter that can remove the impulse noise of three-axis acceleration signals.This noiseisgenerated by the intermittent gesture changes associated with the use of smartphones.In addition, this method can filter out the gravitational component in three-axis acceleration.Next, we designed an evolutionary modeling algorithm to enlarge the search space to obtain a better fitness running model. The experimental results show that the proposed approach can obtain more accurate speed and fitness running models than the Brzostowski’s approach.


[1] 颜庆.不同强度和时间有氧健身跑对体脂的影响[J].武汉体育学院学报, 2013, 47(10):54-58.YAN Qing.Effect of different intensity and duration of aerobic fitness exercise on body fat[J]. Journal of wuhan institute of physical education, 2013, 47(10):54-58.
[2] ALLMAN K. Five fitness apps worth the download[J]. LSJ:law society of NSW journal, 2017(31):57.
[3] RENAUDIN V, SUSI M, LACHAPELLE G. Step length estimation using handheld inertial sensors[J]. Sensors, 2012, 12(7):8507-8525.
[4] HO N H, TRUONG P H, JEONG G M. Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone[J]. Sensors, 2016, 16(9):1423.
[5] LI F, ZHAO C, DING G, et al. A reliable and accurate indoor localization method using phone inertial sensors[C]//Proceedings of the 2012 ACM Conference on Ubiquitous Computing. Pittsburgh, USA, 2012:421-430.
[6] 谢雨驼, 边耐政. Android手机端运动量检测的研究与应用[J]. 计算机应用与软件, 2012, 29(10):227-229.XIE Yutuo, BIAN Naizheng. RESEARCH and application of physical activity consumption detection on android smart phone[J]. Computer applications and software, 2012, 29(10):227-229.
[7] KWAPISZ J R, WEISS G M, MOORE S A. Activity recognition using cell phone accelerometers[J]. ACM sigkdd explorations newsletter, 2011, 12(2):74-82.
[8] BAI Y W, YU C H, WU S C. Using a three-axis accelerometer and GPS module in a smart phone to measure walking steps and distance[C]//2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). Toronto, Canada, 2014:1-6.
[9] 马松岩. 基于iOS平台的健身应用的设计与实现[D]. 北京:北京邮电大学, 2013.MA Songyan. The design and implementation of a pedometer application based on ios platform[D]. Beijing:Beijing University of Posts and Telecommunications, 2013.
[10] FASEL B, DUC C, DADASHI F, et al. A wrist sensor and algorithm to determine instantaneous walking cadence and speed in daily life walking[J]. Med biol eng comput, 2017, 55:1773-1785.
[11] NEVILLE J G, ROWLANDS D D, LEE J B, et al. A Model for comparing over-ground running speed and accelerometer derived step rate in elite level athletes[J]. IEEE sensors journal, 2016, 16(1):185-191.
[12] 赵寅, 徐国华, 杨超, 等. 基于模糊卡尔曼滤波算法的速度估算方法[J]. 仪表技术与传感器, 2013(12):80-83.ZHAO Yin, XU Guohua, YANG Chao, et al. Estimation of speed based on fuzzy-Kalman filter[J]. Instrument technique and sensor, 2013(12):80-83.
[13] KRANZ M, MÖLLER A, HAMMERLA N, et al. The mobile fitness coach:towards individualized skill assessment using personalized mobile devices[J]. Pervasive and mobile computing, 2013, 9(2):203-215.
[14] 周萍, 田磊. 有氧健身跑对大学生身体意象影响的研究[J]. 运动, 2016(21):53-54.ZHOU Ping, TIAN Lei. The Research about effect of aerobic fitness running on body intention[J]. Sports, 2016(21):53-54.
[15] SORNANATHAN L, KHALIL I. Fitness monitoring system based on heart rate and SpO2 level[C]//201010th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB). Kuantan, Malaysia, 2010:1-5.
[16] FUDGE B W, WILSON J, EASTON C, et al. Estimation of oxygen uptake during fast running using accelerometry and heart rate[J]. Medicine and science in sports and exercise, 2007, 39(1):192-198.
[17] 孙泊, 刘宇, 庄涛, 等. 基于腰部加速度计的行走能耗建模实验研究[J]. 体育科学, 2013(4):36-41.SUN Bo, LIU Yu, ZHUANG Tao, et al. The experimental research on modeling of walking energy expenditure based on the waist accelerometer[J]. China sport science, 2013(4):36-41.
[18] GARCIA-GARCIA F, GARCIA-SÁEZ G, CHAUSA P, et al. Statistical machine learning for automatic assessment of physical activity intensity using multi-axial accelerometry and heart rate[J].Lecture notes in computer science, 2011, 6747:70-79.
[19] CHENG T M, SAVKIN A V, CELLER B G, et al. Nonlinear modeling and control of human heart rate response during exercise with various work load intensities[J]. IEEE transactions on biomedical engineering, 2008, 55(11):2499-2508.
[20] SCALZI S, TOMEI P, VERRELLI C M. Nonlinear control techniques for the heart rate regulation in treadmill exercises[J]. IEEE transactions on biomedical engineering, 2012, 59(3):599-603.
[21] BRZOSTOWSKI K, DRAPA?A J, GRZECH A, et al. Adaptive decision support system for automatic physical effort plan generation-data-driven approach[J].Cybernetics and systems, 2013, 44(2/3):204-221.
[22] FUJIKI Y, TSIAMYRTZIS P, PAVLIDIS I. Making sense of accelerometer measurements in pervasive physical activity applications[C]//CHI’09 Extended Abstracts on Human Factors in Computing Systems. Boston, USA, 2009:3425-3430.
[23] KUO Y S, PANNUTO P, HSIAO K J, et al. Luxapose:Indoor positioning with mobile phones and visible light[C]//Proceedings of the 20th Annual International Conference on Mobile Computing and Networking.Maui, USA, 2014:447-458.
[24] SUN H, MCINTOSH S. Phone call detection based on smartphone sensor data[C]//International Conference on Cloud Computing and Security. Nanjing, China, 2016:284-295.
[25] SHI D, WANG R, WU Y, et al. A novel orientation-and location-independent activity recognition method[J]. Personal and ubiquitous computing, 2017, 21(3):1-15.
[26] WANG Z, ZHANG D. Progressive switching median filter for the removal of impulse noise from highly corrupted images[J]. IEEE transactions on circuits and systems Ⅱ:analog and digital signal processing, 1999, 46(1):78-80.
[27] FORSYTHE G E, MOLER C B, MALCOLM M A. Computer methods for mathematical computations[M]. Upper Saddle River:Prentice-Hall, 1977.
[28] Goulden K J. Effect sizes for research:a broad practical approach[J]. Lawrence erlbaum associates, 2005, 27(5):419-420.


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更新日期/Last Update: 2017-10-25