WO2021128923A1 - 运动量测量方法、装置及电子设备 - Google Patents

运动量测量方法、装置及电子设备 Download PDF

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Publication number
WO2021128923A1
WO2021128923A1 PCT/CN2020/112930 CN2020112930W WO2021128923A1 WO 2021128923 A1 WO2021128923 A1 WO 2021128923A1 CN 2020112930 W CN2020112930 W CN 2020112930W WO 2021128923 A1 WO2021128923 A1 WO 2021128923A1
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target user
exercise
information
doppler
user
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PCT/CN2020/112930
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English (en)
French (fr)
Inventor
宋德超
陈翀
李斌山
陈向文
罗晓宇
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珠海格力电器股份有限公司
珠海联云科技有限公司
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Publication of WO2021128923A1 publication Critical patent/WO2021128923A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/20—Movements or behaviour, e.g. gesture recognition

Definitions

  • the present disclosure relates to the technical field of electromagnetic wave detection, and in particular to a method, device and electronic equipment for measuring the amount of exercise.
  • the present disclosure provides a method, device and electronic device for measuring the amount of exercise.
  • the present disclosure provides a method for measuring exercise volume, including:
  • the exercise amount information is displayed to the target user through the terminal device.
  • the step of determining the exercise volume information of the target user according to the environmental information of the area where the target user is located includes:
  • the identity information and location change information of the target user are determined according to the environmental information of the area where the target user is located, where the location change information is the displacement and time consumed by the target user moving from the first location to the second location ;
  • the exercise amount information of the target user is determined according to the position change information and exercise state of the target user.
  • the step of determining the identity information and location change information of the target user according to the environmental information of the area where the target user is located includes:
  • the Doppler feature data in the Doppler wave data is acquired, and the Doppler feature data corresponding to each moving object is input into an identity discrimination classifier to train to obtain the target user's
  • the steps of identity information and location change information include:
  • Each of the acquired Doppler feature data is input into the identity discrimination classifier to obtain the user identity corresponding to the sample with the highest correlation in the identity discrimination classifier, and the identity information and location of the target user are determined Change information.
  • the step of collecting target Doppler wave data generated by the target user in different motion states, and determining the motion state of the target user according to the target Doppler wave data includes:
  • the Doppler feature data of the target user is input into the motion state classifier model to obtain the motion state of the target user in different time periods.
  • the method before inputting the Doppler characteristic data of the target user into the motion state classifier model to obtain the motion state of the target user in different time periods, the method further includes:
  • the target Doppler wave data is input into a support vector machine classifier as a motion state sample for training, and the motion state classifier model is obtained.
  • the step of determining the exercise amount information of the target user according to the position change information and exercise state of the target user includes:
  • an exercise volume measuring device including:
  • the environmental information acquisition module is configured to acquire environmental information of the area where the target user is located through a radar signal, where the radar signal is a signal returned after a millimeter wave radar sends a detection signal to the target user;
  • the data processing module is configured to determine the amount of exercise information of the target user according to the environmental information of the area where the target user is located, wherein the amount of exercise information includes the duration of the target user corresponding to the exercise state, and the amount of exercise The movement distance corresponding to the state;
  • the data display module is configured to display the exercise volume information to the target user through the terminal device.
  • the present disclosure provides an electronic device including a memory, a processor, and a program stored on the memory and capable of running on the processor, and the processor implements the steps of the above method when the program is executed.
  • the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are realized.
  • the method and device provided by the embodiments of the present disclosure can realize non-contact measurement of the amount of human body movement without the need for the human body to carry a wearable measurement device, and can accurately measure the amount of exercise of the user without bringing the measured user The inconvenience and uncomfortable feelings enhance the user experience.
  • FIG. 1 is a flowchart of a method for measuring exercise amount according to an embodiment of the disclosure
  • FIG. 2 is a schematic diagram of exercise statistics according to an embodiment of the present disclosure
  • FIG. 3 is another schematic diagram of exercise volume statistics provided by an embodiment of the present disclosure.
  • FIG. 4 is a schematic diagram of a device for measuring exercise amount provided by an embodiment of the disclosure.
  • FIG. 5 is a schematic diagram of the internal structure of an electronic device provided by an embodiment of the disclosure.
  • FIG. 1 is a flowchart of a method for measuring exercise amount provided by an embodiment of the present disclosure, and the method includes:
  • the present disclosure uses millimeter-wave radar to detect targets in a certain space, where the space is a fixed space, such as indoors, basketball courts, and other places.
  • the millimeter-wave radar is installed at a certain position in the space, for example, The millimeter wave radar is installed in an indoor air conditioner or on the ceiling of a basketball court.
  • the millimeter wave radar transmits electromagnetic wave signals to a fixed space.
  • the Doppler wave signal reflected by the user in the space can determine a lot of user information, and In a fixed space, especially indoor interference, the accuracy of the measurement results using millimeter wave radar is relatively high.
  • the millimeter wave radar transmits electromagnetic wave signals into the space, and all objects in the space will produce reflected waves.
  • the millimeter wave radar receives these reflected waves and sends the received time and information contained in the waveform to the subsequent data analysis module To deal with.
  • millimeter-wave radar to obtain the user's exercise information in a fixed space will not affect the user, and the obtained information has high accuracy and accurate measurement results.
  • a digital signal filter is used to filter the received reflected wave to filter out noise signals and abnormal points.
  • the K clustering algorithm is selected to perform clustering based on the point cloud data detected in the spatial environment in the return signal.
  • Each class can represent an object, and the position and height attributes of the object can be identified.
  • the position change information determines whether each object is stationary or moving.
  • each moving object After acquiring the morphological characteristics of each object, it is necessary to identify the user identity from each moving object. For example, in a basketball court, multiple users are playing a basketball game, and the moving objects include basketball in addition to the user. It is necessary to measure the user's exercise volume information, but not the basketball exercise volume information, so it is very important to determine the user's identity.
  • the millimeter wave radar transmits and receives millimeter wave signals in real time, and sends the object-related information carried in the millimeter wave to the background for information processing.
  • each moving object is tracked and captured in real time, and the position of each moving object is obtained. Doppler wave data from information and objects, and extract the Doppler feature data of each moving object from the Doppler wave data.
  • the Doppler wave information of each user is collected as sample data in advance, including the user's height, weight, posture and other state data, and input the sample data of each user into the relevant filter (CF) for training.
  • CF filter
  • Obtain different identity discrimination related filtering classifiers identity discrimination classifiers for short
  • a user’s sample data can be trained to obtain an identity discrimination classifier for detecting the identity of the user, and then each movement obtained in the above embodiment
  • the Doppler feature data of the object is calculated in turn with each identity discrimination classifier.
  • the obtained Doppler feature data of each moving object and which identity discrimination The classifier has the highest correlation and it can be determined that the moving object is the user corresponding to the identity discrimination classifier with the highest correlation, and the user's identity information can be determined.
  • the user's identity information can be determined. Position information, speed information, etc. at different times.
  • three existing users have provided their own sample data, and have been trained to obtain three identity discrimination classifiers A, B, and C.
  • the Doppler feature data of each moving object obtained are respectively compared with the identity discrimination classifier A. , B, and C perform correlation calculation, where the correlation between the first moving object and the three identity discrimination classifiers A, B, and C is 30%, 90%, and 40%; the second moving object is related to A, B
  • the correlations of the three identity discrimination classifiers of, C are 80%, 20%, and 30% respectively;
  • the correlations of the third moving object with the three identity discrimination classifiers of A, B, and C are 40%, 30%, 85%, it can be judged that the first moving object has the highest correlation with the identity discrimination classifier B, and it can be judged that the user identity corresponding to the identity discrimination classifier B is the first moving object detected.
  • the identity discrimination classifier The user identity corresponding to A is the first moving object detected, and the user identity corresponding to the identity discrimination classifier C is the first moving object detected. From this, the identity of each user can be obtained, and the user identity to be detected is selected as the target user identity, Doppler characteristic data corresponding to the target user identity is obtained, and position information, speed information, etc. at different times are obtained.
  • the exercise state and exercise time of the target user need to be determined.
  • the target Doppler wave data generated by the target user is input into the support vector machine classifier as a training sample.
  • a motion state classifier model is obtained, which can distinguish the target user's motion state including: sleeping, sitting, lying, walking, jogging, fast running and other states.
  • support vector machine classifier here can also be replaced by a machine learning model such as a neural network model, a random forest, a decision tree, etc., and a classifier that can classify the user's motion state can be obtained by training.
  • a machine learning model such as a neural network model, a random forest, a decision tree, etc.
  • the motion state classifier model After training the motion state classifier model, obtain the target user's real-time position information and Doppler wave data information, and then input the user's real-time Doppler wave data information into the motion state classifier model to obtain the target user's real-time motion state Information, and finally calculate the movement distance of the target user in each movement state according to the duration of each movement state.
  • the movement time in any movement state is the accumulation of the length of time in the movement state
  • the movement distance is the movement distance in the movement state.
  • the accumulation of the movement distance of the state For example, the target user has jogging training at 6-8 o'clock in the morning, and jogging training at 7-9 o'clock in the evening.
  • the motion state classifier model When the motion state classifier model recognizes the user's jogging state, it will calculate the total time in the jogging state. That is, two hours at 6-8 o'clock in the morning and two hours at 7-9 o'clock in the evening, a total of four hours, and the user's jogging distance is calculated by the user's position change during these four hours.
  • the user information obtained by the millimeter-wave radar can be calculated by the training model to obtain accurate exercise information of the various exercise states of the user to be detected.
  • the required time is short and the calculation accuracy is high, so that the user can accurately understand their own exercise volume.
  • FIG. 2 is a schematic diagram of exercise volume statistics provided by an embodiment of the present disclosure.
  • the statistics can be Use the time of day as the limit to count the user's exercise volume in a day, calculate the proportion of each exercise state to the total exercise volume in a day, and store the user's daily exercise volume statistics chart, compare historical statistics with current statistics, Suggest exercises for users. For example, the user’s exercise volume from Monday to Friday is similar, but on Saturday, the user’s exercise volume has changed significantly, and the proportion of lying down has increased a lot, indicating that the user will usually exercise time to sleep or rest. At this time, it will be given to the user Provide suggestions that you should exercise as soon as possible.
  • FIG. 3 is another schematic diagram of exercise statistics provided by the embodiments of the present disclosure.
  • the user’s exercise distance of each exercise state in a day can also be used to count the user’s exercise volume, calculate the proportion of the exercise distance of each exercise state to the total exercise distance, and set the user’s daily exercise threshold according to the health level , To detect whether each exercise state reaches the threshold level, if it does not reach the standard, it will record and remind the user.
  • the statistical graphs shown in Figures 2 and 3 are not the statistical graphs presented to the user.
  • the user's movement distance of each sports state and the duration of the sports state are presented to the user.
  • the user is provided with exercise suggestions. If the user exercises too much, the user will be advised to reduce the exercise volume. If the user has a certain exercise state for too long, the user will be reminded to balance each exercise state.
  • FIG. 4 is a schematic diagram of an exercise amount measuring device provided by an embodiment of the disclosure. As shown in FIG. 4, the device includes:
  • the environmental information acquisition module 41 is configured to acquire environmental information of the area where the target user is located through a radar signal, where the radar signal is a signal returned after the millimeter wave radar sends a detection signal to the target user;
  • the data processing module 42 is configured to determine the amount of exercise information of the target user according to the environmental information of the area where the target user is located, where the amount of exercise information includes the duration of the target user corresponding to the exercise state and the exercise distance corresponding to the exercise state;
  • the data display module 43 is configured to display the exercise volume information to the target user through the terminal device.
  • the above-mentioned device further includes a storage module, which is composed of a storage medium and related circuits, and is configured to store signals collected by the millimeter wave radar and statistical exercise data.
  • a storage module which is composed of a storage medium and related circuits, and is configured to store signals collected by the millimeter wave radar and statistical exercise data.
  • the above-mentioned device further includes a communication module, which is a chip module equipped with a wireless transmission function, which is configured to transmit the collected millimeter wave radar signals and send the target user's exercise data to the user terminal (mobile phone or computer) in real time, and Cloud platform server.
  • a communication module which is a chip module equipped with a wireless transmission function, which is configured to transmit the collected millimeter wave radar signals and send the target user's exercise data to the user terminal (mobile phone or computer) in real time
  • Cloud platform server a communication module
  • the data processing module 42 includes an identity recognition unit, which is configured to determine the identity information and location change information of the target user according to the environmental information of the area where the target user is located, where the location change information is that the target user moves from the first location. The displacement to the second position and the time consumed; it also includes a motion state acquisition unit, which is set to collect target Doppler wave data generated by the target user in different motion states, and determine the target user’s motion based on the target Doppler wave data State; the exercise amount calculation unit is set to determine the target user's exercise amount information according to the target user's position change information and exercise state.
  • an identity recognition unit which is configured to determine the identity information and location change information of the target user according to the environmental information of the area where the target user is located, where the location change information is that the target user moves from the first location. The displacement to the second position and the time consumed; it also includes a motion state acquisition unit, which is set to collect target Doppler wave data generated by the target user in different motion states, and determine the target user’s motion
  • the identity recognition unit is further configured to: determine the object and its location in the environmental information according to the K-means clustering algorithm; obtain the Doppler wave data and position change information generated by the moving object in the environmental information; For the Doppler feature data in the Doppler wave data, the Doppler feature data corresponding to each moving object is input into the identity discrimination classifier to obtain the identity information and position change information of the target user.
  • the identity recognition unit is further configured to: train the Doppler feature data of different users whose identities are known as the identity discrimination sample to obtain an identity discrimination classifier; and use each acquired Doppler feature data Input the identity discrimination classifier respectively to obtain the user identity corresponding to the sample with the highest correlation in the identity discrimination classifier, and determine the identity information and location change information of the target user.
  • the motion state acquisition unit is further configured to input the Doppler characteristic data of the target user into the motion state classifier model to obtain the motion state of the target user in different time periods.
  • the motion state acquisition unit is further configured to input the target Doppler wave data as a motion state sample into the support vector machine classifier for training to obtain a motion state classifier model.
  • the exercise amount calculation unit is further configured to: obtain the position change information of the target user at different times; determine the target user’s exercise time sum in each exercise state according to the position change information and exercise state of the target user at different times.
  • FIG. 5 is a schematic diagram of the internal structure of an electronic device provided by an embodiment of the disclosure.
  • the electronic device includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus.
  • the memory includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium of the electronic device stores an operating system and may also store a program.
  • the processor can realize the method for measuring the amount of exercise.
  • a program may also be stored in the internal memory, and when the program is executed by the processor, the processor can execute the method for measuring the amount of exercise.
  • the display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen.
  • the input device of the electronic device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the housing of the electronic device. It can be an external keyboard, touchpad, or mouse.
  • the embodiments of the present disclosure can be provided as methods, devices (equipment), or computer program products. Therefore, the present disclosure may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
  • a computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
  • the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

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Abstract

一种运动量测量方法、装置、电子设备及计算机可读存储介质，其中方法包括：通过雷达信号获取目标用户所在区域的环境信息（S11），其中，雷达信号是由毫米波雷达向目标用户发出检测信号后返回的信号；根据目标用户所在区域的环境信息确定目标用户的运动量信息（S12），其中，运动量信息包括目标用户的与运行状态对应的持续时间、与运动状态对应的运动距离；将运动量信息通过终端设备展示给目标用户（S13）。无需人体随身携带可穿戴式测量设备，就能够实现对人体运动量的非接触式测量，且能准确测量出用户的运动量，不会给被测用户带来不便和不舒适的感受。

Description

运动量测量方法、装置及电子设备
本公开要求于2019年12月25日提交中国专利局、申请号为201911358623.5、发明名称为“运动量测量方法、装置及电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及电磁波检测技术领域，尤其涉及一种运动量测量方法、装置及电子设备。
背景技术
随着人们生活水平的提升，越来越重视健康问题，运动量已逐渐成为衡量一个人是否健康的一项重要指标，并且通过检测用户的日常运动量来分析和判断用户的健康水平，已逐渐成为当前辅助医疗的一项重要途径。随着科学技术水平的不断提升，出现了大量便携式和可穿戴式的辅助医疗设备用于测量人体运动量，如：手机、智能手环等。这些设备通过监测人的行走步数、行走时间、睡眠时间等数据来计算其运动量，进而判断其健康状况。然而，便携式或可穿戴式测量设备都采用接触式的测量方式，这种方式需要人随身携带传感器才能够完成测量。因此，在一些不便携带的场景中受到一定的局限，如：分析篮球运动员的场内运动量、在睡眠时会影响睡眠姿态等。
发明内容
为了解决在部分场景中不便于使用可穿戴式测量设备测量用户运动量的技术问题，本公开提供了一种运动量测量方法、装置及电子设备。
第一方面，本公开提供了一种运动量测量方法，包括：
通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号；
根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与运动状态对应的持续时间、与所述运动状态对应的运动距离；
将所述运动量信息通过终端设备展示给目标用户。
在一些实施方式中，所述根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息的步骤包括：
根据所述目标用户所在区域的环境信息确定所述目标用户的身份信息和位置变化信息，其中，所述位置变化信息是所述目标用户从第一位置移动到第二位置的位移及消耗的时间；
采集所述目标用户在不同运动状态下产生的目标多普勒波数据，根据所述目标多普勒波数据确定所述目标用户的运动状态；
根据所述目标用户的位置变化信息和运动状态确定所述目标用户的运动量信息。
在一些实施方式中，所述根据所述目标用户所在区域的环境信息确定所述目标用户的身份信息和位置变化信息的步骤包括：
根据K均值聚类算法确定所述环境信息中的物体及所在位置；
获取所述环境信息中运动的物体产生的多普勒波数据及位置变化信息；
获取所述多普勒波数据中的多普勒特征数据，将每个运动物体对应的所述多普勒特征数据输入身份判别分类器，得到所述目标用户的身份信息和位置变化信息。
在一些实施方式中，所述获取所述多普勒波数据中的多普勒特征数据，将每个运动物体对应的所述多普勒特征数据输入身份判别分类器训练得到所述目标用户的身份信息和位置变化信息的步骤包括：
将身份已知的不同用户的多普勒特征数据作为身份判别样本进行训练，得到所述身份判别分类器；
将获取的每个所述多普勒特征数据分别输入所述身份判别分类器，得到与所述身份判别分类器中相关性最高的样本对应的用户身份，确定所述目标用户的身份信息和位置变化信息。
在一些实施方式中，所述采集所述目标用户在不同运动状态下产生的目标多普勒波数据，根据所述目标多普勒波数据确定所述目标用户的运动状态的步骤包括：
将所述目标用户的多普勒特征数据输入运动状态分类器模型，得到所述目标用户在不同时间段内的运动状态。
在一些实施方式中，在将所述目标用户的多普勒特征数据输入运动状态分类器模型，得到所述目标用户在不同时间段内的运动状态前，所述方法还包括：
将所述目标多普勒波数据作为运动状态样本输入支持向量机分类器进行训练，得到所述运动状态分类器模型。
在一些实施方式中，所述根据所述目标用户的位置变化信息和运动状态确定所述目标用户的运动量信息的步骤包括：
获取所述目标用户在不同时间的位置变化信息；
根据所述目标用户在不同时间的位置变化信息和所述运动状态确定所述目标用户在每个运动状态的运动时间和对应的运动距离，其中所述运动时间为所述目标用户处于对应运动状态的累计时间长度。
第二方面，本公开提供了一种运动量测量装置，包括：
环境信息获取模块，被设置为通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号；
数据处理模块，被设置为根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与所述运动状态对应的持续时间、与运动状态对应的运动距离；
数据展示模块，被设置为将所述运动量信息通过终端设备展示给 目标用户。
另一方面，本公开提供了一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的程序，所述处理器执行所述程序时实现上述方法的步骤。
另一方面，本公开提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述的方法的步骤。
本公开实施例提供的上述技术方案与相关技术相比具有如下优点：
本公开实施例提供的方法和装置，无需人体随身携带可穿戴式测量设备，就能够是实现对人体运动量的非接触式测量，并且能准确测量出用户的运动量，且不会给被测用户带来不便和不舒适的感受，提升用户体验。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开实施例提供的一种运动量测量方法流程图；
图2为本公开实施例提供的一种运动量统计示意图；
图3为本公开实施例提供的另一种运动量统计示意图；
图4为本公开实施例提供的一种运动量测量装置示意图；
图5为本公开实施例提供的一种电子设备内部结构示意图。
具体实施方式
为使本公开实施例的目的、技术方案和优点更加清楚，下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、 完整地描述，显然，所描述的实施例是本公开的一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本公开保护的范围。
图1为本公开实施例提供的一种运动量测量方法流程图，该方法包括：
S11、通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号。
具体地，本公开利用毫米波雷达对某空间范围内的目标进行检测，这里的空间为固定空间，例如室内、篮球场等场所，将毫米波雷达安装在空间的某个位置上，例如可以将毫米波雷达安装在室内的空调内，或安装在篮球场的天花板上，通过毫米波雷达向固定空间发射电磁波信号，通过空间内用户的反射的多普勒波信号可以确定用户的许多信息，且在固定空间特别是室内干扰较小，使用毫米波雷达测量的结果准确度较高。通过毫米波雷达向空间内发射电磁波信号，接触到空间内的所有物体均会产生反射波，毫米波雷达接收这些反射波，并将接收的时间及波形中包含的信息发送给后续的数据分析模块来处理。
使用毫米波雷达获取固定空间内用户的运动量信息，不会对用户产生影响，且获取的信息精度高，测量结果准确。
S12、根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与运动状态对应的持续时间、与所述运动状态对应的运动距离。
具体地，在获取到空间内物体的反射波信息后，采用数字信号滤波器对接收的反射波进行滤波处理，滤除噪声信号和异常点。对滤波后的信号中的数据进行分析，首先需要从信号中寻找到测量运动量的目标用户。
在本实施例中选用K聚类算法，根据返回信号中空间环境内检测 到的点云数据进行聚类，每个类可表示一个物体，且可识别出物体的位置和高度属性，并根据物体的位置变化信息判断出每个物体属于静止的还是运动的。
在获取到每个物体的形态特征后，需要从每个运动物体中识别出用户身份，例如在篮球场地，多个用户在进行篮球比赛，其中运动的物体除了用户外还包括篮球，此时只需要测量用户的运动量信息，而无需测量篮球的运动量信息，所以确定用户身份就至关重要。毫米波雷达通过实时发射和接收毫米波信号，将毫米波中携带的物体相关信息发送到后台进行信息处理，根据粒子滤波跟踪算法对每个运动的物体实时跟踪捕捉，获取每个运动物体的位置信息和物体发出的多普勒波数据，并从多普勒波数据中提取每个运动物体的多普勒特征数据。
在识别用户身份前，事先采集每个用户的多普勒波信息作为样本数据，包括用户的身高、体重、姿态等状态数据，将每个用户的样本数据分别输入相关滤波器(CF)进行训练得到不同的身份判别相关滤波分类器(简称身份判别分类器)，一个用户的样本数据可训练得出一个用于检测该用户身份的身份判别分类器，然后将上述实施例中获取的每个运动物体的多普勒特征数据依次与每个身份判别分类器进行相关性计算，依据身份判别分类器的相关得分或置信度最大原则，获取的每个运动物体的多普勒特征数据与哪个身份判别分类器的相关性最高，就可以判定该运动物体即为与其相关性最高的身份判别分类器对应的用户，即可确定该用户的身份信息，在确定用户的身份信息后，便可确定用户的在不同时间的位置信息、速度信息等。
例如现有三个用户分别提供了各自的样本数据，并经过训练得到了三个身份判别分类器A、B、C，将获取的每个运动物体的多普勒特征数据分别与身份判别分类器A、B、C进行相关性计算，其中第一个运动物体与A、B、C三个身份判别分类器的相关性分别为30％、90％、40％；第二个运动物体与A、B、C三个身份判别分类器的相关性分别为80％、20％、30％；第三个运动物体与A、B、C三个身份判别分类 器的相关性分别为40％、30％、85％，由此可判定，第一个运动物体与身份判别分类器B相关性最高，可判定身份判别分类器B对应的用户身份即为检测的第一个运动物体，同样，身份判别分类器A对应的用户身份即为检测的第一个运动物体，身份判别分类器C对应的用户身份即为检测的第一个运动物体。由此可得到每个用户的身份，从中选择待检测的用户身份作为目标用户身份，得到目标用户身份对应的多普勒特征数据，获取其中在不同时间的位置信息、速度信息等。
在确定了目标用户的身份信息并获取该目标用户的多普勒特征数据后，需要确定目标用户的运动状态和运动时间。
首先采集目标用户在不同运动状态下产生的目标多普勒波数据，由于人在不同运动状态下产生的多普勒图谱不同，将目标多普勒波数据作为训练样本输入支持向量机分类器进行训练，得到运动状态分类器模型，该模型可判别该目标用户的运动状态包括：睡、坐、躺、行走、慢跑、快跑等状态。
需要说明的是，这里的支持向量机分类器也可使用神经网络模型、随机森林、决策树等机器学习模型代替，训练得到可以对用户运动状态进行分类的分类器即可。
在训练得到运动状态分类器模型后，获取目标用户实时的位置信息和多普勒波数据信息，随后将用户实时的多普勒波数据信息输入运动状态分类器模型，得到目标用户实时的运动状态信息，最后根据每种运动状态持续的时间计算目标用户在各个运动状态下的运动距离，其中，任意运动状态下的运动时间是处于该运动状态的时间长度的累加，运动距离则是处于该运动状态的运动距离的累加。例如，目标用户在早晨6-8点进行了慢跑训练，在晚上7-9点也进行了慢跑训练，当运动状态分类器模型识别出用户的慢跑状态后，会计算处于慢跑状态的时间总和，即早晨6-8点两个小时加晚上7-9点的两个小时，共四个小时时间，同时通过在这四个小时中用户的位置变化计算该用户慢跑的距离。
通过毫米波雷达获取的用户信息，经过训练模型计算，可以得到准确的待检测用户的各种运动状态的运动量信息，所需时间短，计算准确度高，使用户可以准确的了解自身的运动量。
S13、将所述运动量信息通过终端设备展示给目标用户。
具体地，在测量得到用户在不同运动状态下的运动量信息后，通过图表的形式对运动量进行统计，图2为本公开实施例提供的一种运动量统计示意图，如图2所示，统计时可以用一天时间为界限统计该用户在一天时间内的运动量，计算出每种运动状态的运动量占一天时间内总运动量的比例，同时存储用户每天的运动量统计图，根据历史统计与当前统计进行比较，对用户提出运动建议，例如用户周一至周五的运动量相差不多，而周六用户运动量有明显变化，躺占的比例增加很多，说明用户将平时运动的时间来睡觉或休息，此时会给用户提供应尽快进行运动的建议。
图3为本公开实施例提供的另一种运动量统计示意图，除了用一天时间为界限统计该用户在一天时间内的运动量，计算出每种运动状态的运动量占一天时间内总运动量的比例外，如图3所示，还可以用用户一天内每种运动状态的运动距离来统计用户的运动量，计算每种运动状态的运动距离占总运动距离的比例，根据健康水平设定用户每天运动量的阈值，检测每种运动状态是否达到阈值水平，若未达标会记录并提醒用户。
此外，还可以以某个时间段为为统计期限进行统计，或者将图2和图3中的百分比数据替换为具体的时间和距离数值。
图2和图3所示的统计图并非呈现给用户的统计图，通过图2和图3进行数据统计后，将用户每种运动状态的运动距离及该运动状态持续的时间段呈现给用户，并根据用户的运动量信息为用户提供运动建议，若用户运动过量会建议用户减少运动量，若用户某一个运动状态持续时间过长，会提醒用户均衡每种运动状态。
通过对用户的运动量做全方位统计，最后将统计结果呈现给用户， 可以使用户清楚知道自身运动情况，以及对后续的运动计划和改善，提高了用户的体验。
图4为本公开实施例提供的一种运动量测量装置示意图，如图4所示，该装置包括：
环境信息获取模块41，被设置为通过雷达信号获取目标用户所在区域的环境信息，其中，雷达信号是由毫米波雷达向目标用户发出检测信号后返回的信号；
数据处理模块42，被设置为根据目标用户所在区域的环境信息确定目标用户的运动量信息，其中，运动量信息包括目标用户的与运动状态对应的持续时间、与运动状态对应的运动距离；
数据展示模块43，被设置为将运动量信息通过终端设备展示给目标用户。
在一些实施方式中，上述装置还包括存储模块，由存储介质和相关电路构成，被设置为存储毫米波雷达的采集的信号和统计的运动量数据。
在一些实施方式中，上述装置还包括通讯模块，是搭载无线传输功能的芯片模块，被设置为传输采集的毫米波雷达信号，并将目标用户运动量数据实时发送给用户终端(手机或电脑)以及云平台服务器。
在一些实施方式中，数据处理模块42包括身份识别单元，被设置为根据目标用户所在区域的环境信息确定目标用户的身份信息和位置变化信息，其中，位置变化信息是目标用户从第一位置移动到第二位置的位移及消耗的时间；还包括运动状态采集单元，被设置为采集目标用户在不同运动状态下产生的目标多普勒波数据，根据目标多普勒波数据确定目标用户的运动状态；运动量计算单元，被设置为根据目标用户的位置变化信息和运动状态确定目标用户的运动量信息。
在一些实施方式中，身份识别单元还被设置为：根据K均值聚类算法确定环境信息中的物体及所在位置；获取环境信息中运动的物体产生的多普勒波数据及位置变化信息；获取多普勒波数据中的多普勒 特征数据，将每个运动物体对应的多普勒特征数据输入身份判别分类器，得到目标用户的身份信息和位置变化信息。
在一些实施方式中，身份识别单元还被设置为：将身份已知的不同用户的多普勒特征数据作为身份判别样本进行训练，得到身份判别分类器；将获取的每个多普勒特征数据分别输入身份判别分类器，得到与身份判别分类器中相关性最高的样本对应的用户身份，确定目标用户的身份信息和位置变化信息。
在一些实施方式中，运动状态采集单元还被设置为将目标用户的多普勒特征数据输入运动状态分类器模型，得到目标用户在不同时间段内的运动状态。
在一些实施方式中，运动状态采集单元还被设置为将目标多普勒波数据作为运动状态样本输入支持向量机分类器进行训练，得到运动状态分类器模型。
在一些实施方式中，运动量计算单元还被设置为：获取目标用户在不同时间的位置变化信息；根据目标用户在不同时间的位置变化信息和运动状态确定目标用户在每个运动状态的运动时间和对应的运动距离，其中运动时间为目标用户处于对应运动状态的累计时间长度。
图5为本公开实施例提供的一种电子设备内部结构示意图。如图5所示，该电子设备包括通过系统总线连接的处理器、存储器、网络接口、输入装置和显示屏。其中，存储器包括非易失性存储介质和内存储器。该电子设备的非易失性存储介质存储有操作系统，还可存储有程序，该程序被处理器执行时，可使得处理器实现运动量测量方法。该内存储器中也可储存有程序，该程序被处理器执行时，可使得处理器执行运动量测量方法。电子设备的显示屏可以是液晶显示屏或者电子墨水显示屏，电子设备的输入装置可以是显示屏上覆盖的触摸层，也可以是电子设备外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域的技术人员应明白，本公开的实施例可提供为方法、装置 (设备)、或计算机程序产品。因此，本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开是参照根据本公开实施例的方法、装置(设备)和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅是本公开的具体实施方式，使本领域技术人员能够理 解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下，在其它实施例中实现。因此，本公开将不会被限制于本文所示的这些实施例，而是要符合与本文所申请的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种运动量测量方法，包括：
    通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号；
    根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与运动状态对应的持续时间、与所述运动状态对应的运动距离；
    将所述运动量信息通过终端设备展示给目标用户。
  2. 根据权利要求1所述的方法，其中，所述根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息的步骤包括：
    根据所述目标用户所在区域的环境信息确定所述目标用户的身份信息和位置变化信息，其中，所述位置变化信息是所述目标用户从第一位置移动到第二位置的位移及消耗的时间；
    采集所述目标用户在不同运动状态下产生的目标多普勒波数据，根据所述目标多普勒波数据确定所述目标用户的运动状态；
    根据所述目标用户的位置变化信息和运动状态确定所述目标用户的运动量信息。
  3. 根据权利要求2所述的方法，其中，所述根据所述目标用户所在区域的环境信息确定所述目标用户的身份信息和位置变化信息的步骤包括：
    根据K均值聚类算法确定所述环境信息中的物体及所在位置；
    获取所述环境信息中运动的物体产生的多普勒波数据及位置变化信息；
    获取所述多普勒波数据中的多普勒特征数据，将每个运动物体对 应的所述多普勒特征数据输入身份判别分类器，得到所述目标用户的身份信息和位置变化信息。
  4. 根据权利要求3所述的方法，其中，所述获取所述多普勒波数据中的多普勒特征数据，将每个运动物体对应的所述多普勒特征数据输入身份判别分类器训练得到所述目标用户的身份信息和位置变化信息的步骤包括：
    将身份已知的不同用户的多普勒特征数据作为身份判别样本进行训练，得到所述身份判别分类器；
    将获取的每个所述多普勒特征数据分别输入所述身份判别分类器，得到与所述身份判别分类器中相关性最高的样本对应的用户身份，确定所述目标用户的身份信息和位置变化信息。
  5. 根据权利要求3所述的方法，其中，所述采集所述目标用户在不同运动状态下产生的目标多普勒波数据，根据所述目标多普勒波数据确定所述目标用户的运动状态的步骤包括：
    将所述目标用户的多普勒特征数据输入运动状态分类器模型，得到所述目标用户在不同时间段内的运动状态。
  6. 根据权利要求5所述的方法，其中，在将所述目标用户的多普勒特征数据输入运动状态分类器模型，得到所述目标用户在不同时间段内的运动状态前，所述方法还包括：
    将所述目标多普勒波数据作为运动状态样本输入支持向量机分类器进行训练，得到所述运动状态分类器模型。
  7. 根据权利要求2所述的方法，其中，所述根据所述目标用户的位置变化信息和运动状态确定所述目标用户的运动量信息的步骤包括：
    获取所述目标用户在不同时间的位置变化信息；
    根据所述目标用户在不同时间的位置变化信息和所述运动状态确定所述目标用户在每个运动状态的运动时间和对应的运动距离，其中所述运动时间为所述目标用户处于对应运动状态的累计时间长度。
  8. 一种运动量测量装置，包括：
    环境信息获取模块，被设置为通过雷达信号获取目标用户所在区域的环境信息，其中，所述雷达信号是由毫米波雷达向所述目标用户发出检测信号后返回的信号；
    数据处理模块，被设置为根据所述目标用户所在区域的环境信息确定所述目标用户的运动量信息，其中，所述运动量信息包括所述目标用户的与运动状态对应的持续时间、与所述运动状态对应的运动距离；
    数据展示模块，被设置为将所述运动量信息通过终端设备展示给目标用户。
  9. 一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的程序，其中，所述处理器执行所述程序时实现权利要求1至7中任一项所述方法的步骤。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现权利要求1至7中任一项所述的方法的步骤。
PCT/CN2020/112930 2019-12-25 2020-09-02 运动量测量方法、装置及电子设备 WO2021128923A1 (zh)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114046560A (zh) * 2021-11-15 2022-02-15 珠海格力电器股份有限公司 一种温度调节方法、装置、供暖设备及供暖系统
CN114242204A (zh) * 2021-12-24 2022-03-25 珠海格力电器股份有限公司 运动策略确定方法及装置
CN115902881A (zh) * 2022-12-29 2023-04-04 中国人民解放军空军预警学院 一种分布式无人机载雷达扩展目标检测方法与系统

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111208508A (zh) * 2019-12-25 2020-05-29 珠海格力电器股份有限公司 运动量测量方法、装置及电子设备
CN113208576A (zh) * 2021-02-01 2021-08-06 安徽华米健康科技有限公司 Pai值计算方法、装置、设备和存储介质
CN113324559B (zh) * 2021-05-10 2023-03-21 青岛海尔空调器有限总公司 一种运动计步方法、装置及空气处理设备
CN117524413B (zh) * 2024-01-05 2024-03-19 亿慧云智能科技（深圳）股份有限公司 基于毫米波雷达的运动保护方法、装置、设备及介质

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104076357A (zh) * 2014-07-07 2014-10-01 武汉拓宝电子系统有限公司 一种用于室内活动目标检测的雷达装置及方法
JP5623253B2 (ja) * 2010-11-25 2014-11-12 三菱電機株式会社 多目標追尾装置
CN108700645A (zh) * 2016-05-13 2018-10-23 谷歌有限责任公司 用于随智能设备利用雷达的系统、方法和设备
CN109557535A (zh) * 2017-09-26 2019-04-02 英飞凌科技股份有限公司 用于使用毫米波雷达传感器的占用检测的系统和方法
CN110118966A (zh) * 2019-05-28 2019-08-13 长沙莫之比智能科技有限公司 基于毫米波雷达的人员检测与计数系统
CN110488264A (zh) * 2019-07-05 2019-11-22 珠海格力电器股份有限公司 人员检测方法、装置、电子设备及存储介质
CN110609281A (zh) * 2019-08-23 2019-12-24 珠海格力电器股份有限公司 一种区域检测方法及装置
CN111208508A (zh) * 2019-12-25 2020-05-29 珠海格力电器股份有限公司 运动量测量方法、装置及电子设备

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5724976B2 (ja) * 2012-09-20 2015-05-27 カシオ計算機株式会社 運動情報検出装置および運動情報検出方法、運動情報検出プログラム
US9087234B2 (en) * 2013-03-15 2015-07-21 Nike, Inc. Monitoring fitness using a mobile device
US10354551B2 (en) * 2014-04-09 2019-07-16 Lg Electronics Inc. Mobile terminal and method for controlling the same
CN106267774B (zh) * 2015-05-25 2019-05-24 腾讯科技（深圳）有限公司 运动状态识别方法和装置
CN105403228B (zh) * 2015-12-18 2019-01-29 北京乐动力网络科技有限公司 一种运动距离的确定方法和装置
US10914834B2 (en) * 2017-05-10 2021-02-09 Google Llc Low-power radar
EP3425419B1 (en) * 2017-07-05 2024-01-03 Stichting IMEC Nederland A method and a system for localization and monitoring of living beings
CN109276254A (zh) * 2018-11-16 2019-01-29 深圳还是威健康科技有限公司 一种运动状态表征方法、装置及相关设备
CN109581361A (zh) * 2018-11-22 2019-04-05 九牧厨卫股份有限公司 一种检测方法、检测装置、终端以及检测系统
CN109709546B (zh) * 2019-01-14 2021-11-16 珠海格力电器股份有限公司 宠物状态监测方法和装置
CN109765539B (zh) * 2019-01-28 2021-06-04 珠海格力电器股份有限公司 室内用户行为监测方法和装置、电器设备和家居监控系统

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5623253B2 (ja) * 2010-11-25 2014-11-12 三菱電機株式会社 多目標追尾装置
CN104076357A (zh) * 2014-07-07 2014-10-01 武汉拓宝电子系统有限公司 一种用于室内活动目标检测的雷达装置及方法
CN108700645A (zh) * 2016-05-13 2018-10-23 谷歌有限责任公司 用于随智能设备利用雷达的系统、方法和设备
CN109557535A (zh) * 2017-09-26 2019-04-02 英飞凌科技股份有限公司 用于使用毫米波雷达传感器的占用检测的系统和方法
CN110118966A (zh) * 2019-05-28 2019-08-13 长沙莫之比智能科技有限公司 基于毫米波雷达的人员检测与计数系统
CN110488264A (zh) * 2019-07-05 2019-11-22 珠海格力电器股份有限公司 人员检测方法、装置、电子设备及存储介质
CN110609281A (zh) * 2019-08-23 2019-12-24 珠海格力电器股份有限公司 一种区域检测方法及装置
CN111208508A (zh) * 2019-12-25 2020-05-29 珠海格力电器股份有限公司 运动量测量方法、装置及电子设备

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114046560A (zh) * 2021-11-15 2022-02-15 珠海格力电器股份有限公司 一种温度调节方法、装置、供暖设备及供暖系统
CN114242204A (zh) * 2021-12-24 2022-03-25 珠海格力电器股份有限公司 运动策略确定方法及装置
CN115902881A (zh) * 2022-12-29 2023-04-04 中国人民解放军空军预警学院 一种分布式无人机载雷达扩展目标检测方法与系统
CN115902881B (zh) * 2022-12-29 2024-03-29 中国人民解放军空军预警学院 一种分布式无人机载雷达扩展目标检测方法与系统

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