Personalized step length online estimation method and device, mobile terminal and storage medium

Abstract

The invention relates to the technical field of indoor positioning, in particular to a personalized step length online estimation method and device, a mobile terminal and a storage medium, and the method comprises the steps: obtaining the smooth acceleration and angular velocity of each walking step collected by the mobile terminal of a user; inputting the smooth acceleration of each walking step into a step length model to obtain a first distance; identifying the motion state of each walking step according to the smooth acceleration and the angular velocity of each walking step, and aligning the sampling time of the mobile terminal and the external positioning source according to the motion state; and calculating a second distance according to a positioning result of the external positioning source, estimating a new empirical parameter of the step length model according to the first distance and the second distance after time alignment, and estimating the step length of the user based on the step length model of the new empirical parameter. Therefore, the problem that the track plotting distance cannot be accurately estimated due to diversity and difference of pedestrian walking in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of indoor positioning technology, and in particular to a personalized step length online estimation method, device, mobile terminal and storage medium. Background Art

[0002] As an important part of the Internet of Things, mobile phone-based indoor positioning technology has been applied in people's daily lives, such as indoor public transportation and shopping navigation in shopping malls. PDR (Pedestrian Dead Reckoning) can provide autonomous and continuous mobile measurements and has become one of the main technologies for mobile phone indoor positioning. It is mainly based on the periodic characteristics of pedestrian walking for positioning, and mainly includes three steps: step detection, step length estimation, and heading estimation. Unlike traditional strapdown inertial navigation, it does not obtain the position by integrating the accelerometer measurement value twice, and is more suitable for low-precision IMU (Inertial Measurement Unit).

[0003] However, the diversity and differences in pedestrian walking make it impossible for the step length model to accurately estimate the distance of track calculation, which will affect the accuracy and robustness of PDR positioning.

[0004] In order to improve the accuracy and robustness of PDR positioning, smartphone-based indoor positioning solutions integrate multiple sensors to enhance the accuracy and robustness of positioning. In a typical multi-sensor fusion system, the correction of PDR positioning by other sensors usually focuses on directly adjusting the trajectory rather than improving the accuracy of step length estimation in the PDR algorithm, and therefore does not solve the above problems. Summary of the invention

[0005] The present application provides a personalized stride length online estimation method, device, mobile terminal and storage medium to solve the problem in related technologies that the distance of track calculation cannot be accurately estimated due to the diversity and differences in pedestrian walking.

[0006] The first aspect of the present application provides a personalized stride length online estimation method, comprising the following steps: acquiring the smoothed acceleration and angular velocity of each walking step collected by a user's mobile terminal; inputting the smoothed acceleration of each walking step into a stride length model to obtain a first distance; identifying the motion state of each walking step according to the smoothed acceleration and angular velocity of each walking step, and aligning the sampling time of the mobile terminal and an external positioning source according to the motion state; calculating the second distance according to the positioning result of the external positioning source, estimating new empirical parameters of the stride length model according to the first distance and the second distance after the alignment time, and estimating the user's stride length based on the step length model with the new empirical parameters.

[0007] Optionally, the smoothed acceleration and angular velocity of each walking step collected by the user's mobile terminal are obtained, including: using the built-in three-axis gyroscope and three-axis acceleration data of the mobile terminal to detect the gravity acceleration in the target dynamic state, and calculating the angular velocity based on the gravity acceleration; using a sliding window to smooth the acceleration, using a step frequency detection method to detect the pedestrian's walking step moment, and recording the smoothed acceleration within each walking step moment.

[0008] Optionally, the smoothed acceleration is calculated as:

[0009]

[0010] Where, t represents the sampling time; Indicates the smooth acceleration amplitude; a t represents the acceleration amplitude at time t, It represents the original measurement value of the three-axis acceleration; g represents the value of gravity acceleration, N represents the number of samples in the static time; l represents the sliding window length.

[0011] Optionally, the step size model is formulated as:

[0012]

[0013] Among them, f k represents the step frequency of the kth step, acc max,k Indicates the maximum acceleration of the kth step, acc min,k They represent the minimum acceleration of the kth step, and K represents the empirical parameter.

[0014] Optionally, the expression formula of the motion state of each walking step is:

[0015]

[0016] Among them, std acc represents the standard deviation of acceleration; ω t Th represents the angular velocity at time t; std Indicates the acceleration standard deviation threshold; Th st Indicates the threshold for transition between straight and non-straight states.

[0017] Optionally, the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state, including: recording the start positioning result and the corresponding time of the external positioning source; if it is identified that the positioning result of the external positioning source is no longer received, or the motion state is non-linear, recording the last received end positioning result and the corresponding time; based on the corresponding time of the start positioning result and the end positioning result, aligning the sampling time of the mobile terminal and the external positioning source.

[0018] Optionally, the calculation formula of the new empirical parameter is:

[0019]

[0020]

[0021] Among them, D HP is the second distance; D PDR is the first distance; K 0 Represents the default preset parameters in the step size model; and They represent the first and last high-precision positioning source results used for parameter estimation respectively; T 0 and T 1 Represents T s The start and end time of the step; SL 1 Indicates T s The step length of the step; d 1 Indicates T s to T 1 Distance; T n-1 and T n Represents T e The start and end time of the step; SL n Indicates T e The step length of the step; d n Indicates T e to T n distance.

[0022] The second aspect of the present application provides a personalized stride length online estimation device, including: an acquisition module, used to acquire the smoothed acceleration and angular velocity of each walking step collected by a user's mobile terminal; an input module, used to input the smoothed acceleration of each walking step into a stride length model to obtain a first distance; an identification module, used to identify the motion state of each walking step according to the smoothed acceleration and angular velocity of each walking step, and align the sampling time of the mobile terminal and the external positioning source according to the motion state; a calculation module, used to calculate the second distance according to the positioning result of the external positioning source, estimate new empirical parameters of the stride length model according to the first distance and the second distance after the alignment time, and estimate the user's stride length based on the step length model of the new empirical parameters.

[0023] The third aspect of the present application provides a mobile terminal, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the personalized step length online estimation method of the first aspect.

[0024] The fourth aspect of the present application provides a computer-readable storage medium having a computer program or instructions stored thereon. When the computer program or instructions are executed, the personalized step length online estimation method of the first aspect is implemented.

[0025] Therefore, this application includes the following beneficial effects:

[0026] The embodiment of the present application utilizes the user's mobile terminal to collect the smooth acceleration and angular velocity of each walking step and input the step length model to obtain the first distance, identify the motion state of each walking step according to the smooth acceleration and angular velocity of each walking step, align the sampling time of the mobile terminal and the external positioning source according to the motion state, and then calculate the second distance according to the positioning result of the external positioning source, estimate the new empirical parameters of the step length model from the first distance and the second distance, and estimate the user's step length, and propose an adaptive online estimation basis for personalized step lengths of different pedestrians. Thus, the problem that the related technology cannot accurately estimate the distance of track calculation due to the diversity and differences of pedestrians' walking is solved.

[0027] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0029] Figure 1 A flowchart of a personalized step length online estimation method provided according to an embodiment of the present application;

[0030] Figure 2 A flowchart of a personalized step length online estimation method provided according to an embodiment of the present application;

[0031] Figure 3 A schematic diagram of online estimation of step size model parameters using a high-precision positioning source according to an embodiment of the present application;

[0032] Figure 4 This is an example diagram of a personalized step length online estimation device according to an embodiment of the present application;

[0033] Figure 5 A schematic diagram of the structure of a mobile terminal provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0034] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following describes the personalized step length online estimation method, device, mobile terminal and storage medium of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the diversity and difference of pedestrian walking mentioned in the above background technology make the related technology unable to accurately estimate the distance of track calculation, the present application provides a personalized step length online estimation method, in which the user's mobile terminal is used to collect the smooth acceleration and angular velocity of each walking step and input the step length model to obtain a first distance, and the motion state of each walking step is identified according to the smooth acceleration and angular velocity of each walking step, and the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state, and then the second distance is calculated according to the positioning result of the external positioning source. The new empirical parameters of the step length model are estimated from the first distance and the second distance to estimate the user's step length, and a personalized step length online estimation basis that is adaptive to different pedestrians is proposed. Thus, the problem that the related technology cannot accurately estimate the distance of track calculation due to the diversity and difference of pedestrian walking is solved.

[0036] Specifically, Figure 1 A flowchart of a personalized step length online estimation method provided in an embodiment of the present application.

[0037] like Figure 1 As shown, the personalized step length online estimation method includes the following steps:

[0038] In step S101, the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal are obtained.

[0039] Among them, the mobile terminal can be a mobile phone, a smart watch and other devices.

[0040] In an embodiment of the present application, the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal are obtained, including: using the built-in three-axis gyroscope and three-axis acceleration data of the mobile terminal to detect the gravity acceleration in the dynamic state of the target, and calculating the angular velocity based on the gravity acceleration; using a sliding window to smooth the acceleration, using a step frequency detection method to detect the moment of the pedestrian's walking steps, and recording the smooth acceleration within each walking step moment.

[0041] Among them, the three-axis gyroscope is a sensor used to measure the rotation speed of an object around three mutually perpendicular axes; the three-axis accelerometer is a sensor used to measure linear acceleration along three mutually perpendicular axes; the sliding window is a data processing technology that smoothes data by operating on a fixed length of data in a continuous data stream; the cadence detection method is an algorithm used to identify the pace of pedestrians while walking.

[0042] It can be understood that the embodiments of the present application can use the three-axis gyroscope and three-axis accelerometer inside the mobile terminal to collect the smooth acceleration and angular velocity of each walking step of the user, detect the direction of gravity acceleration by analyzing the data, and then calculate the angular velocity, and use the sliding window method to smooth the acceleration data to improve the data quality. Finally, combined with the step frequency detection technology, each walking step of the pedestrian can be accurately detected, and the smooth acceleration during each step can be recorded.

[0043] Specifically, the smooth acceleration is calculated through the sampling time, sliding window length, acceleration amplitude at time t, gravity acceleration value and number of samples in the static time. The calculation formula of the smooth acceleration is:

[0044]

[0045] Where, t represents the sampling time; Indicates the smooth acceleration amplitude; a t represents the acceleration amplitude at time t, It represents the original measurement value of the three-axis acceleration; g represents the value of gravity acceleration, N represents the number of samples in the static time; l represents the sliding window length.

[0046] In step S102, the smooth acceleration of each walking step is input into a step length model to obtain a first distance.

[0047] Among them, the step length model is established based on the smoothed acceleration data of each walking step and the step frequency of each step. It is more sensitive to the walking speed of pedestrians. The calculation formula of the step length model will be described in detail below and will not be repeated here.

[0048] It can be understood that the embodiments of the present application can combine the smooth acceleration data of each walking step and add the step frequency of each step to establish a stride length model, so as to achieve the purpose of being more sensitive to the walking speed of pedestrians, and input the smooth acceleration of each walking step into the stride length model to obtain the first distance.

[0049] In the embodiment of the present application, the formula of the step length model is:

[0050]

[0051] Among them, f k represents the step frequency of the kth step, acc max,k Indicates the maximum acceleration of the kth step, acc min,k They represent the minimum acceleration of the kth step, and K represents the empirical parameter.

[0052] It can be understood that the stride length model of the embodiment of the present application improves the establishment of the above formula, and the first distance can be calculated using the step frequency of the kth step, the maximum acceleration and the minimum acceleration of the kth step, and empirical parameters.

[0053] In step S103, the motion state of each walking step is identified according to the smooth acceleration and angular velocity of each walking step, and the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state.

[0054] The motion state of each walking step includes stationary, straight and non-straight states; the method of aligning the sampling time of the mobile terminal and the external positioning source according to the motion state will be described in detail below and will not be repeated here.

[0055] It can be understood that the embodiments of the present application can identify the motion state of each walking step through the smooth acceleration and angular velocity of each walking step. The motion state of each walking step includes stationary, straight-moving and non-straight-moving states. After obtaining the motion state of each walking step, the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state.

[0056] In the embodiment of the present application, the expression formula of the motion state of each walking step is:

[0057]

[0058] Among them, std acc represents the standard deviation of acceleration; ω t Th represents the angular velocity at time t; std Indicates the acceleration standard deviation threshold; Th st Indicates the threshold for transition between straight and non-straight states.

[0059] It can be understood that the embodiment of the present application can determine the motion state of each walking step through the above formula according to the acceleration standard deviation, angular velocity, acceleration standard deviation threshold and the threshold for switching between straight and non-straight states.

[0060] In an embodiment of the present application, the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state, including: recording the start positioning result and the corresponding time of the external positioning source; if it is identified that the positioning result of the external positioning source is no longer received, or when the motion state is non-linear, recording the last received end positioning result and the corresponding time; based on the corresponding time of the start positioning result and the end positioning result, aligning the sampling time of the mobile terminal and the external positioning source.

[0061] Among them, external positioning sources: systems or devices that provide geographic location information, such as GPS satellites, Wi-Fi networks, or Bluetooth beacons.

[0062] It can be understood that the embodiment of the present application first records the start positioning result and its corresponding time received by the mobile terminal from the external positioning source as the starting point of synchronization. When the positioning result is no longer received or non-linear motion is detected, the last received end positioning result and the corresponding time are recorded, and the timestamps of the two positioning results are compared. The time axis of the sensor data inside the mobile terminal can be aligned to keep it consistent in time with the data of the external positioning source.

[0063] In step S104, the second distance is calculated according to the positioning result of the external positioning source, new empirical parameters of the step length model are estimated according to the first distance and the second distance after the alignment time, and the step length model based on the new empirical parameters estimates the user's step length.

[0064] Among them, the second distance is the distance measured by the external positioning source within the same time period of this straight line, calculated based on the positioning results of the external positioning source and the start and end positioning results and their corresponding times received by the mobile terminal from the external positioning source; the estimation method of the new empirical parameters will be described in detail below and will not be repeated here.

[0065] It can be understood that the embodiment of the present application can calculate the second distance measured by the external positioning source within the same time period of this straight line according to the positioning result of the external positioning source and the start and end positioning results and their corresponding times received by the mobile terminal from the external positioning source, and combine it with the first distance output by the above-mentioned step length model to estimate the new empirical parameters of the step length model, and estimate the user's step length based on the step length model of the new empirical parameters.

[0066] In the embodiment of the present application, the calculation formula of the new empirical parameter is:

[0067]

[0068] Among them, D HP is the second distance; D PDR is the first distance; K 0 Represents the default preset parameters in the step size model; and They represent the first and last high-precision positioning source results used for parameter estimation respectively; T 0 and T 1 Represents T s The start and end time of the step; SL 1 Indicates T s The step length of the step; d 1 Indicates T s to T 1 Distance; T n-1 and T n Represents T e The start and end time of the step; SLn Indicates T e The step length of the step; d n Indicates T e to T n distance.

[0069] According to the personalized stride length online estimation method proposed in the embodiment of the present application, the user's mobile terminal is used to collect the smooth acceleration and angular velocity of each walking step and input them into the stride length model to obtain a first distance, the motion state of each walking step is identified according to the smooth acceleration and angular velocity of each walking step, the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state, and then the second distance is calculated according to the positioning result of the external positioning source, and new empirical parameters of the stride length model are estimated from the first distance and the second distance to estimate the user's stride length, and a basis for online estimation of personalized stride length that is adaptive to different pedestrians is proposed.

[0070] The personalized step length online estimation method is further described below through a specific embodiment. Figure 2 As shown, the method comprises the following steps:

[0071] Step S201, mobile phone built-in IMU data.

[0072] Combining the built-in three-axis gyroscope and three-axis acceleration data of the mobile phone, the gravity acceleration is detected under low dynamic conditions to obtain the angular velocity measurement value.

[0073] Specifically: Combined with the built-in three-axis acceleration data of the mobile phone, the acceleration amplitude result is obtained, as shown in Formula 1; based on the acceleration data within a period of time under low dynamics, the gravity acceleration value is obtained, as shown in Formula 2; after removing gravity from the result of Formula 1, sliding window smoothing is performed to suppress noise, and smoothed acceleration data is obtained, as shown in Formula 3.

[0074]

[0075]

[0076] Where t represents the sampling time, a x,t ,a y,t ,a z,t Represents the original measurement value of the three-axis acceleration, a t represents the acceleration amplitude at time t, g represents the gravity acceleration value, N represents the number of samples in the static time, l represents the sliding window length, Indicates the smoothed acceleration amplitude.

[0077] Step S202: data smoothing processing.

[0078] The sliding window is used to smooth the acceleration data. The step frequency detection method is used to detect the walking steps of pedestrians and record the smooth acceleration data in each step.

[0079] Step S203: step status recognition.

[0080] The motion state of each step is identified based on the smoothed acceleration and angular velocity data, and the state sliding window is used to detect the actual step state transition.

[0081] Specifically, the motion state at each sampling moment is identified based on the smoothed acceleration and angular velocity data, as shown in Formula 5. The state sliding window is used to detect the real motion state transition to avoid state misjudgment caused by adverse actions during walking.

[0082] Combined with the motion state at each moment in a step, the state with the largest proportion is determined as the motion state of the step.

[0083]

[0084] Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std represents the acceleration standard deviation threshold, T st Indicates the threshold for transition between straight and non-straight states.

[0085] Step S204: step length model.

[0086] Combining the smooth acceleration data within each step and the corresponding moment, a step length model that can adapt to different speeds is proposed. Specifically, the smooth acceleration data within the step obtained by the cadence detection method is combined, and the cadence of each step is considered to be added when the step length model is established, so that it is more sensitive to the pedestrian's walking speed and can adapt to different speeds, as shown in Formula 4.

[0087]

[0088] Among them, f k represents the step frequency of the kth step, acc max,k ,acc min,k They represent the maximum acceleration and the minimum acceleration of the kth step respectively. K represents the empirical parameter, which is also the parameter estimated online in this method.

[0089] Step S205: high-precision positioning source.

[0090] Combined with the step status, obtain the positioning results and corresponding time of the external high-precision positioning source for correction, and find the corresponding sampling time of the IMU through time alignment;

[0091] Specifically, combined with the step state obtained in step S203, such as Figure 3 As shown in the figure, when a pedestrian is detected walking straight and the positioning result of an external high-precision positioning source is obtained, the first positioning result of the high-precision positioning source and the corresponding time are recorded. As a start; when no high-precision positioning source results are received or the pace state is non-linear, the last received high-precision positioning result and the corresponding time Record it as the end. Since the sampling rates of the high-precision sensor and IMU may be different, the closest sampling time T of the IMU can be found by time alignment based on the corresponding time in the start and end markers. s and T e .

[0092] Step S206: Parameter online estimation.

[0093] The high-precision positioning source results and PDR estimated distance from the start time to the end time are calculated respectively, and the parameters in the PDR step length model are estimated online according to the distance between the two, so as to realize personalized step length estimation that can adapt to different pedestrians.

[0094] Specifically, the distance D measured by the high-precision positioning source in the same time period of this straight line is calculated by combining the high-precision positioning source result obtained in step S205 and the start and end times corresponding to the IMU. HP and the distance D calculated by the step length model in PDR PDR , where the parameter K in the step size model is the default parameter. HP and D PDR , the new parameter K can be estimated online estimate , as shown in Formula 6-10.

[0095]

[0096] Among them, T 0 and T 1 Represents T s The start and end time of the step, SL 1 Indicates T s The step length of the current step, d 1 Indicates T s to T 1 Distance, T n-1 and T n Represents T e The start and end time of the step, SL n Indicates T e The step length of the current step, d n Indicates T e to T n distance.

[0097]

[0098]

[0099] Among them, K 0 represents the default preset parameters in the step size model, and Respectively represent the first and last high-precision positioning source results used for parameter estimation.

[0100] The embodiments of the present application can utilize information with common attributes from various high-precision positioning sources to perform online correction on the step size model, aiming to achieve parameter personalization and enhance the adaptability of the PDR positioning system, and can ensure that no matter what kind of fusion system is used, the use of any high-precision positioning source will help improve the PDR positioning system.

[0101] In summary, the embodiments of the present application can combine the built-in IMU data measurement values ​​of the mobile phone to obtain the smoothed acceleration data after removing gravity, and use the step frequency detection method to detect the pace; secondly, based on the smoothed acceleration and angular velocity data in each step, the step state is identified in combination with the state transition sliding window; finally, combined with the step state, according to the distance measured by the high-precision positioning source and the distance calculated by the PDR step length model, the step length model parameters are estimated and updated, so that it can adapt to different pedestrians to achieve personalized step length estimation.

[0102] Next, a personalized step length online estimation device proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0103] Figure 4 It is a block diagram of a personalized step length online estimation device according to an embodiment of the present application.

[0104] like Figure 4 As shown, the personalized step length online estimation device 10 includes: an acquisition module 301 , an input module 302 , a recognition module 303 and a calculation module 304 .

[0105] Among them, the acquisition module 301 is used to obtain the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal; the input module 302 is used to input the smooth acceleration of each walking step into the step length model to obtain the first distance; the identification module 303 is used to identify the motion state of each walking step according to the smooth acceleration and angular velocity of each walking step, and align the sampling time of the mobile terminal and the external positioning source according to the motion state; the calculation module 304 is used to calculate the second distance according to the positioning result of the external positioning source, estimate the new empirical parameters of the step length model according to the first distance and the second distance after the alignment time, and estimate the user's step length based on the step length model of the new empirical parameters.

[0106] In the embodiment of the present application, the acquisition module 301 is further used to: utilize the built-in three-axis gyroscope and three-axis acceleration data of the mobile terminal to detect the gravity acceleration in the dynamic state of the target, and calculate the angular velocity based on the gravity acceleration; use the sliding window to smooth the acceleration, use the step frequency detection method to detect the pedestrian's walking steps, and record the smoothed acceleration within each walking step.

[0107] In the embodiment of the present application, the calculation formula of the smooth acceleration is:

[0108]

[0109] Where, t represents the sampling time; Indicates the smooth acceleration amplitude; a t represents the acceleration amplitude at time t, It represents the original measurement value of the three-axis acceleration; g represents the value of gravity acceleration, N represents the number of samples in the static time; l represents the sliding window length.

[0110] In the embodiment of the present application, the formula of the step length model is:

[0111]

[0112] Among them, f k represents the step frequency of the kth step; acc max,k ,acc min,k They represent the maximum acceleration and the minimum acceleration of the kth step respectively; K represents the empirical parameter.

[0113] In the embodiment of the present application, the expression formula of the motion state of each walking step is:

[0114]

[0115] Among them, std acc represents the standard deviation of acceleration; ω t Th represents the angular velocity at time t; std Indicates the acceleration standard deviation threshold; Th st Indicates the threshold for transition between straight and non-straight states.

[0116] In the embodiment of the present application, the calculation module 304 is further used to: record the start positioning result and the corresponding time of the external positioning source; if it is identified that the positioning result of the external positioning source is no longer received, or the motion state is non-linear, record the last received end positioning result and the corresponding time; based on the corresponding time of the start positioning result and the end positioning result, align the sampling time of the mobile terminal and the external positioning source.

[0117] In the embodiment of the present application, the calculation formula of the new empirical parameter is:

[0118]

[0119] Among them, D HP is the second distance; D PDR is the first distance; K 0 Represents the default preset parameters in the step size model; and They represent the first and last high-precision positioning source results used for parameter estimation respectively; T 0 and T 1 Represents T s The start and end time of the step; SL 1 Indicates T s The step length of the step; d 1 Indicates T s to T 1 Distance; T n-1 and T n Represents T e The start and end time of the step; SL n Indicates T e The step length of the step; d n Indicates T e to T n distance.

[0120] It should be noted that the above explanation of the embodiment of the personalized step length online estimation method is also applicable to the personalized step length online estimation device of this embodiment, and will not be repeated here.

[0121] According to the personalized stride length online estimation device proposed in the embodiment of the present application, through the coordinated action of the acquisition module, the input module, the identification module and the calculation module, the user's mobile terminal can be used to collect the smooth acceleration and angular velocity of each walking step and input the step length model to obtain the first distance, the motion state of each walking step is identified according to the smooth acceleration and angular velocity of each walking step, the sampling time of the mobile terminal and the external positioning source are aligned according to the motion state, and then the second distance is calculated according to the positioning result of the external positioning source, and the new empirical parameters of the step length model are estimated from the first distance and the second distance to estimate the user's stride length, and a basis for adaptive online estimation of personalized stride length for different pedestrians is proposed.

[0122] Figure 5 A schematic diagram of the structure of a mobile terminal provided in an embodiment of the present application. The mobile terminal may include:

[0123] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0124] When the processor 402 executes the program, the personalized step length online estimation method provided in the above embodiment is implemented.

[0125] Furthermore, the mobile terminal also includes:

[0126] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0127] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0128] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0129] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0130] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0131] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0132] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned personalized step length online estimation method.

[0133] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0134] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0135] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0136] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0137] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0138] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A personalized step length online estimation method, characterized in that: The following steps are involved: Obtain the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal; Inputting the smooth acceleration of each walking step into the step length model to obtain a first distance; Identifying the motion state of each walking step according to the smooth acceleration and angular velocity of each walking step, and aligning the sampling time of the mobile terminal and the external positioning source according to the motion state; The second distance is calculated according to the positioning result of the external positioning source, new empirical parameters of the step length model are estimated according to the first distance and the second distance after the alignment time, and the step length model based on the new empirical parameters estimates the step length of the user.

2. The personalized step length online estimation method according to claim 1, characterized in that: The step of acquiring the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal includes: Using the built-in three-axis gyroscope and three-axis acceleration data of the mobile terminal, the gravity acceleration is detected in the dynamic state of the target, and the angular velocity is calculated according to the gravity acceleration; The sliding window is used to smooth the acceleration, the step frequency detection method is used to detect the pedestrian's walking steps, and the smooth acceleration within each walking step is recorded.

3. The personalized step length online estimation method according to claim 1, characterized in that: The calculation formula of the smooth acceleration is: Where, t represents the sampling time; Indicates the smooth acceleration amplitude; a t represents the acceleration amplitude at time t, a x,t ,a y,t ,a z,t It represents the original measurement value of the three-axis acceleration; g represents the value of gravity acceleration, N represents the number of samples in the static time; l represents the sliding window length.

4. The personalized step length online estimation method according to claim 1, characterized in that: The formula for the step size model is: Among them, f k represents the step frequency of the kth step, acc max,k Indicates the maximum acceleration of the kth step, acc min,k They represent the minimum acceleration of the kth step, and K represents the empirical parameter.

5. The personalized step length online estimation method according to claim 1, characterized in that: The expression formula of the motion state of each walking step is: Among them, std acc represents the standard deviation of acceleration; ω t Th represents the angular velocity at time t; std Indicates the acceleration standard deviation threshold; Th st Indicates the threshold for transition between straight and non-straight states.

6. The personalized step length online estimation method according to claim 1, characterized in that: The aligning the sampling time of the mobile terminal and the external positioning source according to the motion state includes: Recording the start positioning result and corresponding time of the external positioning source; If it is identified that the positioning result of the external positioning source is no longer received, or the motion state is non-linear, the last received end positioning result and the corresponding time are recorded; Based on the corresponding time of the start positioning result and the end positioning result, the sampling time of the mobile terminal and the external positioning source are aligned.

7. The personalized step length online estimation method according to claim 1, characterized in that: The calculation formula of the new empirical parameter is: Among them, D HP is the second distance; D PDR is the first distance; K0 represents the default preset parameter in the step length model; and They represent the first and last high-precision positioning source results used for parameter estimation; T0 and T1 represent T s The start and end time of the step; SL1 represents T s The step length of the current step; d1 represents T s Distance to T1; T n-1 and T n Represents T e The start and end time of the step; SL n Indicates T e The step length of the step; d n Indicates T e to T n distance.

8. A personalized step length online estimation device, characterized in that: include: An acquisition module, used to acquire the smooth acceleration and angular velocity of each walking step collected by the user's mobile terminal; An input module, used for inputting the smooth acceleration of each walking step into a step length model to obtain a first distance; an identification module, configured to identify the motion state of each walking step according to the smooth acceleration and angular velocity of each walking step, and align the sampling time of the mobile terminal and the external positioning source according to the motion state; A calculation module is used to calculate the second distance according to the positioning result of the external positioning source, estimate the new empirical parameters of the step length model according to the first distance and the second distance after the alignment time, and estimate the step length of the user based on the step length model of the new empirical parameters.

9. A mobile terminal, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the personalized step length online estimation method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the personalized step length online estimation method according to any one of claims 1 to 7 is implemented.

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