Walking speed estimation device operation method, walking speed estimation device, and recording medium

Abstract

According to one embodiment, a walking speed estimation method is provided that includes the steps of determining N subjects to be measured for walking speed measurement and acquiring physical data of the subjects, conducting a clinical evaluation on the subjects, conducting a gait evaluation on the subjects to acquire measured gait data, and developing a walking speed estimation model having a plurality of walking parameters based on the measured gait data, wherein the walking speed estimation model includes, as walking parameters, a roll angle, a yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the body of the subjects.

Description

[Technical Field]

[0001] The following embodiments relate to a walking speed estimation method and device. [Background technology]

[0002] Human gait changes due to muscle and sensory degeneration. Elderly people may have various age-related issues that alter their gait to some degree. Previous studies have focused on human kinematics and muscle weakening as the main focus of stride length changes. People with more degenerative changes may experience shorter leg length because their walking efficiency decreases the longer they walk. Research has also shown that both stride length and body weight exhibit a curvilinear relationship with walking speed.

[0003] Gait speed is associated with aging, disability, falls, cognitive decline, and mortality. However, in most previous studies, walking speed was measured manually using a stopwatch, resulting in human error and limited accuracy. This has led to the development of laboratory-based motion capture systems and instrumented walkways, which provide more accurate and reliable estimates of walking speed than manual measurements using a stopwatch. However, laboratory-based motion capture systems and instrumented walkways have the disadvantages of only being able to measure a limited number of steps due to spatial constraints and of being expensive to implement.

[0004] To overcome these spatial and cost limitations, walking speed can be estimated using wearable accelerometers, which are cheaper and less spatially constrained than motion capture systems or instrumented walkways, and less sensitive to human error than stopwatches. However, wearable accelerometers indirectly estimate walking speed through direct integration of acceleration, kinematic modeling and gait correction, data regression modeling, or hybrid methods, without directly measuring it. This means that the accuracy of walking speed estimated by inertial sensors is low. Summary of the Invention [Problem to be solved by the invention]

[0005] To provide a walking speed estimation method and device capable of accurately estimating walking speed and diagnosing aging and related risks.

[0006] The problems to be solved by the present invention are not limited to those described above, and other unmentioned problems and advantages of the present invention can be understood from the following description and will be more clearly understood by the embodiments of the present invention. Furthermore, it will be understood that the problems to be solved and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims. [Means for solving the problem]

[0007] According to one embodiment of the present invention, there is provided a walking speed estimation method including the steps of determining N subjects whose walking speeds will be measured and acquiring physical data of the subjects, acquiring data on the results of a clinical evaluation of the subjects, conducting a gait evaluation of the subjects to acquire measured gait data, and developing a walking speed estimation model having a plurality of gait parameters based on the measured gait data, wherein the walking speed estimation model includes, as gait parameters, roll angle, yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the subjects' bodies.

[0008] In the present invention, determining the N subjects can be determined by excluding people in the population who have a disease that may affect walking or whose balance ability assessment score is below a predetermined score.

[0009] In the present invention, the clinical assessment may be to diagnose a mental disorder according to predetermined diagnostic criteria, determine the overall severity of cognitive impairment, and evaluate walking and balance ability using a Performance Oriented Mobility Assessment (POMA).

[0010] In the present invention, the step of acquiring the measured gait data can acquire the gait characteristic values ​​of the subject using an accelerometer attached to the body's center of gravity and an existing gait measurement device.

[0011] In the present invention, the accelerometer may be an inertial measurement unit (IMU) attached to the waist of the subject with a predetermined adhesive material, and may be a device capable of measuring acceleration in three axial directions.

[0012] In the present invention, the step of developing the walking speed estimation model may include a first model that performs a linear regression analysis including roll angle, yaw angle, and body weight as gait parameters, and a second model that separates the subject into a plurality of subgroups based on NSL and performs the linear regression analysis for each subgroup.

[0013] In the present invention, the NSL is calculated by the following formula: JPEG0007786693000001.jpg1281 In the above formula, l l is the leg length, l s is the stride length, α1 is the person's leg / height ratio, α1 is the leg bending coefficient, and a is the angular displacement.

[0014] The present invention may further include a step of statistically analyzing the developed walking speed estimation model.

[0015] The present invention may further include a step of calculating the walking speed of the person whose walking speed is to be estimated using the walking speed estimation model that has been developed.

[0016] In the present invention, the step of calculating the walking speed can calculate the walking speed by inputting and acquiring data on the age, weight, and leg length of the person whose walking speed is to be estimated, acquiring cadence, VHD (Vertical height displacement), roll angle, and yaw angle from an inertial measurement unit (IMU), and substituting the acquired values ​​into the walking speed estimation model.

[0017] According to another embodiment of the present invention, there is provided a walking speed estimation device including a processor and a memory, wherein the processor determines N subjects to be measured for walking speed measurement, acquires physical data of the subjects, performs a clinical evaluation on the subjects, performs a gait evaluation on the subjects to acquire measured gait data, and develops a walking speed estimation model having a plurality of gait parameters based on the measured gait data, wherein the walking speed estimation model includes, as gait parameters, a roll angle, a yaw angle, and a body weight measured using an accelerometer worn at the center of gravity of the body of the subjects.

[0018] Furthermore, there is provided a computer-readable recording medium having a program recorded thereon for executing the method of the present invention on a computer. [Effects of the Invention]

[0019] According to the above-described means for solving the problems of the present disclosure, walking speed can be precisely estimated to diagnose aging and related risks.

[0020] Furthermore, according to the means for solving the problems of the present disclosure, weight, roll angle, and yaw angle can be used as additional walking parameters to reduce training errors, and normalized subgroups can be used to develop a walking speed estimation model.

[0021] Furthermore, according to the means for solving the problems of the present disclosure, the model of the present invention can minimize the demographic and anthropometric influences on walking speed estimation by using NSL instead of the original stride length value. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 is a system diagram including a user terminal and an estimation server according to one embodiment. [Figure 2] FIG. 2 is a flow chart illustrating a walking speed estimation method according to one embodiment. [Figure 3] FIG. 3 is a table showing characteristics of subjects according to one embodiment. [Figure 4] FIG. 4 is a table illustrating a comparison between the 0th model and the 1st model according to one embodiment. [Figure 5] FIG. 5 is an embodiment showing a table of subgroups sorted by normalized stride length according to one embodiment. [Figure 6] FIG. 6 is a table illustrating a method for estimating walking speed for each subgroup classified by normalized stride length according to one embodiment. [Figure 7] FIG. 7 is a comparative example of statistical analysis of the walking speed estimation model according to one embodiment and other models. [Figure 8] FIG. 8 is a walking model of a walking speed estimation model according to one embodiment. [Figure 9] FIG. 9 is a block diagram of a walking speed estimation device according to an embodiment. BEST MODE FOR CARRYING OUT THE INVENTION

[0023] According to one embodiment of the present invention, there is provided a walking speed estimation method including the steps of determining N subjects whose walking speeds will be measured and acquiring physical data of the subjects, acquiring data on the results of a clinical evaluation of the subjects, conducting a gait evaluation of the subjects to acquire measured gait data, and developing a walking speed estimation model having a plurality of gait parameters based on the measured gait data, wherein the walking speed estimation model includes, as gait parameters, roll angle, yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the subjects' bodies. DETAILED DESCRIPTION OF THE INVENTION

[0024] The advantages and features of the present invention, as well as the methods for achieving them, will become more apparent with reference to the embodiments described in detail in conjunction with the accompanying drawings. However, it should be understood that the present invention is not limited to the embodiments presented below, but can be embodied in different forms, and includes all modifications, equivalents, and alternatives that fall within the spirit and scope of the present invention.

[0025] The terms used in this application are merely used to describe specific embodiments and are not intended to limit the present invention. The singular expressions include the plural expressions unless the context clearly dictates otherwise. In this application, terms such as "comprise" or "have" are intended to specify the presence of features, numbers, operations, components, parts, or combinations thereof described herein, and are understood not to preclude the possibility of the presence or addition of one or more other features, numbers, operations, components, parts, or combinations thereof.

[0026] Some embodiments of the present disclosure may be represented by functional blocks and various processing operations. Some or all of these functional blocks may be implemented in a variety of hardware and / or software configurations that perform specific functions. For example, the functional blocks of the present disclosure may be implemented by one or more microprocessors or by circuit configurations for a given function. For example, the functional blocks of the present disclosure may be implemented in various programming or scripting languages. The functional blocks may be implemented by algorithms executed by one or more processors. The present disclosure may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "configuration" may be used broadly and are not limited to mechanical or physical configurations.

[0027] Furthermore, the connecting lines or members between components shown in the figures are merely exemplary functional and / or physical or circuit connections, and in an actual device the connections between components may be represented by various alternative or additional functional, physical, or circuit connections.

[0028] Hereinafter, an action performed by a user may refer to an action performed by a user through a user terminal. As an example, a command corresponding to an action performed by a user may be input to the user terminal through an input device (e.g., a keyboard, a mouse, etc.) embedded in or additionally connected to the user terminal. As another example, a command corresponding to an operation performed by a user may be input to the user terminal through a touch screen of the user terminal. In this case, the operation performed by the user may include a predetermined gesture. For example, the gesture may include a tap, a touch and hold, a double tap, a drag, a pan, a flick, a drag and drop, etc.

[0029] The present disclosure will now be described in detail with reference to the accompanying drawings.

[0030] FIG. 1 is a system diagram including a user terminal and an estimation server according to one embodiment.

[0031] A system according to one embodiment may include multiple user terminals 1000, estimation servers 2000, and IMUs 10.

[0032] The user terminal 1000 may be a smartphone, tablet PC, PC, smart TV, mobile phone, laptop, or other mobile or non-mobile computing device. The user terminal 1000 may also be a wearable device such as glasses or a headband with communication and data processing capabilities. The user terminal 1000 may include any type of device that can communicate with other devices via a network.

[0033] The estimation server 2000 may be implemented by a computing device or multiple computing devices that communicate over a network to provide instructions, code, files, content, services, etc.

[0034] The user terminal 1000 and the estimation server 2000 can communicate with each other using a network. The estimation server 2000 can communicate with the user terminal 1000 via the network and provide the walking speed estimation model and estimation results to the user terminal 1000. In one embodiment, the estimation server 2000 can estimate the user's walking speed using values ​​obtained from an IMU (inertial measurement unit) 10.

[0035] Meanwhile, in another embodiment, the user terminal 1000 may be a walking speed estimation device that has downloaded an application capable of executing the walking speed estimation method of the present invention from the estimation server 2000 or another external device not shown. That is, the walking speed estimation method of the present disclosure may be executed by the user terminal 1000 or the estimation server 2000. Hereinafter, for convenience of explanation, the user terminal 1000 or the estimation server 2000 will be referred to as a walking speed estimation device.

[0036] The IMU 10 may be an acceleration measuring device, and more specifically, a three-axis acceleration measuring device. According to one embodiment of the present invention, the IMU 10 may be attached to the center of gravity of a person's body, as shown in FIG. 1. More specifically, the IMU 10 may be a three-axis accelerometer attached to the waist using a predetermined adhesive material.

[0037] Meanwhile, the term "network" refers to a data communication network in the comprehensive sense that enables smooth communication between the network components shown in FIG. 1, including a local area network (LAN), a wide area network (WAN), a value-added network (VAN), a mobile radio communication network, a satellite communication network, and any combination thereof. The term "network" refers to a wired internet, a wireless internet, and a mobile radio communication network. Wireless communication may include, but is not limited to, wireless LAN (Wi-Fi), Bluetooth, Bluetooth low energy, ZigBee, Wi-Fi Direct (WFD), ultrawideband (UWB), infrared data association (IrDA), and near field communication (NFC).

[0038] FIG. 2 is a flow chart illustrating a walking speed estimation method according to one embodiment.

[0039] Referring to FIG. 2, first, N subjects whose walking speeds will be measured are determined, and their physical data is acquired (S10).

[0040] Next, a clinical evaluation is performed on the determined subject (S20).

[0041] Next, a gait assessment is performed based on the clinical assessment (S30).

[0042] Next, a walking speed estimation model having a plurality of walking parameters is developed based on the gait assessment (S40). At this time, the walking speed estimation model may include, as walking parameters, roll angle, yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the subject's body.

[0043] The walking speed estimation method of the present invention will be considered in more detail below.

[0044] First, the subject to be measured in the present invention can be identified as follows.

[0045] FIG. 3 is a table showing characteristics of subjects according to one embodiment.

[0046] As summarized in the table of FIG. 3, the present invention can determine a portion of the total N participants from two existing cohort studies on aging and senility, i.e., Study 1 and Study 2, as subjects. Furthermore, the present invention acquires physical characteristic data of the subjects. For example, the physical characteristic data of the subjects can be listed in the first column of the table in FIG. 3, such as sex, age, Mini-Mental State Examination (MMSE), Performance-Oriented Mobility Assessment (POMA), height, weight, BMI including overweight and underweight ratios, foot length, vertical height displacement (VHD), cadence, gait speed, step length, roll angle, and yaw angle. More specifically, VHD can be the average difference between the maximum and minimum vertical heights of the body's center of gravity within one walking cycle. The walking speed and stride length listed in the table of FIG. 3 are calculated using existing gait analysis devices (e.g., GAITRite TM ) and the roll angle may be measured using a waist-worn acceleration measurement unit.

[0047] More specifically, according to one embodiment of the present invention, the first and second studies may be population-based exploratory cohort studies of elderly Koreans. According to one embodiment, the first study is “Overview of the Korean Longitudinal Study on Cognitive Aging and Dementia” (JW Han, T.H. Kim, K.P. Wak, K.Kim, B.J. Kim, S.G. Kim, J.L. Kim, T.H. Kim, S.W. Moon, and J.Y. Park, Psychiatry Investigation, vol. 15, no. 8, pp. 767, 2018.), and the second study is “The Korean Version of the FRAIL Scale: Clinical The subject of the present invention may be, but is not necessarily limited to, "feasibility and validity of assessing the frailty status of Korean elderly" (H.-W. Jung, H.-J. Yoo, S.-Y. Park, S.-W. Kim, J.-Y. Choi, S.-J. Yoon, C.-H. Kim, and K.-I. Kim, The Korean Journal of Internal Medicine, vol. 31, no. 3, pp. 594, 2016.), and the subject of the present invention may be freely modified.

[0048] More specifically, in Study 1, elderly individuals selected from specific regions were tracked at predetermined intervals. More specifically, in Study 1, 6,818 Koreans aged 60 or older randomly sampled from 13 regions across South Korea were tracked every two years starting in 2009. In Study 2, follow-up observations of subjects were conducted in specific regions at predetermined intervals. More specifically, in Study 2, 3,000 Korean volunteers aged 70 to 84 were tracked every two years from 2016 to 2020.

[0049] Meanwhile, the walking speed measurement model of the present invention can be established by using N healthy elderly people living in the community as subjects, excluding participants with neurological disorders such as major psychiatric disorders and neurocognitive disorders, from among the M participants in the first and second studies. That is, according to one embodiment of the present invention, the N subjects can be determined from a population of participants in the existing studies, excluding those with psychiatric or neurological disorders.

[0050] 3, a total of 759 subjects may be included, including 338 men aged 74.8±5.0 years and 421 women aged 73.3±4.5 years. According to one embodiment, participants with stroke and musculoskeletal disorders that may affect gait or balance at baseline or subsequent assessments, as well as participants with a pre-set score (e.g., less than 25 points) on the Tinetti Performance Oriented Mobility Assessment (POMA), are excluded from the subjects.

[0051] Next, according to one embodiment of the present invention, a clinical evaluation is performed on the determined subject (S20). More specifically, a geriatric psychiatrist or neurologist with expertise in dementia research may meet with the subject and perform a standardized diagnostic interview, physical and neurological examinations, complete blood counts, chemistry profiles, syphilis serology tests, cardiac ultrasound, chest X-ray, and other laboratory tests.

[0052] Furthermore, the present invention can diagnose dementia and other mental disorders according to preset diagnostic criteria, and determine the overall severity of cognitive impairment using a clinical dementia scale. Also, the above-mentioned POMA can be used to evaluate gait and balance. In one embodiment, a higher POMA score indicates better gait and balance, and the maximum POMA score can be 28 points.

[0053] Next, the present invention performs a gait assessment to obtain measured gait data (S30). More specifically, the present invention can obtain results of measuring the gait of each subject using an IMU attached to the center of body mass (CoM) and an existing gait measurement device. In one example, the IMU may be, but is not limited to, FITMET® FitLife Inc. and may be replaced with ActiGraph®. Furthermore, in one embodiment, the existing gait measurement device may be GAITite®. TM The device that generates the walking data required for developing the walking speed estimation model of the present invention described later may be, but is not limited to, an existing gait measurement device. For the sake of convenience, the following description assumes that the existing gait measurement device is GAITRite.

[0054] According to one embodiment, the IMU is a smooth-edged hexagon (35x35x13mm [14g] or 30x40x10mm [17g]) and can be a similar digital tri-axis accelerometer (e.g., BMA255, BOSH, Germany) and gyroscope (e.g., BMX055, BOSH, Germany). The illustrated tri-axis accelerometer can measure tri-axis accelerations of up to ±8g (0.004g resolution) and up to ±1,000μ / s (0.03μ / s resolution) at 250Hz.

[0055] In a measurement method according to one embodiment of the present invention, an IMU can be fixed to the subject's third or fourth lumbar vertebrae using a predetermined adhesive (e.g., Hypafix). Furthermore, during the experiment, the subject can walk back and forth three times along a 14-meter flat, straight walkway at a speed of their choosing, and the walking path can be designed so that the subject turns after crossing the 14-meter line. Furthermore, according to one embodiment, a GAITRite electronic mat can be placed in the center of the walkway to measure steady-state walking.

[0056] In one embodiment, the GAITRite can be a portable gait analysis walkway device that measures temporal and spatial gait parameters via a 100 Hz electronic track connected to a computer's USB port. The GAITRite electronic mat's track size can be 520 (L) x 90 (W) x 0.6 cm (H), and the active sensing area can be 427 (L) x 61 cm (W). Furthermore, the electronic mat can contain 16,128 sensors arranged with a spatial precision of 1.27 cm.

[0057] Next, the present invention develops a walking speed estimation model (S40). In one embodiment, the present invention can remove walking sections of a predetermined length before and after the data generated in the above-mentioned walking data acquisition step to measure the subject's steady-state gait. More specifically, the walking speed estimation device of the present invention can remove walking sections of 2 m length before the reference point and before each rotation to measure the subject's steady-state gait, and then analyze the remaining intermediate 10 m of data. Furthermore, the present invention can identify each step after preprocessing the IMU signal and selecting features. In this case, the present invention can use a comparative standard walking speed obtained from another existing gait measurement device (e.g., GAITRite).

[0058] The present invention establishes a 0th model based on the obtained standard walking speed for comparison, and can estimate walking speed using IMU signals according to the 0th model. In the 0th model, walking speed can be estimated using a regression model including age, sex, foot length, cadence, vertical height displacement (VHD), and cadence (gait rate).

[0059] More specifically, according to one embodiment, Model 0 may be a regression model for estimating walking speed of healthy older adults using accelerations and angles from IMU sensors. In Model 0, two demographic features (age and gender), an anthropometric feature (leg length), and two features measured by the IMU (VHD and cadence) can be used as gait parameters to estimate walking speed.

[0060] On the other hand, walking speed is the product of cadence and step length. Regarding walking speed estimation, since the cadence measured by the IMU is reliable, if the walking speed estimation model includes features related to step length, the accuracy of the model can be improved.

[0061] The following equation (1) is a mathematical linear regression equation for the 0th model developed based on the subject's physical data acquired in the embodiment shown in Figure 2. From equation (1), it can be seen that the linear regression equation for the 0th model includes age, gender, cadence, VHD, and leg length as gait parameters for estimating walking speed (gait speed), and each gait parameter can have a corresponding coefficient. Furthermore, leg length is the average length of both legs, and gender can be set to 1 for men and 2 for women.

[0062] [Number 1] Walking speed [0th model] =-113.2-0.388*age+3.06*gender+1.17*cadence+12.1*VHD+3.25*foot length

[0063] However, model 0 exhibited low accuracy for people with a walking speed of 100 cm per second or less, and the estimation error gradually increased as the walking speed decreased. Therefore, in the present invention, we supplement model 0 to estimate walking speed by adding features related to step length, and optimize the walking speed estimation model of the present invention within subgroups stratified by normalized step length (NSL). Furthermore, elderly people's walking patterns may vary depending on their health condition, and linear regression models are vulnerable to heterogeneous samples. To address this, we developed a regression model within subsequent subgroups of walking speed to further improve the accuracy of the model. Furthermore, we compared the accuracy of the walking speed estimation model of the present invention with that of other existing gait analysis systems.

[0064] Hereinafter, a first model and a second model of a walking speed estimation method that can complement the above-described 0th model will be described as an embodiment of the present invention.

[0065] According to one embodiment of the present invention, the roll and yaw angles of the body center of gravity and the body weight can be added to the zeroth model as walking parameters to develop the first model of the walking speed estimation method of the present invention.

[0066] According to one embodiment, stride length is a product of trunk, knee, and foot movement, and the roll and yaw angles of the body center of mass can reflect the angular motion of the trunk and limbs. More specifically, the roll and yaw angles of the body center of mass can be calculated from IMU sensor data, and the roll and yaw angle values ​​can be re-oriented to Cartesian coordinates. Stride length is also related to body weight, which is related to thrust power.

[0067] More specifically, the three additional features selected in the linear regression model of the first model of the present invention are body weight, CoM roll angle, and yaw angle. Relatedly, body weight is a key gait parameter in that both weight loss and weight gain reduce gait speed by reducing muscle strength or inducing physical weakness. Furthermore, in a sample of elderly adults, overweight individuals with a BMI of 25 or more outnumber underweight individuals with a BMI of less than 18.5. This may be the reason for adding body weight as a gait parameter in the walking speed estimation model of the present invention, and also the reason for the negative coefficient in the regression model.

[0068] Furthermore, body rotation is related to gait patterns. The roll angle and yaw angle represent three-dimensional gait patterns. From a top-down perspective, yaw rotation is the addition of CoM movement to the forward ground motion of the stance leg when a subject rotates their hips in a gait in which each leg moves forward. In the present walking speed estimation model, the yaw angle coefficient is positive, which may indicate that the yaw angle may increase as walking speed increases. Therefore, the present invention can define a relationship between the CoM yaw angle and walking speed that has not been previously studied.

[0069] Furthermore, roll angle indicates the behavior related to medio-lateral (ML) stability and is related to walking speed. In our walking speed estimation model, the roll angle coefficient is positive, indicating that roll angle is likely to increase as walking speed increases.

[0070] On the other hand, gender does not need to be selected as a walking parameter in the first and second walking speed estimation models of the present invention. This is because, among the three features added to the model of the present invention, the roll angle of the CoM can reflect the influence of gender on walking speed to some extent. That is, as can be seen in the table of FIG. 3 above, women have a larger roll angle than men. In contrast, the yaw angle does not statistically differ between genders.

[0071] The following equation (2) is a mathematical linear regression equation of the first model developed based on the subject's physical data acquired in the embodiment of Fig. 2. From equation (2), it can be seen that the linear regression equation of the first model includes age, cadence, VHD, foot length, weight, roll angle, and yaw angle as gait parameters for estimating walking speed, and can have coefficients corresponding to each gait parameter.

[0072] [Number 2] Walking speed [first model] =-106.0 - 0.328 x age + 1.10 x cadence + 10.1 x VHD + 3.29 x foot length - 0.115 x weight + 1.01 x roll angle + 0.647 x yaw angle

[0073] According to one embodiment of the present invention, the walking speed of a person whose walking speed is to be estimated can be accurately calculated using the developed first model. That is, the walking speed estimation device of the present invention can input and acquire the age, weight, and leg length data of the person whose walking speed is to be calculated, or acquire the data from another terminal or server. The walking speed estimation device also acquires from the IMU the cadence, VHD, roll angle, and yaw angle, which are values ​​measured by the IMU while the person whose walking speed is to be estimated walks while wearing the IMU. The walking speed estimation device may also calculate the walking speed by substituting the acquired gait parameter values ​​into the linear regression equation of the first model. According to one embodiment of the present invention, when estimating walking speed using the developed first model, accurate estimation of walking speed is possible simply using the IMU, even without a gait measurement device (e.g., GAITRite).

[0074] FIG. 4 is a table illustrating a comparison between the 0th model and the 1st model according to one embodiment.

[0075] 4, the first model of the present invention includes gender, roll angle, yaw angle, and weight as walking parameters, and the remaining walking parameters can use the walking parameters of the 0th model. In the table of FIG. 4, VIF is the variation inflation factor.

[0076] Furthermore, compared with the values ​​measured by Gaitrite, the 0th model has a high level of walking speed estimation (R = 0.935, R 2 =0.875, adjustR 2 =0.874, F=1051, p<0.001), and the first model's estimation of walking speed is more accurate (R=0.953, R 2 =0.908, adjustR 2 =0.908, F=10.001, p<0.001).

[0077] In addition, the present invention can set a second model, which is a subgroup-specific regression model that has gait parameters included in the first model, within subgroups classified by normalized step length (NSL) using the K-means algorithm. The regression model of the second model of the present invention can include only features with a variance inflation factor (VIF) of less than 2.5.

[0078] According to one embodiment of the present invention, NSL is obtained by dividing stride length by height, and stride length can be obtained by dividing the walking speed determined by the first model by cadence. Hereinafter, the first and second models described above can be collectively referred to as the walking speed estimation model of the present invention.

[0079] FIG. 5 is an embodiment showing a table of subgroups sorted by normalized stride length according to one embodiment.

[0080] Referring to FIG. 5, the characteristics of three NSL subgroups are summarized in the table of FIG. 5. More specifically, the NSL subgroups may be short, medium, or long. According to one embodiment of the present invention, the reference values ​​for distinguishing the short, medium, and long NSL subgroups may be set differently depending on the characteristics of the subject, and the number of subgroups may be set to more or less than three. FIG. 5 shows physical characteristic data of subjects classified into NSL subgroups according to preset reference values ​​according to one embodiment of the present invention.

[0081] More specifically, referring to FIG. 5, the present invention can acquire the subject's sex, age, height, weight, foot length, gait speed, cadence, VHD, roll angle, yaw angle, and POMA value for each subgroup. VHD and POMA are the same as those described in FIG. 3, and roll angle and yaw angle are acquired from an IMU. Foot length and gait speed can also be acquired from GAITRite as described in FIG. 3. The remaining values ​​are also omitted from the description of FIG. 3. Furthermore, according to one embodiment, analysis of variance based on Bonferron post-hoc comparison was used for the statistics in FIG. 3, but it goes without saying that the statistical analysis method may be changed in other embodiments.

[0082] 10 is a table illustrating a method for estimating walking speed by NSL subgroup according to one embodiment.

[0083] Referring to Figure 6 , when the second model, which is a walking speed estimation model for each NSL subgroup, was developed using the walking parameters included in the first model, it can be seen that all walking parameters were selected as significant walking parameters for walking speed in all NSL subgroups and had low VIF.

[0084] Referring to FIG. 6, the NSL subgroup model (model 2) according to one embodiment estimated walking speed more accurately (R = 0.955, R 2 = 0.912, p < 0.001), and the walking speed estimates for all NSL subgroups are reasonable. More specifically, the walking speed statistics estimated by the second model are shown by NSL subgroup, with R for the short NSL subgroup (shortNSL in Figure 6). 2 =0.828, F=195.545, p<0.001, and for the medium NSL (MediumNSL in Figure 6) subgroup, R 2 =0.801, F=217.135, p<0.001, and for the long NSL (longNSL in Figure 6) subgroup, R 2 =0.836, F=140.971, p<0.001.

[0085] The following equations (3-1) to (3-3) are mathematical linear regression equations for the second model shown in FIG. 6, which were developed based on the subject's physical data acquired in the embodiment shown in FIG. 2. From equations (3-1) to (3-3), it can be seen that the linear regression equation for each NSL subgroup of the second model includes age, cadence, VHD, foot length, weight, roll angle, and yaw angle as gait parameters for estimating walking speed, and each gait parameter can have a corresponding coefficient. The short NSL subgroup is a group with an NSL of 0.347 or less, the intermediate NSL subgroup is a group with an NSL of greater than 0.347 and less than 0.394, and the long NSL subgroup is a group with an NSL of greater than 0.394.

[0086] [Number 3-1] Walking speed [second model, NSL ≤ 0.347] =-95.6-0.462*age+1.06*cadence+13.3*VHD+3.24*foot length-0.154*weight+1.27*roll angle+0.550*yaw angle

[0087] [Number 3-2] Walking speed [second model, 0.347 <NSL≦0.394] = -117.8 - 0.327 x age + 1.14 x cadence + 11.8 x VHD + 3.26 x foot length - 0.122 x weight + 1.14 x roll angle + 0.828 x yaw angle

[0088] [Number 3-3] Walking speed [second model, 0.394 <NSL] = -121.4 - 0.272 x age + 1.15 x cadence + 9.90 x VHD + 3.68 x foot length - 0.179 x weight + 1.04 x roll angle + 0.620 x yaw angle

[0089] According to one embodiment of the present invention, the walking speed of a walking speed estimation subject can be accurately calculated for each NSL subgroup using the developed second model. That is, the walking speed estimation device of the present invention can input and acquire the age, weight, and leg length data of the subject whose walking speed is to be calculated, or acquire the data from another terminal or server. The walking speed estimation device also acquires the cadence, VHD, roll angle, and yaw angle, which are values ​​measured by the IMU while the subject walks while wearing the IMU. The walking speed estimation device can also use the walking parameter values ​​acquired in the above-described first model use phase in the second model.

[0090] The walking speed estimation device may also determine an NSL subgroup for a walking speed estimation subject using the walking speed calculated by the first model. In this case, the NSL of the walking speed estimation subject is calculated as NSL = (walking speed of the first model) / (cadence / 60) / height, and the NSL subgroup may be determined using the calculated NSL. The walking speed estimation device may also calculate the walking speed by substituting the acquired gait parameter values ​​into a second model linear regression equation (a regression equation for the corresponding NSL subgroup). According to one embodiment of the present invention, when estimating walking speed using the developed second model, precise estimation of walking speed for each NSL subgroup is possible simply using an IMU, even without a gait measurement device (e.g., GAITRite).

[0091] Next, the present invention can perform statistical analysis of the developed walking speed estimation model. According to one embodiment, the present invention can compare continuous variables using a Student's t-test or analysis of variance (ANOVA) and categorical variables using a between-groups chi-square test. Using this statistical analysis method, the present invention can compare statistical values ​​estimated by Gaitrite with statistical values ​​estimated by the walking speed estimation model of the present invention. More specifically, the statistical values ​​compared may be the intraclass correlation coefficient (ICC), mean error (ME), mean absolute error (MAE), or root mean square error (RMSE).

[0092] Furthermore, the present invention can compare the ICC, ME, MAE, and RMSE of estimated walking speed between walking speed estimation models using repeated measures ANOVA. The present invention can perform all statistical analyses using the Statistical Package for Social Sciences version 25.0 (International Business Machines Corporation, Armonk, NY). In one embodiment of the present invention, the 0th model of the walking speed estimation model of the present invention can be set to the existing technology, and the 1st and 2nd models can be set to the developed technology to perform statistical analysis.

[0093] FIG. 7 is a comparative example of statistical analysis of the walking speed estimation model according to one embodiment and other models.

[0094] Referring to Figure 7, the present invention can compare the accuracy of the walking speed estimation method of the present invention using the walking speed measured by GAITRite as a comparison standard. Referring to Figure 7, it can be seen that for all subjects, the first and second models exhibit lower MAE, ME, and RMSE and better ICC than the first model. Furthermore, it can be seen that the second model exhibits lower MAE than the first model (p=0.007).

[0095] 7, subjects can be grouped into three subgroups (slow, medium, and fast) using the walking speed based on GAITRite according to the second model of the present invention. Slow walking was defined as a standard deviation below the mean (17.9 m / s), and fast walking was defined as a standard deviation above the mean (114.4 m / s).

[0096] The walking speed estimated by the 0th model tended to be slower than that measured by GAITRite in the fast NSL subgroup (p<0.001), while it tended to be faster in the slow NSL subgroup (p<0.001). This was also true for the walking speeds estimated by the 1st and 2nd models, but the difference between the IMU and GAITRite was not significant. TMIt can be seen that the difference in walking speed measurements between the fast NSL subgroup and the fast NSL subgroup was reduced (p<0.001).

[0097] In summary, walking speed estimation using linear regression according to one embodiment of the present invention is essentially to calculate an approximation of a mathematical expression of human walking speed. The linear regression in the model of the present invention is a simplified form of Taylor expansion of a virtual true expression and can include higher degrees and numbers of variables. Furthermore, the present invention can improve the accuracy of walking speed estimation using an IMU by optimizing the linear regression model with NSL subgroups using additional gait parameters in the linear regression model.

[0098] Furthermore, the walking speed estimation model of the present invention can use NSL to develop subgroup-specific models. Because stride length decreases with age, stride length can reflect the walking patterns of elderly people. As shown in Figure 5 above, anthropometric measurements differ among the three NSL subgroups, and each of the three subgroups may have a different walking pattern associated with different anthropometric characteristics.

[0099] FIG. 8 is a walking model of a walking speed estimation model according to one embodiment.

[0100] Our model can minimize the demographic and anthropometric influences on walking speed estimation by using NSL instead of the original stride length value. Human gait during stance phase can be modeled as an inverted pendulum with a spring attached as shown in Figure 8, where CP is the contact point. The leg length during stance phase can be expressed as the following equation (4): where l l is the leg length, and β1 is the leg bending coefficient.

[0101] [Number 4] JPEG0007786693000002.jpg719

[0102] Also, the stride length can be calculated. Considering the conservation of angular momentum and the assumption of a point mass, the energy after contact (E con ) can be expressed as the following equation (5). In this case, m1 is the mass point, ω1 is the angular velocity before contact, and l l is the leg length, l s is the stride length.

[0103] [Number 5] JPEG0007786693000003.jpg1551

[0104] Therefore, taking into account the symmetry of the movement, the stride length can be calculated using the following equation (6).

[0105] [Number 6] JPEG0007786693000004.jpg734

[0106] Therefore, NSL can be calculated as follows: (7) where α1 is the leg / height ratio of the person and a is the angular displacement in walking mechanics.

[0107] [Number 7] JPEG0007786693000005.jpg1281

[0108] In the first model of the present invention, the VHD coefficient differed among the three NSL subgroups. More specifically, in the second model, the VHD coefficient was highest in the short NSL subgroup and lowest in the long NSL subgroup, but B was smallest in the short NSL subgroup, indicating that dynamic gait and body rotation were less effective in the short NSL subgroup compared to the other NSL subgroups. Furthermore, the age coefficient and B value were lowest in the long NSL subgroup, suggesting that the gait of the long NSL subgroup may not be affected by age. Furthermore, the cadence was lower in the short NSL subgroup, indicating that the short NSL subgroup may have a more conservative gait pattern compared to the other subgroups.

[0109] In summary, the present invention generates a walking speed estimation model based on the results of analyzing the walking patterns of N elderly people. The present invention can improve existing speed estimation models by using additional gait parameters to reduce training error and further advance the estimation equation using normalized subgroups.

[0110] According to one embodiment of the present invention, additional gait parameters are roll angle, yaw angle, and weight, which are related to movement under the body's center of mass in terms of angle and propulsion. Adding these additional gait parameters can lead to better estimation results than gender-based estimations in existing studies. Furthermore, because NSL subgroups are generated by separating subjects with different gait mechanics, regardless of size, the walking speed estimation algorithm can be improved to achieve higher accuracy.

[0111] FIG. 9 is a block diagram of a walking speed estimation device according to an embodiment.

[0112] The walking speed estimation device 1100 in FIG. 9 may be the walking speed estimation device in FIG.

[0113] 9, a walking speed estimation device 1100 may include a communication unit 1110, a processor 1120, and a DB 1130. Only components related to the embodiment are shown in the walking speed estimation device 1100 in Fig. 9. Therefore, it may be understood by a person skilled in the art that the walking speed estimation device 1100 may further include other general-purpose components in addition to the components shown in Fig. 9.

[0114] The communication unit 1110 may include one or more components that enable wired / wireless communication with other nodes. For example, the communication unit 1110 may include at least one of a short-range communication unit (not shown), a mobile communication unit (not shown), and a broadcast receiving unit (not shown).

[0115] The DB 1130 is hardware that stores various data processed in the walking speed estimation device 1100, and can store programs for processing and control of the processor 1120. The DB 1130 can store payment information, user information, etc.

[0116] DB1130 may include RAM (random access memory), such as DRAM (dynamic random access memory), SRAM (static random access memory), ROM (read-only memory), EEPROM (electrically erasable programmable read-only memory), CD-ROM, Blu-ray or other optical disk storage, HDD (hard disk drive), SSD (solid state drive), or flash memory.

[0117] The processor 1120 controls the overall operation of the walking speed estimation device 1100. For example, the processor 1120 can generally control the input unit (not shown), the display (not shown), the communication unit 1110, the DB 1130, etc. by executing a program stored in the DB 1130. The processor 1120 can control the operation of the walking speed estimation device 1100 by executing a program stored in the DB 1130.

[0118] The processor 1120 may be implemented using at least one of ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers, microcontrollers, microprocessors, and electrical units for performing other functions.

[0119] Embodiments of the present invention may be implemented in the form of a computer program executable on a computer via various components, and such a computer program may be recorded on a computer-readable medium, which may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, etc.

[0120] On the other hand, the computer programs may be those specially designed and constructed for the purposes of the present invention, or they may be those well known and available to those skilled in the computer software art. Examples of computer programs include not only machine code, such as produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter, etc.

[0121] According to one embodiment, methods according to various embodiments of the present disclosure may be provided in a computer program product. The computer program product may be traded as a commodity between sellers and buyers. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact discreet only memory (CD-ROM)) or may be available through an application store (e.g., Play Store). TM ) or directly online between two user devices (e.g., downloaded or uploaded). In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily generated on a machine-readable storage medium, such as the memory of a manufacturer's server, an application store server, or an intermediary server.

Claims

1. A step in which a walking speed estimation device determines N subjects to be measured for walking speed measurement and acquires physical data of the subjects; a step in which the walking speed estimation device acquires data on the results of a clinical evaluation of the subject; a step in which the walking speed estimation device performs a gait evaluation on the subject and acquires measured gait data; the walking speed estimation device developing a walking speed estimation model having a plurality of walking parameters based on the measured walking data; the walking speed estimation device includes a step of calculating the walking speed of the person whose walking speed is to be estimated by using the developed walking speed estimation model; A method for operating a walking speed estimation device, wherein the walking speed estimation model includes, as walking parameters, a roll angle, a yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the subject's body.

2. Determining the N subjects includes: The method for operating a walking speed estimation device according to claim 1, wherein the N subjects are determined by excluding people in the population who have a disease that may affect walking or whose balance ability assessment score is below a predetermined score.

3. 2. The method of claim 1, wherein the clinical assessment is to diagnose a mental disorder according to preset diagnostic criteria, determine the overall severity of cognitive impairment, and evaluate walking and balance ability using a Performance Oriented Mobility Assessment (POMA).

4. A method for operating a walking speed estimation device as described in claim 1, wherein the step of the walking speed estimation device acquiring the measured walking data includes the walking speed estimation device acquiring the walking characteristic values ​​of the person being measured using an accelerometer attached to the body's center of gravity and an existing gait measurement device.

5. 2. The method of claim 1, wherein the accelerometer is an inertial measurement unit (IMU) attached to the waist of the subject with a predetermined adhesive material and capable of measuring acceleration in three axial directions.

6. The walking speed estimation model is a first model performing a linear regression analysis including roll angle, yaw angle and body weight as gait parameters; a second model that divides the subjects into a plurality of subgroups based on normalized step length (NSL) and performs the linear regression analysis for each of the subgroups; 2. A method for operating the walking speed estimation device according to claim 1, comprising:

7. The NSL is calculated by the following formula: In the above formula, l l is the leg length, l s is the stride length, α 1 is the leg / height ratio of a person, β 1 7. The method for operating a walking speed estimation device according to claim 6, wherein: is a leg bending coefficient, and a is an angular displacement.

8. A step in which the walking speed estimation device statistically analyzes the developed walking speed estimation model. The method of claim 1 further comprising:

9. The step of the walking speed estimation device calculating the walking speed comprises:

2. The method for operating a walking speed estimation device according to claim 1, further comprising: inputting and acquiring age, weight, and leg length data of the person whose walking speed is to be estimated; acquiring cadence, VHD (Vertical Height Displacement), roll angle, and yaw angle from an inertial measurement unit (IMU); and substituting the acquired values ​​into the walking speed estimation model to calculate the walking speed.

10. As a walking speed estimation device, a processor; a memory; The processor: A walking speed estimation device comprising: determining N subjects to be measured for walking speed measurement; acquiring physical data of the subjects; conducting a clinical evaluation of the subjects; conducting a gait evaluation of the subjects to acquire measured walking data; developing a walking speed estimation model having a plurality of walking parameters based on the measured walking data; calculating the walking speed of the subject for whom walking speed estimation is to be performed using the developed walking speed estimation model; wherein the walking speed estimation model includes, as walking parameters, a roll angle, a yaw angle, and body weight measured using an accelerometer worn at the center of gravity of the subject's body.

11. A computer-readable recording medium storing a program for causing the walking speed estimation device to execute the method according to claim 1.

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