基于步态触觉生物力学参数步频建模的运动能耗测评方法研究
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摘要
保持能量平衡是预防多种代谢综合症的基本前提,关于研究能耗的组成和应用也是当下的热点。目前国际公认的较精准能量消耗测评法有双标水法和间接热量测定方法。但是这两者都因为受到价格过高和使用不便等因素限制通常仅用于实验室测试。在大众健身中比较普及的能量测定方法有加速度计和计步器等,同时新兴的能量消耗测评系统的开发也是现在的研究热方向,比如利用心率还有步态参数等指标进行能量评估的系统。
研究目的:通过利用自主研发的数字化跑鞋测量走、跑运动中的压力和加速度参数推测出的步频值,将其与能量消耗相关联,同时综合青少年个体参数,为数字化跑鞋拟建立一种适用于普通青少年大众的体力活动能量消耗估测模型。为建立青少年步态触觉生物力学参数指标体系,研究多参量指标的低成本、便携式能量测量方法提供参考。
研究方法:选用11-14岁的上海青少年初中学生作为受试者。实验仪器主要有:K4b2心肺功能测试仪,可变速跑台,欧姆龙体重身体脂肪测量仪,自主研制的数字化鞋垫等。在实验开始时首先对仪器进行预热和校验,同时测量受试者的基本信息,包括身高体重体脂率腿长等参数,填写受试者知情协议书;然后帮助受试者佩戴仪器并讲解实验流程,帮助受试者上跑台。受试者先以2km/h的速度进行热身运动,接下来以3、4、5、6、7、8km/h的速度进行走、跑运动,其中要求3、4km/h为慢走,5、6km/h为快走,7、8km/h为慢跑运动,同时进行数据采集。
数据处理:步频参数来源于数字化鞋垫采集的压力和加速度信息,对相邻峰值之间时间进行积分计算得到,得到的步频数据和实际人工计数的步频差距甚微,准确度高且通过加速度和压力信息分别得到的步频计数也几乎一致。能耗数据为运动时候的采集的能耗代谢率平均值减去根据Harris-Benedict公式计算的静息代谢率再除以体重以标准化。数据采用SPSS17.0对数据进行分析。模型校验:受试者包括32名男生,32名女生,每人在6档速度下进行测试,剔除无意义数据之后,数据组N=353。从中随机抽取10%即n2=34作为验证样本量,剩余n1=319用于建立常模。最后将得到的常模对n2中的数据进行检验。
实验结果:
1.初中青少年的肥胖程度、腿长、年龄和性别方面对单位体重运动能耗代谢率无影响。
2.通过对比发现青少年步频参数和运动能量代谢率可以用线性关系来表示。
3.根据分层线性回归得到基于生物力学步态触觉参数所得步频值以及身体参数值与单位体重运动能耗代谢率关系模型为:
AEE=1.913*SF-2.079*BMI+10.755*Sex-4.211*Age-42.312 (其中AEE为每公斤体重每分钟消耗的能量,单位为Cal/min/kg;SF为步频,单位为Step/min;BMI为身体质量指数,单位为Kg/m2;Sex为性别,定义男生=1,女生=0;Age为年龄,单位为周岁)但是考虑到实际应用的简便度以及未来数字化鞋垫软件开发过程中的具体情况也可以采用仅以步频为单一自变量的的单位体重运动能耗代谢率模型: AEE=1.904*SF-134.386
(其中AEE为每公斤体重每分钟消耗的能量,单位为Cal/min/kg;SF为步频,单位为Step/min)
结论与展望:通过基于生物力学步态触觉参数得到的步频值可以准确的估测初中阶段青少年的运动能量代谢率。建立了初中阶段青少年步频以及身体参数针对自主研发生产的数字化鞋垫的单位体重运动能耗模型,模型R2值为0.754,验证准确度达到83%。此外本次研究表明初中青少年的肥胖程度、腿长、年龄和性别对单位体重的运动能量代谢率并无影响且青少年步频参数和运动能量代谢率可以用线性关系来表示。步态触觉参数作为新兴能耗评量标准有很大的发展前景。
The incidence of obesity and the corresponding incidence of metabolic syndrome around the world are rising, that may be related to the decrease of physical activities. The most accuracy methods to measure the energy expenditure are double labeled water method and indirect calorimetry method. However, both of them are too expensive and inconvenience to use in free-style movement. Currently, fitness methods on energy expenditure are popular, such as accelerometers and pedometers. At the same time, development of new methods is the hot point, for instance the system based on gait parameters and heart rate..
Objective: Research on the relationship between gait parameters such as step frequency measured with digital insole made by ourselves and energy expenditure measured by K4b2 system. Stride frequency during walking, jogging and other daily movement are in consider, then combined with information of individual physical feature, made the regression on gait parameters and energy expenditure of youth, and do some study for the further research of digital insole’s software and public promotion.
Research Methods: Taking 11-14 year-old students of middle school in Shanghai as subjects. Experimental apparatus: K4b2 system, treadmill, Omron body fat measuring equipment, Digital insole made by Hefei Institution of Intelligent and so on. In the beginning of the experiment, we could warm-up the instruments, and take the basic measurement of subjects, including height, weight, body fat, leg length and other parameters. Then help participants to wear equipment and explain the experimental procedure, help the subjects using the treadmill. Subjects were asked to take the warm up exercise with speed of 2km/h on treadmill, then they have to walk or run with the speed of 3,4,5,6,7,8 km/h, and we require 3,4,5,6 km/h as walking, 7,8 km/h for jogging exercise, and acquisitive the data. Data processing: step frequency is from the digitized pressure and accelerate information collected by the digital insole and adjacent the peak integration between the calculated times. The stride frequency data has highly accuracy. Energy expenditure data come from the K4b2 outcome and then detract the resting energy expenditure which accord to Harris-Benedict formula. Finally, removing some of the instability data and analyze data with SPSS17.0. Model validation: The subjects included 32 boys and 32 girls, both have 6 speed tests, remove invalid data, the total data N=353. Apart 10% randomly as n2 = 34 are used to validation the model, and the remaining n1 = 319 are used to make model.
Results:
1. Obesity, leg length, age and gender with the active energy expenditure/kg/min is no significant difference of junior high school youth.
2. The relationship between stride frequency and active energy expenditure has a linear relationship.
3. The regression model between body characters and stride frequency by digital insole from Hefei Institution of Intelligent and energy expenditure/body weight/time of junior school students: AEE = 1.913 * SF-2.079 * BMI +10.755 * Sex-4.211 * Age-42.312 (AEE is the energy expenditure per kilogram of body weight per minute, in units of Cal/min/kg; SF for the step frequency, in units of Step/min; BMI is body mass index, in unit Kg/m2; Sex is defined as male=1, female=0)
However consider of the actual, we also made another model only with step frequency and energy expenditure: AEE = 1.904 * SF-134.386
(AEE is the energy expenditure per kilogram of body weight per minute, in units of Cal/min/kg; SF for the step frequency, in units of Step/min)
Conclusions:
1) The regression model between body characters and stride frequency by digital insole from Hefei Institution of Intelligent and energy expenditure/body weight/time of junior school students are made (R2=0.754); 2) Obesity, leg length, age and gender with the active energy expenditure/kg/min is no significant difference of junior high school youth.; 3) Gait parameters as a new standard of energy assessment has great prospects for development.
研究目的:通过利用自主研发的数字化跑鞋测量走、跑运动中的压力和加速度参数推测出的步频值,将其与能量消耗相关联,同时综合青少年个体参数,为数字化跑鞋拟建立一种适用于普通青少年大众的体力活动能量消耗估测模型。为建立青少年步态触觉生物力学参数指标体系,研究多参量指标的低成本、便携式能量测量方法提供参考。
研究方法:选用11-14岁的上海青少年初中学生作为受试者。实验仪器主要有:K4b2心肺功能测试仪,可变速跑台,欧姆龙体重身体脂肪测量仪,自主研制的数字化鞋垫等。在实验开始时首先对仪器进行预热和校验,同时测量受试者的基本信息,包括身高体重体脂率腿长等参数,填写受试者知情协议书;然后帮助受试者佩戴仪器并讲解实验流程,帮助受试者上跑台。受试者先以2km/h的速度进行热身运动,接下来以3、4、5、6、7、8km/h的速度进行走、跑运动,其中要求3、4km/h为慢走,5、6km/h为快走,7、8km/h为慢跑运动,同时进行数据采集。
数据处理:步频参数来源于数字化鞋垫采集的压力和加速度信息,对相邻峰值之间时间进行积分计算得到,得到的步频数据和实际人工计数的步频差距甚微,准确度高且通过加速度和压力信息分别得到的步频计数也几乎一致。能耗数据为运动时候的采集的能耗代谢率平均值减去根据Harris-Benedict公式计算的静息代谢率再除以体重以标准化。数据采用SPSS17.0对数据进行分析。模型校验:受试者包括32名男生,32名女生,每人在6档速度下进行测试,剔除无意义数据之后,数据组N=353。从中随机抽取10%即n2=34作为验证样本量,剩余n1=319用于建立常模。最后将得到的常模对n2中的数据进行检验。
实验结果:
1.初中青少年的肥胖程度、腿长、年龄和性别方面对单位体重运动能耗代谢率无影响。
2.通过对比发现青少年步频参数和运动能量代谢率可以用线性关系来表示。
3.根据分层线性回归得到基于生物力学步态触觉参数所得步频值以及身体参数值与单位体重运动能耗代谢率关系模型为:
AEE=1.913*SF-2.079*BMI+10.755*Sex-4.211*Age-42.312 (其中AEE为每公斤体重每分钟消耗的能量,单位为Cal/min/kg;SF为步频,单位为Step/min;BMI为身体质量指数,单位为Kg/m2;Sex为性别,定义男生=1,女生=0;Age为年龄,单位为周岁)但是考虑到实际应用的简便度以及未来数字化鞋垫软件开发过程中的具体情况也可以采用仅以步频为单一自变量的的单位体重运动能耗代谢率模型: AEE=1.904*SF-134.386
(其中AEE为每公斤体重每分钟消耗的能量,单位为Cal/min/kg;SF为步频,单位为Step/min)
结论与展望:通过基于生物力学步态触觉参数得到的步频值可以准确的估测初中阶段青少年的运动能量代谢率。建立了初中阶段青少年步频以及身体参数针对自主研发生产的数字化鞋垫的单位体重运动能耗模型,模型R2值为0.754,验证准确度达到83%。此外本次研究表明初中青少年的肥胖程度、腿长、年龄和性别对单位体重的运动能量代谢率并无影响且青少年步频参数和运动能量代谢率可以用线性关系来表示。步态触觉参数作为新兴能耗评量标准有很大的发展前景。
The incidence of obesity and the corresponding incidence of metabolic syndrome around the world are rising, that may be related to the decrease of physical activities. The most accuracy methods to measure the energy expenditure are double labeled water method and indirect calorimetry method. However, both of them are too expensive and inconvenience to use in free-style movement. Currently, fitness methods on energy expenditure are popular, such as accelerometers and pedometers. At the same time, development of new methods is the hot point, for instance the system based on gait parameters and heart rate..
Objective: Research on the relationship between gait parameters such as step frequency measured with digital insole made by ourselves and energy expenditure measured by K4b2 system. Stride frequency during walking, jogging and other daily movement are in consider, then combined with information of individual physical feature, made the regression on gait parameters and energy expenditure of youth, and do some study for the further research of digital insole’s software and public promotion.
Research Methods: Taking 11-14 year-old students of middle school in Shanghai as subjects. Experimental apparatus: K4b2 system, treadmill, Omron body fat measuring equipment, Digital insole made by Hefei Institution of Intelligent and so on. In the beginning of the experiment, we could warm-up the instruments, and take the basic measurement of subjects, including height, weight, body fat, leg length and other parameters. Then help participants to wear equipment and explain the experimental procedure, help the subjects using the treadmill. Subjects were asked to take the warm up exercise with speed of 2km/h on treadmill, then they have to walk or run with the speed of 3,4,5,6,7,8 km/h, and we require 3,4,5,6 km/h as walking, 7,8 km/h for jogging exercise, and acquisitive the data. Data processing: step frequency is from the digitized pressure and accelerate information collected by the digital insole and adjacent the peak integration between the calculated times. The stride frequency data has highly accuracy. Energy expenditure data come from the K4b2 outcome and then detract the resting energy expenditure which accord to Harris-Benedict formula. Finally, removing some of the instability data and analyze data with SPSS17.0. Model validation: The subjects included 32 boys and 32 girls, both have 6 speed tests, remove invalid data, the total data N=353. Apart 10% randomly as n2 = 34 are used to validation the model, and the remaining n1 = 319 are used to make model.
Results:
1. Obesity, leg length, age and gender with the active energy expenditure/kg/min is no significant difference of junior high school youth.
2. The relationship between stride frequency and active energy expenditure has a linear relationship.
3. The regression model between body characters and stride frequency by digital insole from Hefei Institution of Intelligent and energy expenditure/body weight/time of junior school students: AEE = 1.913 * SF-2.079 * BMI +10.755 * Sex-4.211 * Age-42.312 (AEE is the energy expenditure per kilogram of body weight per minute, in units of Cal/min/kg; SF for the step frequency, in units of Step/min; BMI is body mass index, in unit Kg/m2; Sex is defined as male=1, female=0)
However consider of the actual, we also made another model only with step frequency and energy expenditure: AEE = 1.904 * SF-134.386
(AEE is the energy expenditure per kilogram of body weight per minute, in units of Cal/min/kg; SF for the step frequency, in units of Step/min)
Conclusions:
1) The regression model between body characters and stride frequency by digital insole from Hefei Institution of Intelligent and energy expenditure/body weight/time of junior school students are made (R2=0.754); 2) Obesity, leg length, age and gender with the active energy expenditure/kg/min is no significant difference of junior high school youth.; 3) Gait parameters as a new standard of energy assessment has great prospects for development.
引文
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[2]Schutz Y, Weinsier R, Hunter G. Assessment of free-living physical activity in humans: an overview of currently available and proposed new measures. Obes Res, 2001: 9, 368–379.
[3]Chen KY,Bassett DR Jr. The technology of accelerometry-based activity monitors: current and future. Med Sci Sports Exerc [J], 2005, 37(11): S490-500.
[4]冯建英.一种测定能量消耗的新技术——双标水法[J].中国运动医学杂志, 1996, 15(3), 203- 205.
[5]戴剑松,孙飙.体力活动测量方法综述.体育科学, 2005 (25): 9: 69- 75.
[6] U. S. Department of Health and Human Service. Physical Activity and Health: a Report of the Surgeon General [M]. Atlanta, GA: U. S. Department of Health and Human Service, Center for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 1996. 20-144.
[7]McLaughlin, J.E., G.A. King, E.T. Hawley, D.R. Bassett, Jr., and B.E.Ainsworth. Validation of the COSMED K4B2 Portable Metabolic System. [J]J Sports Med. 22:280-284, 2001.
[8]Duffield R, Dawson B, Pinnington HC and Wong P. Accuracy and reliability of a Cosmed K4b2 portable gas analysis system. Journal of Science, Medicine and Sport 7(1): 11-22, 2004.
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