WO2017075953A1 - 一种基于加速度传感器预测运动过程心率的方法及装置 - Google Patents

一种基于加速度传感器预测运动过程心率的方法及装置 Download PDF

Info

Publication number
WO2017075953A1
WO2017075953A1 PCT/CN2016/081427 CN2016081427W WO2017075953A1 WO 2017075953 A1 WO2017075953 A1 WO 2017075953A1 CN 2016081427 W CN2016081427 W CN 2016081427W WO 2017075953 A1 WO2017075953 A1 WO 2017075953A1
Authority
WO
WIPO (PCT)
Prior art keywords
heart rate
value
energy consumption
max
acceleration sensor
Prior art date
Application number
PCT/CN2016/081427
Other languages
English (en)
French (fr)
Inventor
李永旭
马自强
王建鹏
陈文武
肖子玉
田言金
Original Assignee
深圳风景网络科技有限公司
李永旭
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 深圳风景网络科技有限公司, 李永旭 filed Critical 深圳风景网络科技有限公司
Publication of WO2017075953A1 publication Critical patent/WO2017075953A1/zh

Links

Images

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate

Definitions

  • the invention relates to a method for measuring heart rate of an acceleration sensor, in particular to a method for predicting a heart rate of a motion process based on an acceleration sensor, and relates to a device using the method for predicting a heart rate of a motion process based on an acceleration sensor.
  • the technical problem to be solved by the present invention is to provide a method for predicting the heart rate of a subject under motion by an acceleration sensor, and to provide a device using the method for predicting a heart rate of a motion process based on the acceleration sensor.
  • the present invention provides a method for predicting a heart rate during a motion process based on an acceleration sensor, comprising the steps of:
  • Step S1 collecting and calculating an acceleration vector generated by the object under motion by the acceleration sensor
  • Step S2 by analyzing the change of the acceleration vector value on the time axis, calculating the change of the energy consumption of the measured object during the movement, and establishing a relationship model between the acceleration vector and the energy consumption;
  • Step S3 collecting basic information of the test object, and calculating the basal metabolic rate, the maximum heart rate, the oxygen uptake under the maximum heart rate, and the energy consumption under the maximum heart rate by the calculation;
  • Step S4 establishing a relationship model between the center rate and the energy consumption of the motion object of the measured object, and utilizing the change of energy consumption Calculate the change in heart rate and predict the heart rate of the subject during exercise.
  • step S1 is to collect the voltage signal generated by the object under test by the multi-axis acceleration sensor, thereby obtaining the acceleration vector of the multi-axis acceleration sensor in various directions, and the acceleration vector in each direction.
  • the acceleration combined vector is calculated.
  • step S1 comprises the following substeps:
  • Step S101 obtaining an acceleration vector of the measured object in n directions at time t 2 by using an n-axis acceleration sensor And calculate the acceleration vector at time t 2
  • Step S102 the acquisition time t is 1, the target object by an acceleration vector in the direction of n by n-axis acceleration sensor And calculate the acceleration vector at time t 1
  • step S2 comprises the following substeps:
  • Step S201 let t 2 > t 1 , according to the time from t 1 to t 2 , the acceleration combined vector Transform to Then establish a relationship model between the acceleration vector and the energy consumption as: Where k 1 is a constant coefficient ranging from 0.005 to 0.010; ⁇ x is a change in energy consumption of the object under motion during the time from t 1 to t 2 ;
  • Step S202 collecting the energy consumption x 0 of the measured object at time t 1 , and then obtaining the energy consumption x of the measured object at time t 2 is:
  • a further improvement of the invention is that the value of k 1 is 0.007.
  • step S3 comprises the following substeps:
  • Step S301 calculating the basal metabolic rate BMR of the test subject according to the sex, weight, height and age of the test subject;
  • Step S302 calculating a maximum heart rate y max of the test object
  • Step S303 measuring the resting heart rate y rest of the subject under quiet, obtaining the oxygen uptake VO 2max at the maximum heart rate per unit volume by the maximum heart rate y max and the resting heart rate y rest ;
  • Step S304 the energy consumption x max of the subject under the maximum heart rate is obtained by the oxygen uptake amount VO 2max at the maximum heart rate.
  • the ages, ⁇ 1 , ⁇ 2 , ⁇ 3 , ⁇ 4 , ⁇ 1 , ⁇ 2 , ⁇ 3 and ⁇ 4 are preset constant coefficients, and the ⁇ 1 ranges from 50 to 80, and the ⁇ 2
  • the value ranges from 10 to 20, the range of ⁇ 3 ranges from 1 to 10, the range of ⁇ 4 ranges from -10 to 0, and the range of ⁇ 1 ranges from 500 to 700.
  • the value of ⁇ 2 ranges from 5 to 15, the range of ⁇ 3 ranges
  • step S303 the oxygen uptake VO 2max at the maximum heart rate per unit volume is obtained by the maximum heart rate y max and the resting heart rate y rest :
  • VO 2max k 4 *weight*y max /y rest , where k 4 is taken The value ranges from 0.01 to 0.03;
  • step S4 comprises the following substeps:
  • Step S402 measuring an acceleration vector of the measured object at a certain moment during the movement by the acceleration sensor, and converting it into an energy consumption x max under the maximum heart rate, and then substituting the value of the energy consumption x max at the maximum heart rate into the
  • the heart rate prediction value y corresponding to the time is obtained by describing the formula of step S401.
  • a further improvement of the present invention is that, in the step S301, the value of ⁇ 1 is 65, the value of ⁇ 2 is 13.73, the value of ⁇ 3 is 5, and the value of ⁇ 4 is -6.9, and the value of ⁇ 1 is taken.
  • the value is 660, the value of ⁇ 2 is 9.6, the value of ⁇ 3 is 1.72, and the value of ⁇ 4 is -4.7.
  • the value of k 2 is 210, and the value of k 3 is 0.7;
  • the value of k 4 is 0.015; in the step S304, the value of k 5 is 20.5; in the step S401, the value of k 6 is 0.0029, and the value of k 7 is 0.035.
  • the present invention also provides an apparatus for predicting a heart rate of a motion process based on an acceleration sensor, which employs a method of predicting a heart rate of a motion process based on an acceleration sensor as described above.
  • the invention has the beneficial effects that the heart rate of the object under test can be predicted by the acceleration sensor, which is simple and easy to operate, and the measurement accuracy can meet the training requirement; more specifically, the invention proposes to adopt the acceleration
  • the sensor realizes the method of predicting the heart rate of the measured object during the movement process, solves the problem that the traditional heart rate measurement method requires professional operation, equipment and can not be portable, and expands the application of heart rate prediction in various fields such as exercise training and health monitoring.
  • the invention can only predict the heart rate of the object under test by using the acceleration sensor, is simple and easy to operate, and the measurement accuracy satisfies the requirements of ordinary physical training, especially for the elderly or special people with heart rate health problems, the meaning is more major.
  • FIG. 1 is a schematic diagram showing the structure of a workflow according to an embodiment of the present invention.
  • this example provides a method for predicting a heart rate during a motion process based on an acceleration sensor, including the following steps:
  • Step S1 collecting and calculating an acceleration vector generated by the object under motion by the acceleration sensor
  • Step S2 by analyzing the change of the acceleration vector value on the time axis, calculating the change of the energy consumption of the measured object during the movement, and establishing a relationship model between the acceleration vector and the energy consumption;
  • Step S3 collecting basic information of the test object, and calculating the basal metabolic rate, the maximum heart rate, the oxygen uptake under the maximum heart rate, and the energy consumption under the maximum heart rate by the calculation;
  • step S4 a relationship model between the center rate and the energy consumption of the motion object of the test object is established, and the change of the heart rate is calculated by using the change of the energy consumption to realize the prediction of the heart rate of the test object during the exercise process.
  • the heart rate value of the object during exercise is calculated by the acceleration sensor according to the human body oxygen consumption rate and the basal metabolic rate, and the acceleration sensor used can sense and collect the acceleration vector generated by the object during the movement, and the acceleration sensor mainly includes the piezoelectric element.
  • the result of the acquisition and calculation is used as the acceleration basic data in the motion process.
  • the acceleration basic data acquisition and calculation can be realized by real-time data acquisition, average data, indirect discrete acquisition data, continuous acquisition data or more.
  • This example utilizes measurable, known basic information of the subject, the basic information of the subject includes resting heart rate, gender, age, height and weight, and the resting heart rate is in a quiet state of the subject.
  • Heart rate and then calculate the maximum heart rate of different individuals, the oxygen consumption at the maximum heart rate, and the basal metabolic rate, and then use the oxygen consumption at the maximum heart rate to determine the energy consumption at the maximum heart rate, which is the energy consumption.
  • the acceleration vector of the measured object changes, the value of the acceleration after a certain time is recorded, and the change of the energy consumption caused by the change of the acceleration during this period is calculated, and the energy consumption at the starting time is added. It is the new energy consumption after this period of time; the energy consumption under the maximum heart rate and the newly calculated new energy consumption and the basal metabolic rate are substituted into the calculation model between the established center rate and energy of the motion process, and then can be predicted. Heart rate during exercise.
  • the step S1 in the present example is to collect the voltage signals generated by the multi-axis acceleration sensor during the motion by the multi-axis acceleration sensor, and then obtain the acceleration vector of the multi-axis acceleration sensor in each direction, and calculate the acceleration vector in each direction.
  • Acceleration vector The various directions refer to the direction of the multi-axis acceleration sensor.
  • the three-axis acceleration sensor includes acceleration vectors of three directions of front, back, horizontal and vertical.
  • Step S1 in this example uses an acceleration sensor to collect a voltage signal or other form of signal generated during the motion of the object under test, and converts the degree of motion into a value of the acceleration vector, which is calculated from the acceleration vectors in n directions. Acceleration vector; said step S1 comprises the following sub-steps:
  • Step S101 obtaining an acceleration vector of the measured object in n directions at time t 2 by using an n-axis acceleration sensor And calculate the acceleration vector at time t 2
  • Step S102 the acquisition time t is 1, the target object by the acceleration vector in the direction of n by n-axis acceleration sensor And calculate the acceleration vector at time t 1
  • step S2 of the example the change of the acceleration combined vector obtained in step S1 is used to calculate the change of the energy consumption of the measured object during the movement, and the relationship model between the acceleration combined vector and the energy consumption is established, and the change of the acceleration combined vector is obtained. Thereafter, the value of the corresponding new energy consumption; the step S2 comprises the following sub-steps:
  • Step S201 let t 2 > t 1 , according to the time from t 1 to t 2 , the acceleration combined vector Transform to Then establish a relationship model between the acceleration vector and the energy consumption as: Where k 1 is a constant coefficient ranging from 0.005 to 0.010, and the optimal value of k 1 is 0.007; ⁇ x is the energy consumption change of the measured object during the movement from time t 1 to time t 2 the amount;
  • Step S202 collecting the energy consumption x 0 of the measured object at time t 1 , and then obtaining the energy consumption x of the measured object at time t 2 is:
  • Step S3 in the present example determines the basal metabolic rate, the maximum heart rate, the oxygen uptake at the maximum heart rate, and the energy consumption at the maximum heart rate based on the underlying information such as age, height, weight, and gender of the subject.
  • the step S3 includes the following sub-steps:
  • Step S301 calculating the basal metabolic rate BMR of the test subject according to the sex, weight, height and age of the test subject;
  • Step S302 calculating a maximum heart rate y max of the test object
  • Step S303 measuring the resting heart rate y rest of the subject under quiet, obtaining the oxygen uptake VO 2max at the maximum heart rate per unit volume by the maximum heart rate y max and the resting heart rate y rest ;
  • Step S304 the energy consumption x max of the subject under the maximum heart rate is obtained by the oxygen uptake amount VO 2max at the maximum heart rate.
  • the value ranges from 5 to 15, the value of ⁇ 3 ranges from 0 to 5, and
  • step S303 the oxygen uptake VO 2max at the maximum heart rate per unit volume is obtained by the maximum heart rate y max and the resting heart rate y rest :
  • VO 2max k 4 *weight*y max /y rest , where k 4 is taken The value ranges from 0.01 to 0.03;
  • Step S4 in this example establishes a relationship model between heart rate and energy consumption, and uses the change of energy consumption to predict and calculate the change of heart rate, thereby realizing the function of predicting the heart rate of the measured object during the exercise; the step S4 includes the following sub step:
  • Step S402 measuring an acceleration vector of the measured object at a certain moment during the movement by the acceleration sensor, and converting it into an energy consumption x max under the maximum heart rate, and then substituting the value of the energy consumption x max at the maximum heart rate into the
  • the heart rate prediction value y corresponding to the time is obtained by describing the formula of step S401.
  • the certain moment is the time at which the heart rate of the object to be measured is to be known. This is set according to the user's needs, and the acceleration vector in each direction is obtained through the moment, and the acceleration combined vector can be obtained, and the value of the energy consumption is combined. , you can calculate the moment
  • the optimal value of ⁇ 1 is 65, the optimal value of ⁇ 2 is 13.73, the best value of ⁇ 3 is 5, and the best value of ⁇ 4 is obtained .
  • the value is -6.9, the optimal value of ⁇ 1 is 660, the optimal value of ⁇ 2 is 9.6, the optimal value of ⁇ 3 is 1.72, and the optimal value of ⁇ 4 is -4.7;
  • the step S302 The optimal value of k 2 is 210, and the optimal value of k 3 is 0.7; in step S303, the optimal value of k 4 is 0.015; in step S304, the best value of k 5 is taken.
  • the value is 20.5; in the step S401, the optimal value of k 6 is 0.0029, and the optimal value of k 7 is 0.035.
  • the method for predicting the heart rate of the motion process based on the acceleration sensor in this example has one-to-one correspondence data for each object to be tested, and the data is data having its own uniqueness for each object to be tested. Therefore, the final calculated heart rate predicted value y is also in one-to-one correspondence with each subject, and the heart rate predicted value y is related to the basic data of the subject, that is, the resting heart rate, gender, and the subject. Age, height, weight and historical data are relevant and therefore very informative.
  • This example proposes a method of using the acceleration sensor to predict the heart rate of the subject under motion, and solves the problem that the traditional heart rate measurement method requires professional operation, equipment and can not be portable, and expands the heart rate prediction in sports training and health monitoring.
  • the application and popularization of multiple fields, the invention can only predict the heart rate of the measured object during exercise by using the acceleration sensor, is simple and easy to operate, and the measurement accuracy satisfies the general training requirements, especially for the elderly or special with heart rate health problems. The crowd is even more significant.
  • this example performs an actual simulation test to test a method for predicting the heart rate of a motion process based on a three-axis acceleration sensor, including the following steps:
  • Step A the three-axis acceleration sensor senses the acceleration signals generated by the object in the front, back, horizontal and vertical directions, and measures the acceleration vectors in the three directions of X, Y and Z, and calculates the acceleration vector corresponding to the time. .
  • Step B establishing a relationship model between the amount of change in the acceleration and the vector on the time axis and the energy consumption
  • the amount of change in energy consumption is obtained, and a value of the new energy consumption is obtained, and the coefficient k 1 ranges from 0.005, 0.007, and 0.010.
  • the range of k 2 is [190, 220], the range of k 3 is [0.5, 1], the best value of k 2 is 210, and the optimum value of k 3 is 0.7.
  • calculate the maximum heart rate oxygen consumption VO 2max k 4 * weight * y max / y rest .
  • the coefficient selected in the calculation method of the oxygen consumption at the maximum heart rate is k 4 .
  • the range of k 4 is [0.01, 0.03], and the optimum value of k 4 is 0.015.
  • the range of k 5 is [19.5, 21.5], and the optimum value of k 5 is 20.5.
  • Step F establish a relationship model between the heart rate and the energy consumption of the measured object during the exercise: y-[ln(x max /(k 6 *BMR)-1)-ln(x max /x-1) ] / k 7 , the energy consumption x max and the basal metabolic rate BMR in the relationship model involving the maximum heart rate can be calculated by the previous steps, and the coefficient k 6 ranges from [0.002, 0.004], the coefficient k The value range of 7 is [0.015, 0.070], the optimum value of k 6 is 0.0029, and the optimum value of k 7 is 0.035.
  • Step G converting the acceleration combined vector measured by the acceleration sensor into the energy consumption x max under the maximum heart rate, and substituting the relationship model between the heart rate and the energy consumption of the object under the motion, that is, the relationship into the step F
  • the module can get the predicted value y of the heart rate.
  • the present invention also provides an apparatus for predicting a heart rate of a motion process based on an acceleration sensor, wherein the apparatus for predicting a heart rate of a motion process based on the acceleration sensor employs a method of predicting a heart rate of a motion process based on an acceleration sensor as described in Embodiment 1 or Embodiment 2.
  • the device for predicting the heart rate of the exercise process based on the acceleration sensor in the present example is a watch, and when the elderly or a special person with a heart rate health problem carries the device for predicting the heart rate of the exercise process based on the acceleration sensor according to the present invention, Time statistics and analysis can achieve heart rate prediction during exercise, which is very significant.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Cardiology (AREA)
  • Biomedical Technology (AREA)
  • Medical Informatics (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Physiology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
  • Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

Abstract

一种基于加速度传感器预测运动过程心率的方法及装置，该方法包括：步骤S1，通过加速度传感器采集并计算受测对象在运动过程中所产生的加速度矢量；步骤S2，通过分析加速度矢量值在时间轴上的变化，计算受测对象在运动过程中能耗的变化，建立加速度矢量和能耗之间的关系模型；步骤S3，采集受测对象的基础信息，计算得到其基础代谢率、最大心率、最大心率下的摄氧量以及能耗；步骤S4，建立受测对象运动过程中心率和能耗之间的关系模型，实现运动过程中针对该受测对象心率的预测。该方法简单易操作，测量精度能够满足训练要求。

Description

一种基于加速度传感器预测运动过程心率的方法及装置 技术领域
本发明涉及一种加速度传感器测心率的方法，尤其涉及一种基于加速度传感器预测运动过程心率的方法，并涉及采用了该基于加速度传感器预测运动过程心率的方法的装置。
背景技术
人体运动能耗和心率的监测在很多领域具有重要意义，在医学、运动训练和日常生活中应用广泛；在医学上传统的心率测量方法依靠心电图进行图像的处理，在运动训练中也需要专业的仪器和操作，无法满足便携、灵活和易操作的要求；现有技术中，也存在较多通过加速度传感器对心率进行测量的方法，但是，这种现有的测量方法仅仅只是起到测量的作用而已，并不能够做到对受测对象在运动过程中进行心率预测的功能，而这个功能恰恰是监测人体健康，进行危险预判断的重要指标，尤其是针对老人等特殊群体，对受测对象在运动过程中进行心率预测的功能显得尤其重要。
发明内容
本发明所要解决的技术问题是需要提供一种通过加速度传感器就可以预测运动过程受测对象的心率的方法，并提供采用了该基于加速度传感器预测运动过程心率的方法的装置。
对此，本发明提供一种基于加速度传感器预测运动过程心率的方法，包括以下步骤：
步骤S1，通过加速度传感器采集并计算受测对象在运动过程中所产生的加速度矢量；
步骤S2，通过分析加速度矢量值在时间轴上的变化，计算受测对象在运动过程中能耗的变化，建立加速度矢量和能耗之间的关系模型；
步骤S3，采集受测对象的基础信息，通过计算得到受测对象的基础代谢率、最大心率、最大心率下的摄氧量以及最大心率下的能耗；
步骤S4，建立受测对象运动过程中心率和能耗之间的关系模型，利用能耗的变化 计算心率的变化，实现运动过程中针对该受测对象心率的预测。
本发明的进一步改进在于，所述步骤S1为通过多轴加速度传感器采集受测对象在运动过程中所产生的电压信号进而得到多轴加速度传感器在各个方向上的加速度矢量，通过各个方向的加速度矢量计算得到加速度合矢量。
本发明的进一步改进在于，所述步骤S1包括以下子步骤：
步骤S101，通过n轴加速度传感器获取t2时刻下，受测对象在n个方向上的加速度矢量
Figure PCTCN2016081427-appb-000001
并计算出在t2时刻的加速度合矢量
Figure PCTCN2016081427-appb-000002
Figure PCTCN2016081427-appb-000003
步骤S102，通过n轴加速度传感器获取t1时刻下，受测对象在n个方向上的加速度矢量
Figure PCTCN2016081427-appb-000004
并计算出在t1时刻的加速度合矢量
Figure PCTCN2016081427-appb-000005
Figure PCTCN2016081427-appb-000006
本发明的进一步改进在于，所述步骤S2包括以下子步骤：
步骤S201，令t2＞t1，根据t1时刻到t2时刻中加速度合矢量从
Figure PCTCN2016081427-appb-000007
变换为
Figure PCTCN2016081427-appb-000008
进而建立加速度矢量和能耗之间的关系模型为：
Figure PCTCN2016081427-appb-000009
其中，k1为取值范围为0.005～0.010的常系数；Δx为从t1时刻到t2时刻里受测对象在运动过程中的能耗变化量；
步骤S202，采集受测对象在t1时刻的能耗x0，进而得到受测对象在t2时刻的能耗x为：
Figure PCTCN2016081427-appb-000010
本发明的进一步改进在于，所述k1的取值为0.007。
本发明的进一步改进在于，所述步骤S3包括以下子步骤：
步骤S301，根据受测对象的性别、体重、身高和年龄，计算出受测对象的基础代谢率BMR；
步骤S302，计算受测对象的最大心率ymax；
步骤S303，测量受测对象在安静时的安静心率yrest，通过最大心率ymax和安静心率yrest得到在单位体积最大心率下的摄氧量VO2max；
步骤S304，通过最大心率下的摄氧量VO2max获取受测对象在最大心率下的能耗xmax。
本发明的进一步改进在于，所述步骤S301中，男性受测对象的基础代谢率BMRmale为BMRmale＝α1+α2*weight+α3*height+α4*age；女性受测对象的基础代谢率BMRfemale为BMRfemale＝β1+β2*weight+β3*height+β4*age；其中，weight为受测对象的体重，height为受测对象的身高，age为受测对象的年龄，α1、α2、α3、α4、β1、β2、β3和β4为预先设置的常系数，所述α1的取值范围为50～80，所述α2的取值范围为10～20，所述α3的取值范围为1～10，所述α4的取值范围为-10～0，所述β1的取值范围为500～700，所述β2的取值范围为5～15，所述β3的取值范围为0～5，所述β4的取值范围为-10～0；
所述步骤S302中，通过ymax＝k2+k3*age计算受测对象的最大心率ymax，其中，k2为取值范围为190～220的常系数，k3为取值范围为0.5～1的常系数；
所述步骤S303，通过最大心率ymax和安静心率yrest得到在单位体积最大心率下的摄氧量VO2max为：VO2max＝k4*weight*ymax/yrest，其中，k4为取值范围为0.01～0.03的常系数；
所述步骤S304中，受测对象在最大心率下的能耗xmax为：xmax＝k5*VO2max，其中，k5为取值范围为19.5～21.5的常系数。
本发明的进一步改进在于，所述步骤S4包括以下子步骤：
步骤S401，建立受测对象运动过程中心率和能耗之间的关系模型为：y＝[ln(xmax/(k6*BMR)-1)-ln(xmax/x-1)]/k7，其中，k6为取值范围为0.002～0.004的常系数，k7为取值范围为0.015～0.070的常系数；
步骤S402，通过加速度传感器测量受测对象在运动过程中某一时刻的加速度矢量，并将其转化为最大心率下的能耗xmax，然后将该最大心率下的能耗xmax的值代入所述步骤S401的公式，即可求出该时刻下对应的心率预测值y。
本发明的进一步改进在于，所述步骤S301中，α1的取值为65，α2的取值为13.73，α3的取值为5，α4的取值为-6.9，β1的取值为660，β2的取值9.6，β3的取值为1.72，β4的取值为-4.7；所述步骤S302中，k2的取值为210，k3的取值为0.7；所述步骤S303中，k4的取值为0.015；所述步骤S304中，k5的取值为20.5；所述步骤S401中，k6的取值为0.0029，k7的取值为0.035。
本发明还提供一种基于加速度传感器预测运动过程心率的装置，采用了如上所述的基于加速度传感器预测运动过程心率的方法。
与现有技术相比，本发明的有益效果在于：通过加速度传感器就能预测运动过程受测对象的心率，简单易操作，测量精度能够满足训练要求；更为具体的，本发明提出了采用加速度传感器实现对受测对象在运动过程的预测心率的方法，解决了传统心率测量方法要求专业的操作、设备且不能便携等难题，拓展了心率预测在运动训练和健康监测等多个领域的应用和普及，本发明仅利用加速度传感器就可以预测受测对象在运动过程中的心率，简单易操作，测量精度满足普通的体能训练要求，尤其是对于老人或是有心率健康问题的特殊人群，意义更加重大。
附图说明
图1是本发明一种实施例的工作流程结构示意图。
具体实施方式
下面结合附图，对本发明的较优的实施例作进一步的详细说明：
实施例1：
如图1所示，本例提供一种基于加速度传感器预测运动过程心率的方法，包括以下步骤：
步骤S1，通过加速度传感器采集并计算受测对象在运动过程所产生的加速度矢量；
步骤S2，通过分析加速度矢量值在时间轴上的变化，计算受测对象在运动过程中能耗的变化，建立加速度矢量和能耗之间的关系模型；
步骤S3，采集受测对象的基础信息，通过计算得到受测对象的基础代谢率、最大心率、最大心率下的摄氧量以及最大心率下的能耗；
步骤S4，建立受测对象运动过程中心率和能耗之间的关系模型，利用能耗的变化计算心率的变化，实现运动过程中针对该受测对象心率的预测。
本例通过加速度传感器依据人体耗氧量和基础代谢率计算运动过程中对象的心率值，所使用的加速度传感器可以感应并采集对象在运动过程中产生的加速度矢量，所述加速度传感器主要包括压电式加速度传感器、压阻式加速度传感器以及基于其他采集原理与方式的加速度传感器，如单轴型加速度传感器、多轴型加速度传感器、角加速度型传感器和地磁复合型传感器，然后对加速度合矢量进行计算，采集及计算的结果作为运动过程中的加速度基础数据，该加速度基础数据采集与计算可以采用实时采集数据、平均数据、间接离散采集数据以及连续采集数据或更多其他方式来实现。
本例利用可测量的、已知的受测对象的基础信息，所述受测对象的基础信息包括安静心率、性别、年龄、身高和体重，所述安静心率为受测对象在安静状态下的心率，进而计算出不同个体的最大心率、最大心率下的耗氧量和基础代谢率，再利用最大心率下的耗氧量求出在最大心率下的能耗，所述能耗为能量消耗量。在运动过程中，受测对象的加速度矢量发生变化，记录在一定时间后加速度的值，计算出在这段时间里，加速度的变化引起的能量消耗量的变化，加上起始时刻的能耗就是经过这段时间后新的能耗；将最大心率下的能耗和当前计算出的新的能耗以及基础代谢率代入到建立的运动过程中心率与能量之间的计算模型，进而能够预测出运动过程中的心率。
具体的，本例所述步骤S1为通过多轴加速度传感器采集受测对象在运动过程中所产生的电压信号进而得到多轴加速度传感器在各个方向上的加速度矢量，通过各个方向的加速度矢量计算得到加速度合矢量。所述各个方向指的是多轴加速度传感器的方向，如三轴加速度传感器则包括前后、水平和垂直共3个方向的加速度矢量。
本例所述步骤S1利用加速度传感器采集受测对象运动过程中产生的电压信号或其他形式的信号，并将运动程度转化成加速度矢量的值表示，由n个方向的加速度矢量计算得到该时刻的加速度合矢量；所述步骤S1包括以下子步骤：
步骤S101，通过n轴加速度传感器获取t2时刻下，受测对象在n个方向上的加速度矢量
Figure PCTCN2016081427-appb-000011
并计算出在t2时刻的加速度合矢量
Figure PCTCN2016081427-appb-000012
Figure PCTCN2016081427-appb-000013
步骤S102，通过n轴加速度传感器获取t1时刻下，受测对象在n个方向上的加速 度矢量
Figure PCTCN2016081427-appb-000014
并计算出在t1时刻的加速度合矢量
Figure PCTCN2016081427-appb-000015
Figure PCTCN2016081427-appb-000016
本例所述步骤S2由步骤S1得到的加速度合矢量的变化，计算受测对象在运动过程中能耗的变化，建立加速度合矢量和能耗之间的关系模型，求出加速度合矢量的变化之后，对应的新的能耗的值；所述步骤S2包括以下子步骤：
步骤S201，令t2＞t1，根据t1时刻到t2时刻中加速度合矢量从
Figure PCTCN2016081427-appb-000017
变换为
Figure PCTCN2016081427-appb-000018
进而建立加速度矢量和能耗之间的关系模型为：
Figure PCTCN2016081427-appb-000019
其中，k1为取值范围为0.005～0.010的常系数，所述k1的最佳取值为0.007；Δx为从t1时刻到t2时刻里受测对象在运动过程中的能耗变化量；
步骤S202，采集受测对象在t1时刻的能耗x0，进而得到受测对象在t2时刻的能耗x为：
Figure PCTCN2016081427-appb-000020
本例所述步骤S3根据受测对象的年龄、身高，体重以及性别等基础信息，求出该受测对象的基础代谢率、最大心率和最大心率下的摄氧量以及最大心率下的能耗；所述步骤S3包括以下子步骤：
步骤S301，根据受测对象的性别、体重、身高和年龄，计算出受测对象的基础代谢率BMR；
步骤S302，计算受测对象的最大心率ymax；
步骤S303，测量受测对象在安静时的安静心率yrest，通过最大心率ymax和安静心率yrest得到在单位体积最大心率下的摄氧量VO2max；
步骤S304，通过最大心率下的摄氧量VO2max获取受测对象在最大心率下的能耗xmax。
本例所述步骤S301中，男性受测对象的基础代谢率BMRmale为BMRmale＝α1+α2*weight+α3*height+α4*age；女性受测对象的基础代谢率BMRfemale为BMRfemale＝β1+β2*weight+β3*height+β4*age；其中，weight为 受测对象的体重，height为受测对象的身高，age为受测对象的年龄，α1、α2、α3、α4、β1、β2、β3和β4为预先设置的常系数，所述α1的取值范围为50～80，所述α2的取值范围为10～20，所述α3的取值范围为1～10，所述α4的取值范围为-10～0，所述β1的取值范围为500～700，所述β2的取值范围为5～15，所述β3的取值范围为0～5，所述β4的取值范围为-10～0；
所述步骤S302中，通过ymax＝k2+k3*age计算受测对象的最大心率ymax，其中，k2为取值范围为190～220的常系数，k3为取值范围为0.5～1的常系数；
所述步骤S303，通过最大心率ymax和安静心率yrest得到在单位体积最大心率下的摄氧量VO2max为：VO2max＝k4*weight*ymax/yrest，其中，k4为取值范围为0.01～0.03的常系数；
所述步骤S304中，受测对象在最大心率下的能耗xmax为：xmax＝k5*VO2max，其中，k5为取值范围为19.5～21.5的常系数。
本例所述步骤S4建立心率和能耗之间的关系模型，利用能耗的变化预测和计算心率的变化，进而实现对运动过程中受测对象心率的预测功能；所述步骤S4包括以下子步骤：
步骤S401，建立受测对象运动过程中心率和能耗之间的关系模型为：y＝[ln(xmax/(k6*BMR)-1)-ln(xmax/x-1)]/k7，其中，k6为取值范围为0.002～0.004的常系数，k7为取值范围为0.015～0.070的常系数；
步骤S402，通过加速度传感器测量受测对象在运动过程中某一时刻的加速度矢量，并将其转化为最大心率下的能耗xmax，然后将该最大心率下的能耗xmax的值代入所述步骤S401的公式，即可求出该时刻下对应的心率预测值y。所述某一时刻即为想要知道受测对象心率的时刻，这个根据用户的需求进行设置，通过该时刻下在各个方向的加速度矢量，并能够求出其加速度合矢量，结合能耗的值，就能够计算得到该时刻下
通过发明人的研究证明，本例所述步骤S301中，α1的最佳取值为65，α2的最佳取值为13.73，α3的最佳取值为5，α4的最佳取值为-6.9，β1的最佳取值为660，β2的最佳取值9.6，β3的最佳取值为1.72，β4的最佳取值为-4.7；所述步骤S302中，k2的最佳取值为210，k3的最佳取值为0.7；所述步骤S303中，k4的最佳取值为0.015；所述 步骤S304中，k5的最佳取值为20.5；所述步骤S401中，k6的最佳取值为0.0029，k7的最佳取值为0.035。
值得一提的是，本例所述的基于加速度传感器预测运动过程心率的方法针对每一个受测对象都有一一对应的数据，这些数据是支持每一个受测对象的具有自身独特性的数据，因此，最终计算出来的心率预测值y也是与每一个受测对象一一对应的，这个心率预测值y与该受测对象的基础数据相关，即与该受测对象的安静心率、性别、年龄、身高、体重和历史采集数据都有相关性，因此，非常具备参考价值。
本例提出了采用加速度传感器实现对受测对象在运动过程的预测心率的方法，解决了传统心率测量方法要求专业的操作、设备且不能便携等难题，拓展了心率预测在运动训练和健康监测等多个领域的应用和普及，本发明仅利用加速度传感器就可以预测受测对象在运动过程中的心率，简单易操作，测量精度满足普通训练要求，尤其是对于老人或是有心率健康问题的特殊人群，意义更加重大。
实施例2：
在实施例1的基础上，本例进行了实际的模拟测试，测试基于三轴加速度传感器预测运动过程心率的方法，包括以下步骤：
步骤A，三轴加速度传感器感应受测对象在前后、水平和垂直三个方向产生的加速度信号，及测量X、Y和Z三个方向上的加速度矢量，并计对应该时刻下的加速度合矢量。
步骤B，建立加速度合矢量在时间轴上变化量和能耗之间的关系模型
Figure PCTCN2016081427-appb-000021
求出能耗的变化量，进而求出新的能耗的值，所述系数k1的取值范围为0.005、0.007和0.010中的任意一个。
步骤C，根据受测对象的性别、体重、身高和年龄，计算出受测人体的基础代谢率BMR，其中，男士的基础代谢率方法BMRmale＝α1+α2*weight+α3*height+α4*age，系数为α1，α2，α3，α4，女士的基础代谢率方法BMRfemale＝β1+β2*weight+β3*height+β4*age，系数为β1，β2，β3，β4；α1的最佳取值为65，α2的最佳取值为13.73，α3的最佳取值为5，α4 的最佳取值为-6.9；β1的最佳取值为660，α2的最佳取值为9.6，β3的最佳取值为1.72，β4的最佳取值为-4.7。
步骤D，通过公式ymax＝k2+k3*age求出最大心率ymax，k2，k3均是常数。k2的取值范围为[190，220]，k3的取值范围为[0.5，1]，k2的最佳取值为210，k3的最佳取值为0.7。并利用测量得到的安静心率yrest，计算最大心率下耗氧量VO2max＝k4*weight*ymax/yrest。最大心率下耗氧量的计算方法中选取的系数为k4。k4的取值范围为[0.01，0.03]，k4的最佳取值为0.015。
步骤E，通过最大心率的耗氧量VO2max计算出最大心率下的能耗xmax，计算最大心率下的能耗xmax的方法xmax＝k5*VO2max，系数为k5。k5的取值范围为[19.5，21.5]，k5的最佳取值为20.5。
步骤F，建立运动过程中，受测对象的心率和能耗之间的关系模型为：y-[ln(xmax/(k6*BMR)-1)-ln(xmax/x-1)]/k7，该关系模型中涉及到最大心率下的能耗xmax和基础代谢率BMR等可以通过前面的步骤计算得到，而系数k6的取值范围为[0.002，0.004]，系数k7的取值范围为[0.015，0.070]，k6的最佳取值为0.0029，k7的最佳取值为0.035。
步骤G，将加速度传感器计测出的加速度合矢量，转化为最大心率下的能耗xmax，代入运动过程中受测对象的心率和能耗之间的关系模型，即代入步骤F中的关系模块就可以得到心率的预测值y。
实施例3：
本发明还提供一种基于加速度传感器预测运动过程心率的装置，所述基于加速度传感器预测运动过程心率的装置采用了如实施例1或实施例2所述的基于加速度传感器预测运动过程心率的方法。
优选的，本例所述基于加速度传感器预测运动过程心率的装置为手表，当老人或是有心率健康问题的特殊人群带上本发明所述的基于加速度传感器预测运动过程心率的装置时，通过一定时间的数据统计和分析，便能够在运动过程中实现心率的预测，意义非常重大。
以上内容是结合具体的优选实施方式对本发明所作的进一步详细说明，不能认定 本发明的具体实施只局限于这些说明。对于本发明所属技术领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干简单推演或替换，都应当视为属于本发明的保护范围。

Claims (16)

  1. 一种基于加速度传感器预测运动过程心率的方法，其特征在于，包括以下步骤：
    步骤S1，通过加速度传感器采集并计算受测对象在运动过程中所产生的加速度矢量；
    步骤S2，通过分析加速度矢量值在时间轴上的变化，计算受测对象在运动过程中能耗的变化，建立加速度矢量和能耗之间的关系模型；
    步骤S3，采集受测对象的基础信息，通过计算得到受测对象的基础代谢率、最大心率、最大心率下的摄氧量以及最大心率下的能耗；
    步骤S4，建立受测对象运动过程中心率和能耗之间的关系模型，利用能耗的变化计算心率的变化，实现运动过程中针对该受测对象心率的预测。
  2. 根据权利要求1所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S1为通过多轴加速度传感器采集受测对象在运动过程中所产生的电压信号进而得到多轴加速度传感器在各个方向上的加速度矢量，通过各个方向的加速度矢量计算得到加速度合矢量。
  3. 根据权利要求1所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S1包括以下子步骤：
    步骤S101，通过n轴加速度传感器获取t2时刻下，受测对象在n个方向上的加速度矢量
    Figure PCTCN2016081427-appb-100001
    并计算出在t2时刻的加速度合矢量
    Figure PCTCN2016081427-appb-100002
    Figure PCTCN2016081427-appb-100003
    步骤S102，通过n轴加速度传感器获取t1时刻下，受测对象在n个方向上的加速度矢量
    Figure PCTCN2016081427-appb-100004
    并计算出在t1时刻的加速度合矢量
    Figure PCTCN2016081427-appb-100005
    Figure PCTCN2016081427-appb-100006
  4. 根据权利要求3所述的基于加速度传感器预测运动过程心率的方法，其特征在 于，所述步骤S2包括以下子步骤：
    步骤S201，令t2＞t1，根据t1时刻到t2时刻中加速度合矢量从
    Figure PCTCN2016081427-appb-100007
    变换为
    Figure PCTCN2016081427-appb-100008
    进而建立加速度矢量和能耗之间的关系模型为：
    Figure PCTCN2016081427-appb-100009
    其中，k1为取值范围为0.005～0.010的常系数；Δx为从t1时刻到t2时刻里受测对象在运动过程中的能耗变化量；
    步骤S202，采集受测对象在t1时刻的能耗x0，进而得到受测对象在t2时刻的能耗x为：
    Figure PCTCN2016081427-appb-100010
  5. 根据权利要求4所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述k1的取值为0.007。
  6. 根据权利要求1至5任意一项所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S3包括以下子步骤：
    步骤S301，根据受测对象的性别、体重、身高和年龄，计算出受测对象的基础代谢率BMR；
    步骤S302，计算受测对象的最大心率ymax；
    步骤S303，测量受测对象在安静时的安静心率ypest，通过最大心率ymax和安静心率ypest得到在单位体积最大心率下的摄氧量VO2max；
    步骤S304，通过最大心率下的摄氧量VO2max获取受测对象在最大心率下的能耗xmax。
  7. 根据权利要6所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S301中，男性受测对象的基础代谢率BMRmale为BMRmale＝α1+α2*weight+α3*height+α4*age；女性受测对象的基础代谢率BMRfemale为BMRfemale＝β1+β2*weight+β3*height+β4*age；其中，weight为受测对象的体重，height为受测对象的身高，age为受测对象的年龄，α1、α2、α3、α4、β1、β2、β3和β4为预先设置的常系数，所述α1的取值范围为50～80，所述α2的取值范围为10～20，所述α3的取值范围为1～10，所述α4的取值范围为-10～0，所述β1的取值范围为500～700，所述β2的取值范围为5～15，所述β3的取值范围为0～5，所述β4的取值范围为-10～0；
    于，所述步骤S2包括以下子步骤：
    步骤S201，令t2＞t1，根据t1时刻到t2时刻中加速度合矢量从
    Figure PCTCN2016081427-appb-100011
    变换为
    Figure PCTCN2016081427-appb-100012
    进而建立加速度矢量和能耗之间的关系模型为：
    Figure PCTCN2016081427-appb-100013
    其中，k1为取值范围为0.005～0.010的常系数；Δx为从t1时刻到t2时刻里受测对象在运动过程中的能耗变化量；
    步骤S202，采集受测对象在t1时刻的能耗x0，进而得到受测对象在t2时刻的能耗x为：
    Figure PCTCN2016081427-appb-100014
  8. 根据权利要求4所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述k1的取值为0.007。
  9. 根据权利要求1至5任意一项所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S3包括以下子步骤：
    步骤S301，根据受测对象的性别、体重、身高和年龄，计算出受测对象的基础代谢率BMR；
    步骤S302，计算受测对象的最大心率ymax；
    步骤S303，测量受测对象在安静时的安静心率ypest，通过最大心率ymax和安静心率ypest得到在单位体积最大心率下的摄氧量VO2max；
    步骤S304，通过最大心率下的摄氧量VO2max获取受测对象在最大心率下的能耗xmax。
  10. 根据权利要6所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S301中，男性受测对象的基础代谢率BMRmale为BMRmale＝α1+α2*weight+α3*height+α4*age；女性受测对象的基础代谢率BMRfemale为BMRfemale＝β1+β2*weight+β3*height+β4*age；其中，weight为受测对象的体重，height为受测对象的身高，age为受测对象的年龄，α1、α2、α3、α4、β1、β2、β3和β4为预先设置的常系数，所述α1的取值范围为50～80，所述α2的取值范围为10～20，所述α3的取值范围为1～10，所述α4的取值范围为-10～0，所述β1的取值范围为500～700，所述β2的取值范围为5～15，所述β3的取值范围为0～5，所述β4的取值范围为-10～0；
    所述步骤S302中，通过ymax＝k2+k3*age计算受测对象的最大心率ymax，其中，k2为取值范围为190～220的常系数，k3为取值范围为0.5～1的常系数；
    所述步骤S303，通过最大心率ymax和安静心率ypest得到在单位体积最大心率下的摄氧量VO2max为：VO2max＝k4*weight*ymax/ypest，其中，k4为取值范围为0.01～0.03的常系数；
    所述步骤S304中，受测对象在最大心率下的能耗xmax为：xmax＝k5*VO2max，其中，k5为取值范围为19.5～21.5的常系数。
  11. 根据权利要求7所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S4包括以下子步骤：
    步骤S401，建立受测对象运动过程中心率和能耗之间的关系模型为：y＝[ln(xmax/(k6*BMR)-1)-ln(xmax/x-1)]/k7，其中，k6为取值范围为0.002～0.004的常系数，k7为取值范围为0.015～0.070的常系数；
    步骤S402，通过加速度传感器测量受测对象在运动过程中某一时刻的加速度矢量，并将其转化为最大心率下的能耗xmax，然后将该最大心率下的能耗xmax的值代入所述步骤S401的公式，即可求出该时刻下对应的心率预测值y。
  12. 根据权利要求8所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S301中，α1的取值为65，α2的取值为13.73，α3的取值为5，α4的取值为-6.9，β1的取值为660，β2的取值9.6，β3的取值为1.72，β4的取值为-4.7；所述步骤S302中，k2的取值为210，k3的取值为0.7；所述步骤S303中，k4的取值为0.015；所述步骤S304中，k5的取值为20.5；所述步骤S401中，k6的取值为0.0029，k7的取值为0.035。
  13. 一种基于加速度传感器预测运动过程心率的装置，其特征在于，采用了如权利要求1至9任意一项所述的基于加速度传感器预测运动过程心率的方法。
    所述步骤S302中，通过ymax＝k2+k3*age计算受测对象的最大心率ymax，其中，k2为取值范围为190～220的常系数，k3为取值范围为0.5～1的常系数；
    所述步骤S303，通过最大心率ymax和安静心率ypest得到在单位体积最大心率下的摄氧量VO2max为：VO2max＝k4*weight*ymax/ypest，其中，k4为取值范围为0.01～0.03的常系数；
    所述步骤S304中，受测对象在最大心率下的能耗xmax为：xmax＝k5*VO2max，其中，k5为取值范围为19.5～21.5的常系数。
  14. 根据权利要求7所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S4包括以下子步骤：
    步骤S401，建立受测对象运动过程中心率和能耗之间的关系模型为：y＝[ln(xmax/(k6*BMR)-1)-ln(xmax/x-1)]/k7，其中，k6为取值范围为0.002～0.004的常系数，k7为取值范围为0.015～0.070的常系数；
    步骤S402，通过加速度传感器测量受测对象在运动过程中某一时刻的加速度矢量，并将其转化为最大心率下的能耗xmax，然后将该最大心率下的能耗xmax的值代入所述步骤S401的公式，即可求出该时刻下对应的心率预测值y。
  15. 根据权利要求8所述的基于加速度传感器预测运动过程心率的方法，其特征在于，所述步骤S301中，α1的取值为65，α2的取值为13.73，α3的取值为5，α4的取值为-6.9，β1的取值为660，β2的取值9.6，β3的取值为1.72，β4的取值为-4.7；所述步骤S302中，k2的取值为210，k3的取值为0.7；所述步骤S303中，k4的取值为0.015；所述步骤S304中，k5的取值为20.5；所述步骤S401中，k6的取值为0.0029，k7的取值为0.035。
  16. 一种基于加速度传感器预测运动过程心率的装置，其特征在于，采用了如权利要求1至9任意一项所述的基于加速度传感器预测运动过程心率的方法。
PCT/CN2016/081427 2015-11-06 2016-05-09 一种基于加速度传感器预测运动过程心率的方法及装置 WO2017075953A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510752717.6A CN105286842B (zh) 2015-11-06 2015-11-06 一种基于加速度传感器预测运动过程心率的方法及装置
CN201510752717.6 2015-11-06

Publications (1)

Publication Number Publication Date
WO2017075953A1 true WO2017075953A1 (zh) 2017-05-11

Family

ID=55185175

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/081427 WO2017075953A1 (zh) 2015-11-06 2016-05-09 一种基于加速度传感器预测运动过程心率的方法及装置

Country Status (2)

Country Link
CN (1) CN105286842B (zh)
WO (1) WO2017075953A1 (zh)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105286842B (zh) * 2015-11-06 2018-04-03 深圳风景网络科技有限公司 一种基于加速度传感器预测运动过程心率的方法及装置
CN107595273B (zh) * 2017-09-12 2019-12-13 高驰运动科技(深圳)有限公司 一种心率估算方法和装置
CN108420413B (zh) * 2018-02-07 2020-12-25 广东中科慈航信息科技有限公司 一种测量心率的方法及其装置
CN108903929B (zh) * 2018-03-30 2021-02-26 广东思派康电子科技有限公司 心率检测修正的方法、装置、存储介质和***
CN110610233B (zh) * 2019-09-19 2023-04-07 福建宜准信息科技有限公司 基于领域知识和数据驱动的健身跑心率预测方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010023320A1 (en) * 2000-03-07 2001-09-20 Hannu Kinnunen Method and equipment for human-related measuring
CN101518444A (zh) * 2008-02-28 2009-09-02 株式会社岛野 消耗热量测定装置,消耗热量测定方法以及消耗热量测定用预先处理方法
WO2013143893A1 (en) * 2012-03-28 2013-10-03 Biorics Nv Method for monitoring an accurate heart rate
CN103701504A (zh) * 2014-01-13 2014-04-02 李漾 基于无线同步技术的体征监测仪
CN204203969U (zh) * 2014-10-30 2015-03-11 武汉体育学院 一种基于传感器技术的健康运动指导装置
CN105286842A (zh) * 2015-11-06 2016-02-03 深圳风景网络科技有限公司 一种基于加速度传感器预测运动过程心率的方法及装置

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105530864B (zh) * 2013-09-13 2019-09-03 博能电子公司 用于监测身体活动的***
US20150088006A1 (en) * 2013-09-20 2015-03-26 Simbionics Method for determining aerobic capacity
JP6599883B2 (ja) * 2014-03-17 2019-10-30 コーニンクレッカ フィリップス エヌ ヴェ 心拍数モニターシステム、心拍数モニタリング方法及びコンピュータプログラム
JP6121069B2 (ja) * 2014-03-17 2017-04-26 コーニンクレッカ フィリップス エヌ ヴェＫｏｎｉｎｋｌｉｊｋｅ Ｐｈｉｌｉｐｓ Ｎ．Ｖ． 心拍監視デバイス

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20010023320A1 (en) * 2000-03-07 2001-09-20 Hannu Kinnunen Method and equipment for human-related measuring
CN101518444A (zh) * 2008-02-28 2009-09-02 株式会社岛野 消耗热量测定装置,消耗热量测定方法以及消耗热量测定用预先处理方法
WO2013143893A1 (en) * 2012-03-28 2013-10-03 Biorics Nv Method for monitoring an accurate heart rate
CN103701504A (zh) * 2014-01-13 2014-04-02 李漾 基于无线同步技术的体征监测仪
CN204203969U (zh) * 2014-10-30 2015-03-11 武汉体育学院 一种基于传感器技术的健康运动指导装置
CN105286842A (zh) * 2015-11-06 2016-02-03 深圳风景网络科技有限公司 一种基于加速度传感器预测运动过程心率的方法及装置

Also Published As

Publication number Publication date
CN105286842A (zh) 2016-02-03
CN105286842B (zh) 2018-04-03

Similar Documents

Publication Publication Date Title
WO2017075953A1 (zh) 一种基于加速度传感器预测运动过程心率的方法及装置
RU2535615C2 (ru) Определение затрат энергии пользователя
JP6531161B2 (ja) 健康リスク指標決定
US9999388B2 (en) Hand-held device for the endurance training and the determination of endurance metrics
JP5448515B2 (ja) 生体信号測定装置
US20160042529A1 (en) Systems and Methods for Non-Contact Tracking and Analysis of Physical Activity
TW201233977A (en) Portable evaluator of amount of exercise and method of evaluating amount of exercise thereof
JP2017158999A (ja) 最大酸素消費をリアルタイムに監視する監視方法
Xin et al. A wearable respiration and pulse monitoring system based on PVDF piezoelectric film
US20170181689A1 (en) System and Method for Measuring the Muscle Tone
CN113873938A (zh) 用于无创心脏监测的***、装置和方法
US20220378349A1 (en) A novel means of assessing muscular function and frailty
CN105054902A (zh) 一种新型体检机及其体质评估方法
JP2018503413A (ja) 心肺適応能評価
US10568547B1 (en) Multifunctional assessment system for assessing muscle strength, mobility, and frailty
US10912510B2 (en) Index deriving device, wearable device, and mobile device
CN105212967A (zh) 一种人体能量消耗监测装置及其手环
KR101817274B1 (ko) 다 센서 기반 착용형 에너지 소모량 측정 장치 및 방법
JP2002336219A (ja) 消費カロリー測定方法及び消費カロリー測定装置
TWI580404B (zh) 肌肉張力感測方法及系統
CN205144602U (zh) 一种人体能量消耗监测装置及其手环
CN106504235A (zh) 基于图像处理的心率监测方法
Gauthier et al. Review of methods to map people’s daily activity–application for smart homes
CN200954107Y (zh) 以蓝牙耳机监控心跳运动的装置
KR101161492B1 (ko) Ｉｄｃ 센서를 활용한 휴대용 칼로리 측정 장치 및 방법

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16861226

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC. EPO FORM 1205A DATED 25.09.18

122 Ep: pct application non-entry in european phase

Ref document number: 16861226

Country of ref document: EP

Kind code of ref document: A1