Patent Description:
With trends of decline of birth rate and/or increase of life expectancy, many countries in the world have entered a (super-)aging society. Among the care issues related to the elderly population, how to prevent the elderly population from falls has become one of the important issues.

After research, it is currently known that, gait-related parameters in people's walk may be used to predict future falls. For example, a normalized stride length of certain person may be used to predict the occurrence of repeated fall of the person in the next <NUM> or <NUM> months. Besides, people who walk relatively slowly also have a higher mortality rate. In addition, as people age, a forward inclination angle of the torso may also gradually increase. Moreover, for those suffering neurological diseases (e.g., Parkinson's disease, Alzheimer's disease, etc.), the angle of the torso may also be inclined forward or sideways.

Therefore, for those skilled in the art, if a mechanism can be designed where the gaits of people can be analyzed to determine whether the gaits of people are normal, it should be able to facilitates grasping the health condition of people, thus achieving the effect of preventing falls.

<CIT> discloses a wearable gait detection device, a walking ability improvement system and a wearable gait detection system. A load measurement part measures a load of a sole of the wearer's feet, a foot movement detection part at the shoes detects acceleration or angular velocity during feet movement, a centroid position calculation part calculates a centroid position of the feet based on changes in measured load, a movement locus calculation part calculates a movement locus of the feet based on acceleration or angular velocity detected by the foot movement detection part, a manifestation recognition part recognizes manifestation in the brain according to a specificity of centroid fluctuation of the feet based on the calculated centroid position and movement locus of the feet, and a sensory output part feeds back a sensation to the wearer based on a recognition result. <CIT> discloses methods and systems for diagnosing, monitoring and treating persons at risk for falling. People are diagnosed before they actually start falling. The diagnosis includes trying out and identifying one or more fall triggers using virtual reality tools. Treatment includes training the persons using situations or triggers which are determined to be relevant for that person. <CIT> discloses a wearable device for gait analysis for screening gait and analysis of lower limb joint kinematics and kinetics including ankle, calf, knee, thigh, hip, pelvic, foot plantar pressure, clearance parameters and all spatial temporal parameters. <CIT> discloses a quantitative gait training or analysis system employing instrumented footwear and an independent processing module. The instrumented footwear has sensors that permit the extraction of gait kinematics in real time and provide feedback from it. <CIT> discloses a method for determining physical properties of ground stepped upon by a user wearing a footwear incorporating an accelerometer, and includes receiving a raw signal from the accelerometer; identifying, in the received raw signal, at least one characteristic signature; associating the at least one characteristic signature to physical properties of the ground; and generating a signal indicating the physical properties based on said association. <CIT> discloses systems and methods for rehabilitation of limb motion including a thermal imaging device for imaging a subject or a plurality of sensors for sensing force or pressure exerted by a portion of the subject's body; a processor and a memory operably coupled to the processor. The processor is configured to receive force or pressure data measured by the sensors or image data captured by the thermal imaging device. <CIT> discloses a system for analyzing gait using textile sensors including a sock sensing system, which comprises a sock and at least one switch, tension sensor, or pressure sensor for sensing a posture or movement; and a processor configured to receive signals from the sock sensing system and to analyze a gait parameter. <CIT> discloses a system for measuring variation in the gait of a subject including a sensor arranged to measure variations in vertical position of the subject, a processor, and a display. The processor is arranged to identify a plurality of points in a first one of multiple steps and a plurality of points in a second one of the steps, to identify a plurality of pairs of the points, each pair comprising one point in each of the steps, to determine a value of height for each of the points in each of the pairs, and to control the display to produce a display plotting the heights of the two points in each pair against each other.

In view of the above, the invention provides a gait evaluating system, which may be used to solve the above technical problems.

The invention is provided by the appended claims. The following disclosure serves a better understanding of the present invention. Accordingly, the disclosure provides a gait evaluating system. The gait evaluating system includes a gait evaluating device configured to: obtain, from a pressure detection device, a plurality of pressure values of a user walking on the pressure detection device, where the pressure values correspond to a plurality of steps of the user; obtain a plurality of step feature values of the user based on the pressure values; obtain a plurality of walking limb feature values when the user walks on the pressure detection device based on a sensing data provided by a limb sensing device; and evaluate a gait of the user based on the step feature values and the walking limb feature values.

With reference to <FIG>, which is a schematic diagram illustrating a gait evaluating system according to an embodiment of the invention. In <FIG>, a gait evaluating system <NUM> may include a gait evaluating device <NUM>, a pressure detection device <NUM>, and limb sensing devices <NUM> to 13Z (where Z is a positive integer). In different embodiments, the gait evaluating device <NUM> is, for example but not limited to, various computer devices and/or smart devices.

As shown in <FIG>, the gait evaluating device <NUM> may include a storage circuit <NUM> and a processor <NUM>. The storage circuit <NUM> is, for example, any form of fixed or mobile random access memory (RAM), read-only memory (ROM), flash memory, hard drives, or other similar devices or a combination of these devices, and may be used to record a plurality of programming codes or modules.

The processor <NUM> is coupled to the storage circuit <NUM>, and may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), any other type of integrated circuits, state machines, processors based on the Advanced RISC Machine (ARM), and the like.

In different embodiments, the pressure detection device <NUM> may be embodied as a pressure detection mat including a plurality of pressure detectors, and may also be used for a user (e.g., a person to be performed with gait evaluation) to walk on, to detect a distribution/value of pressure applied to the pressure detection device <NUM> at each step of the user.

In some embodiments, the limb sensing devices <NUM> to 13Z may each be embodied as a video camera to capture a walking image of the user walking on the pressure detection device <NUM>.

Reference may be to <FIG>, which is a schematic diagram illustrating a gait evaluating system according to a first embodiment of the invention. In <FIG>, the pressure detection device <NUM> may be embodied as a pressure detection mat, and a user <NUM> may walk on the pressure detection device <NUM> in a walking direction D1 upon request.

In an embodiment, the pressure detection device <NUM> may include a plurality of pressure detectors 120a exhibiting a one-dimensional distribution. In another embodiment, the pressure detection device <NUM> may also include a plurality of pressure detectors 120b exhibiting a two-dimensional distribution. Nonetheless, the disclosure is not limited thereto. In some embodiments, the length of the pressure detection mat may be greater than or equal to <NUM> meters, and the width may be greater than or equal to <NUM> meters. Besides, in some embodiments, the pressure detection mat may be provided with one pressure detector 120a (or one pressure detector 120b) per <NUM><NUM> (or less). In some embodiments, the pressure detection mat may also be provided with one pressure detector 120a (or one pressure detector 120b) per <NUM><NUM>, but it is not limited thereto.

In the first embodiment, when the user <NUM> walks on the pressure detection device <NUM>, the pressure detectors distributed on the pressure detection device <NUM> may detect a plurality of pressure values PV corresponding to steps of the user <NUM>. The pressure detection device <NUM> may provide the pressure values PV to the gait evaluating device <NUM> for further analysis by the gait evaluating device <NUM>.

In the first embodiment, the limb sensing devices <NUM> and <NUM> may be respectively embodied as a first video camera and a second video camera. The first video camera may be used to capture a first walking image IM1 when the user <NUM> walks on the pressure detection device <NUM>, and the second video camera may be used to capture a second walking image IM2 when the user <NUM> walks on the pressure detection device <NUM>.

As shown in <FIG>, the imaging direction of the limb sensing device <NUM> (i.e., the first video camera) may be opposite to the walking direction D1 of the user <NUM>, to thereby capture a front image of the user <NUM> when walking. In addition, the imaging direction of the limb sensing device <NUM> (i.e., the second video camera) may be perpendicular to the walking direction D1 of the user <NUM>, to thereby capture a side image (e.g., from the right side) of the user <NUM> when walking.

In the first embodiment, for the first walking image IM1 and the second walking image IM2 obtained by the first video camera and the second video camera at a t-th time point (where t is a time index value), the gait evaluating device <NUM> may obtain a first skeleton diagram <NUM> and a second skeleton diagram <NUM> respectively in the first walking image IM1 and the second walking image IM2. In the embodiment of the invention, the gait evaluating device <NUM> may obtain the first skeleton diagram <NUM> and the second skeleton diagram <NUM> respectively in the first walking image IM1 and the second walking image IM2 based on any known image processing algorithms, for example but not limited to, the literature document "<NPL>".

In the first embodiment, the first skeleton diagram <NUM> and the second skeleton diagram <NUM> may, for example, correspond to the human body posture of the user <NUM> at the t-th time point, and may each include a plurality of reference points corresponding to a plurality of joints of the user <NUM> (e.g., corresponds to a reference point 210a at a wrist of the user <NUM>).

The gait evaluating device <NUM> projects the first skeleton diagram <NUM> and the second skeleton diagram <NUM> into a first integrated skeleton diagram based on the relative position between the first video camera and the second video camera. For related projection technology, reference may be made to the literature document "<NPL>".

In an embodiment, the first integrated skeleton diagram may include a plurality of joint angles (e.g., neck angle, shoulder angle, elbow angle, wrist angle, hip angle, knee angle, ankle angle, etc.) at the t-th time point. The joint angles correspond to the joints (e.g., neck, shoulders, elbows, wrists, hips, knees, ankles, etc.) of the user <NUM>. After that, the gait evaluating device <NUM> may obtain a plurality of angle values of the joint angles, and take the angle values as a plurality of walking limb feature values of the user <NUM> at the t-th time point.

In some embodiments, after obtaining the first skeleton diagram <NUM>, the second skeleton diagram <NUM>, and/or the first integrated skeleton diagram, the gait evaluating device <NUM> may, for example, remove outliers from the skeleton diagrams based on the median filter or other similar noise reduction technology, and then remove high-frequency fluctuations from the skeleton diagrams through a fast Fourier transform (FFT). After that, the gait evaluating device <NUM> may also smooth the movement between the skeleton diagrams at different time points through polyfitting. Nonetheless, the disclosure is not limited thereto.

With reference to <FIG>, which is a schematic diagram illustrating another gait evaluating system according to <FIG>. In <FIG>, except that the imaging directions of the limb sensing devices <NUM> and <NUM> are different from those of <FIG>, the rest of the configuration is generally the same as that of <FIG>.

Specifically, in <FIG>, from two sides in front of the user <NUM>, the limb sensing device <NUM> (i.e., the first video camera) and the limb sensing device <NUM> (i.e., the second video camera) may respectively capture the first walking image IM <NUM> and the second walking image IM2 of the user <NUM> when the user <NUM> walks on the pressure detection device <NUM> along the walking direction D1. After that, the gait evaluating device <NUM> may also obtain the first skeleton diagram <NUM> and the second skeleton diagram <NUM> respectively from the first walking image IM1 and the second walking image IM2, and project the first skeleton diagram <NUM> and the second skeleton diagram <NUM> into the first integrated skeleton diagram based on the aforementioned teaching.

In an embodiment, when human bodies other than that of the user <NUM> are present in the first walking image IM1 and the second walking image IM2, the gait evaluating device <NUM> may thus be unable to correctly obtain the integrated skeleton diagram corresponding to the user <NUM>. Therefore, in the embodiments of the invention, human bodies other than that of the user <NUM> may be excluded through a specific mechanism, thereby increasing the gait evaluation accuracy.

According to the invention, after obtaining the first integrated skeleton diagram, the gait evaluating device <NUM> further determines whether the first integrated skeleton diagram satisfies a specified condition. If so, the gait evaluating device <NUM> then obtains the angle values of the joint angles, and take the angle values as the walking limb feature values of the user <NUM> at the t-th time point.

In an embodiment, the gait evaluating device <NUM> may determine whether the first walking image IM1 and the second walking image IM2 do not include skeleton diagrams corresponding to other human bodies. If so, this means that the first skeleton diagram <NUM> and the second skeleton diagram <NUM> correspond to the human body (i.e., the user <NUM>) to be performed with gait evaluation at present. Therefore, the gait evaluating device <NUM> may correspondingly determine that the first integrated skeleton diagram satisfies the specified condition. If not, this means that skeleton diagrams corresponding to other human bodies are present in the first walking image IM1 and the second walking image IM2. Therefore, the gait evaluating device <NUM> may perform further screening to find the integrated skeleton diagram actually corresponding to the user <NUM>. The related details accompanied with <FIG> will be further described.

With reference to <FIG>, which is a schematic diagram illustrating screening of an integrated skeleton diagram according to the first embodiment of the invention. In this embodiment, it is assumed that the first walking image IM1 and the second walking image IM2 obtained at the t-th time point are as shown in <FIG>.

From <FIG>, it can be seen that the first walking image IM1 includes a first skeleton diagram <NUM> and a third skeleton diagram <NUM>, and the second walking image IM2 includes a second skeleton diagram <NUM> and a fourth skeleton diagram <NUM>. The first skeleton diagram <NUM> and the second skeleton diagram <NUM> correspond to the user to be performed with gait evaluation at present, and the third skeleton diagram <NUM> and the fourth skeleton diagram <NUM> correspond to another human body.

In this case, the gait evaluating device <NUM> may project the first skeleton diagram <NUM> and the second skeleton diagram <NUM> into a first integrated skeleton diagram <NUM>, and project the third skeleton diagram <NUM> and the fourth skeleton diagram <NUM> into a second integrated skeleton diagram <NUM>.

Then, the gait evaluating device <NUM> may obtain a first projection error of the first integrated skeleton diagram <NUM> and a second projection error of the second integrated skeleton diagram <NUM>, and determine whether the first projection error is less than the second projection error.

In the scenario of <FIG>, assuming that the first projection error is determined to be less than the second projection error, the gait evaluating device <NUM> may determine that the first integrated skeleton diagram <NUM> satisfies the specified condition, and may obtain the angle values of the joint angles in the first integrated skeleton diagram <NUM>. After that, the gait evaluating device <NUM> may then take the angle values as the walking limb feature values of the user <NUM> at the t-th time point.

In other embodiments, in response to determining that the first projection error is not less than the second projection error, this means that the first integrated skeleton diagram <NUM> does not correspond to the human body to be performed with gait evaluation. Therefore, the gait evaluating device <NUM> may determine that the first integrated skeleton diagram <NUM> does not satisfy the specified condition. After that, the gait evaluating device <NUM> may obtain the walking limb feature values of the user <NUM> at the t-th time point based on the second integrated skeleton diagram <NUM>.

Accordingly, even in a case where the gait evaluating system <NUM> of the first embodiment is disposed in a general field not dedicated to gait detection, in the embodiments of the invention, the target to be performed with gait evaluation may still be evaluated after other irrelevant human bodies are excluded. Accordingly, an effect that the target may be evaluated without noticing that the target is being evaluated can be achieved.

In other embodiments, the gait evaluating system <NUM> in <FIG> and <FIG> may also include more video cameras to capture images of the user <NUM> from different angles. In this case, the gait evaluating device <NUM> may correspondingly obtain a more accurate integrated skeleton diagram, but it is not limited thereto.

With reference to <FIG>, which is a schematic diagram illustrating a pressure detection device according to a second embodiment of the invention. In <FIG>, the pressure detection device <NUM> may be embodied as a pressure detection insole including a plurality of pressure detectors. In an embodiment, the pressure detection device <NUM> may be disposed in the shoes of the user <NUM> for the user <NUM> to wear and walk in. In this case, the pressure detection insole may detect the pressure value PV of each step of the user <NUM> when the user <NUM> walks, and may provide the pressure value PV corresponding to each step to the gait evaluating device <NUM>. In the second embodiment, for the relevant measurement means, reference may be made to the content of the literature document "<NPL>", which will not be repeatedly described herein.

In a third embodiment, the limb sensing devices <NUM> to 13Z may also be embodied as a plurality of dynamic capturing elements (e.g., inertial measurement units) that may be worn on the user <NUM>. The dynamic capturing elements may be distributed at the joints (e.g., neck, shoulders, elbows, wrists, hips, knees, ankles, etc.) of the user <NUM> to capture movements of the joints.

For example, the gait evaluating device <NUM> may obtain, at the t-th time point, a plurality of three-dimensional spatial positions of the dynamic capturing elements, and accordingly establish a spatial distribution diagram of the dynamic capturing elements at the t-th time point. The spatial distribution diagram at the t-th time point may include a plurality of reference points corresponding to the dynamic capturing elements.

After that, according to the relative position between the joints of the user <NUM>, the gait evaluating device <NUM> may connect the reference points in the spatial distribution diagram into the skeleton diagram (which may have an aspect similar to that of the first integrated skeleton diagram <NUM> of <FIG>) of the user <NUM> at the t-th time point. The skeleton diagram may include the joint angles of the joints at the t-th time point. Then, the gait evaluating device <NUM> may obtain the angle values of the joint angles, and take the angle values as the walking limb feature values of the user <NUM> at the t-th time point.

In the third embodiment, for the details of detection through the dynamic capturing elements, reference may be made to the content of the literature documents "<NPL>" and "<NPL>", which will not be repeatedly described herein.

In an embodiment, each joint of the user <NUM> may be predetermined with a corresponding angle range of motion. After obtaining the skeleton diagram of the user <NUM> at the t-th time point, the gait evaluating device <NUM> may determine whether the angle value of any joint angle in the skeleton diagram does not fall within the corresponding angle range of motion. If so, this means that the current skeleton diagram may contain a detection error, so the gait evaluating device <NUM> may correspondingly discard the skeleton diagram at the t-th time point.

For example, assuming that the angle range of motion corresponding to the elbow joint is <NUM> degrees to <NUM> degrees. In this case, if the gait evaluating device <NUM> determines that the joint angle of the elbow in the skeleton diagram at the t-th time point is less than <NUM> degrees or greater than <NUM> degrees, the gait evaluating device <NUM> may correspondingly discard the skeleton diagram at the t-th time point, but it is not limited thereto.

In the embodiments of the invention, the processor <NUM> may access the modules and programming codes recorded in the storage circuit <NUM> to realize the gait evaluating method provided by the invention, which will be described in detail as follows.

With reference to <FIG>, which is a flowchart illustrating a gait evaluating method according to an embodiment of the invention. The method of the embodiment may be performed by the gait evaluating system <NUM> of <FIG>. Each of steps of <FIG> accompanied with the elements shown in <FIG> will be described in detail below.

First, in step S510, the processor <NUM> may obtain, from the pressure detection device <NUM>, a plurality of pressure values PV of the user <NUM> walking on the pressure detection device <NUM>. In different embodiments, the processor <NUM> may obtain the pressure values PV with reference to the description in the above embodiments, which will not be repeated herein.

In step S520, the processor <NUM> may obtain a plurality of step feature values of the user <NUM> based on the pressure values PV. In different embodiments, based on the pressure values PV, the processor <NUM> may obtain at least one of a gait speed, a step length, a stride length, a cadence, a step width, a gait cycle, a stance time, a swing time, a center of pressure, a moving trajectory, a double support time, and a foot pressure distribution of the user <NUM> as the step feature values.

In some embodiments, the processor <NUM> may also obtain a stride-to-stride variation of the user <NUM> based on the pressure values PV. The stride-to-stride variation may include, but is not limited to, at least one of a swing time variation, a double support time variation, a step length time variation, and a stride length time variation.

In some embodiments, the user <NUM> may perform a timed up and go test (TUG) on the pressure detection device <NUM> upon request. In this case, based on the pressure values PV, the processor <NUM> may also obtain at least one of a get-up time, a turn time, a sit-down time, a walk speed, a walk time, and a total performance time of the user <NUM> in the timed up and go test as part of the step feature values. Nonetheless, the disclosure is not limited thereto.

With reference to <FIG>, which is a schematic diagram illustrating a plurality of step feature values according to an embodiment of the invention. <FIG> illustrates the difference between the terms such as step length, stride length, step width, and the like. For further details of the step feature values, reference may be made to the literature documents "<NPL>" and "<NPL>", which will not be repeatedly described herein.

Besides, for the details of obtaining the step feature values based on the pressure values PV, reference may be made to the literature documents "<NPL>" and "<NPL>", which will not be repeatedly described herein.

In step S530, based on sensing data provided by the limb sensing devices <NUM> to 13Z, the processor <NUM> may obtain a plurality of walking limb feature values when the user <NUM> walks on the pressure detection device. In different embodiments, the processor <NUM> may obtain the walking limb feature values (e.g., a plurality of angle values of a plurality of joint angles of the user <NUM>) based on the sensing data (e.g., the first walking image IM1 and the second walking image IM2) provided by the limb sensing devices <NUM> to 13Z with reference to the description in the above embodiments, which will not be repeated herein.

Then, in step S540, the processor <NUM> may evaluate a gait of the user <NUM> based on the step feature values and the walking limb feature values. In different embodiments, the processor <NUM> may evaluate the gait of the user <NUM> based on different ways, which will be further described below.

In a fourth embodiment, the processor <NUM> may determine whether the step feature values and the walking limb feature values of the user <NUM> do not satisfy a corresponding first statistical standard. In response to determining that Y of the step feature values and the walking limb feature values of the user <NUM> (where Y is a specified number) does not satisfy the corresponding first statistical standard, the processor <NUM> may determine that the gait of the user <NUM> belongs to an abnormal gait, and in the opposite case, the processor <NUM> may determine that the gait of the user <NUM> belongs to a normal gait.

In different embodiments, the first statistical standard corresponding to the step feature values and the walking limb feature values may be determined in different ways.

For example, an average gait speed of males in the sixties is statistically <NUM>/s. Accordingly, when the user <NUM> is a male between <NUM> and <NUM> years old, the first statistical standard corresponding to the gait speed may be set to <NUM>/s. Besides, since an average gait speed of healthy elder people is statistically <NUM>/s to <NUM>/s, when the user <NUM> is an elder person, the first statistical standard corresponding to the gait speed may be set to <NUM>/s. Nonetheless, the disclosure is not limited thereto.

In an embodiment, the normal stride length of ordinary people is about <NUM> to <NUM> on average, so the first statistical standard corresponding to the stride length of the user <NUM> may be set to <NUM>. Nonetheless, the disclosure is not limited thereto.

Based on a similar concept to the above teaching, the processor <NUM> may also correspondingly determine the first statistical standard corresponding to the step feature values and the walking limb feature values, for example, the cadence, a TUG time, a torso inclination angle, the stride-to-stride variation, a heel strike angle, and a toe-off angle based on the relevant literature documents/statistical data (e.g., the content of "<NPL>", "<NPL>", and "<NPL>").

For example, the first statistical standard corresponding to the cadence may be <NUM> times/s, and the first statistical standard corresponding to the TUG time may be less than <NUM> seconds. In addition, the first statistical standard of the torso inclination angle is, for example, that a square root of the sum of squares of the total inclination angles toward the front and back/the left and right must be less than <NUM> degrees. The first statistical standard of the stride-to-stride variation is, for example, that the step length time variation must be less than <NUM>%, the swing time variation must be less than <NUM>%, the double support time variation must be less than <NUM>%, the stride length time variation must be less than <NUM>%, and the like. Nonetheless, the disclosure is not limited thereto.

Besides, the first statistical standard of the heel strike angle, for example, must be greater than <NUM> degrees, and the first statistical standard of the toe-off angle, for example, must be greater than <NUM> degrees. Nonetheless, the disclosure is not limited thereto.

In an embodiment, when the user <NUM> belongs to a specific group including a plurality of group members, the processor <NUM> may also determine the first statistical standard corresponding to each step feature value and each walking limb feature value based on the properties of the specific group.

For example, the processor <NUM> may obtain a plurality of reference step feature values and a plurality of reference walking limb feature values of the group members of the specific group, and accordingly estimate the first statistical standard of each of the step feature values and each of the walking limb feature values. In some embodiments, the reference step feature values and the reference walking limb feature values of each group member may correspond to the step feature values and the walking limb feature values of the user A.

For example, when obtaining the first statistical standard corresponding to the stride length, the processor <NUM> may obtain the stride length of each group member, and then take the first <NUM>% of the stride lengths of the group members as the first statistical standard of the stride length. In this case, when the stride length of the user <NUM> falls within the last <NUM>% of the specific group, the processor <NUM> may then determine that the stride length of the user <NUM> does not satisfy the corresponding first statistical standard. For other step feature values and other walking limb feature values, the processor <NUM> may determine the corresponding first statistical standard based on a similar principle, the details of which will not be repeatedly described herein.

In an embodiment, the processor <NUM> may also determine the first statistical standard corresponding to each step feature value and each walking limb feature value based on previously measured historical step feature values and historical walking limb feature values of the user <NUM>.

In an embodiment, the processor <NUM> may obtain the step feature values and the walking limb feature values of the user <NUM> measured in the previous test as the historical step feature values and the historical walking limb feature values of the user <NUM>. After that, the processor <NUM> may determine the first statistical standard of each of the step feature values and each of the walking limb feature values of the user <NUM> based on a specific ratio of each of the historical step feature values and each of the historical walking limb feature values.

For example, when determining the first statistical standard of the stride length of the user <NUM>, the processor <NUM> may obtain the previously measured stride length (hereinafter referred to as historical stride length) of the user <NUM>, and take a specific ratio (e.g., <NUM>%) of historical stride length as the first statistical standard of the stride length of the user <NUM>. When the processor <NUM> determines that the stride length of the user <NUM> does not satisfy the corresponding first statistical standard (e.g., the stride length of the user <NUM> is less than <NUM>% of the historical stride length), this means that the stride length of the user <NUM> has shown a certain extent of regression (e.g., regression by more than <NUM>%), which may thus be used as a basis for determining that the gait of the user <NUM> is abnormal. For other step feature values and other walking limb feature values, the processor <NUM> may determine the corresponding first statistical standard based on a similar principle, the details of which will not be repeatedly described herein.

In different embodiments, the value of Y may be set by the designer depending on the needs. For example, in a case where Y is set to <NUM>, the processor <NUM> may determine that the gait of the user <NUM> belongs to an abnormal gait when any one of the step feature values and the walking limb feature values of the user <NUM> does not satisfy the corresponding first statistical standard. Moreover, in a case where Y is set to <NUM>, the processor <NUM> may determine that the gait of the user <NUM> belongs to an abnormal gait when any two of the step feature values and the walking limb feature values of the user <NUM> do not satisfy the corresponding first statistical standard. Nonetheless, the disclosure is not limited thereto.

In a fifth embodiment, the processor <NUM> may select an N number of specific values from the step feature values and the walking limb feature values of the user <NUM>, and may map the specific values into a plurality of map values according to a K number of reference bases corresponding to each specific value, where N and K are positive integers, and each map value falls within a predetermined range.

After that, the processor <NUM> may perform a weighting operation on the map values to obtain a weighting operation result. Then, in response to determining that the weighting operation result does not satisfy a second statistical standard, the processor <NUM> may determine that the gait of the user <NUM> belongs to an abnormal gait, and in the opposite case, the processor <NUM> may determine that the gait of the user <NUM> belongs to a normal gait. Nonetheless, the disclosure is not limited thereto.

In an embodiment, for a first specific value in the specific values, the processor <NUM> may obtain a reference mean and a reference difference factor corresponding to the first specific value, accordingly estimate the reference bases corresponding to the first specific value.

In an embodiment, the reference mean may be represented as M, and the reference difference factor may be represented as S. In an embodiment, the reference bases corresponding to the first specific value may be represented as M+iS, where i is an integer, i ∈ [-a,. , +a], and a is a positive integer.

With reference to <FIG>, which is a schematic diagram illustrating a plurality of reference bases for determining a first specific value according to an embodiment of the invention. In <FIG>, assuming that a is <NUM>, then the reference bases may respectively be M-<NUM>, M-S, M, M+S, and M+<NUM>, but are not limited thereto.

Based on the architecture of <FIG>, the processor <NUM> may map the first specific value into a first map value in the map values. In an embodiment, in response to determining that the first specific value is between the j-th reference basis and the j+ I-th reference basis, the processor <NUM> may determine that the first map value is j+<NUM>+b, where <NUM> ≤ j ≤ K - <NUM>, and b is a constant. In response to determining that the first specific value is less than the first reference basis (e.g., M-<NUM>), the processor <NUM> may determine that the first map value is <NUM>+b. In response to determining that the first specific value is greater than the K-th reference basis (e.g., M+<NUM>), the processor <NUM> may determine that the first map value is K + <NUM> +b.

For ease of description, it is assumed that b is <NUM> in the following, but the invention is not limited thereto. In this case, when the first specific value is less than the first reference basis (e.g., M-<NUM>), the processor <NUM> may map the first specific value into <NUM>. When the first specific value is between the first reference basis (i.e., M-<NUM>) and the second reference basis (i.e., M-S), the processor <NUM> may map the first specific value into <NUM>. When the first specific value is between the second reference basis (i.e., M-S) and the third reference basis (i.e., M), the processor <NUM> may map the first specific value into <NUM>. When the first specific value is between the third reference basis (i.e., M) and the fourth reference basis (i.e., M+S), the processor <NUM> may map the first specific value into <NUM>. When the first specific value is between the fourth reference basis (i.e., M+S) and the fifth reference basis (M+<NUM>), the processor <NUM> may map the first specific value into <NUM>. When the first specific value is greater than the fifth reference basis (e.g., M+<NUM>), the processor <NUM> may map the first specific value into <NUM>. Nonetheless, the disclosure is not limited thereto.

In the scenario of <FIG>, it can be seen that the predetermined range of the first map value is, for example, <NUM>+b, <NUM>+b, <NUM>+b, <NUM>+b, <NUM>+b, and <NUM>+b. In other embodiments, for other specific values, the processor <NUM> may map each of the specific values into the corresponding map values based on the above teaching, and the map values may have the same predetermined range as that of the first map value. Nonetheless, the disclosure is not limited thereto.

In different embodiments, the processor <NUM> may determine the reference mean (i.e., M) and the reference difference factor (i.e., S) of the first specific value based on different principles.

For example, assuming that the gait speed is the first specific value under consideration, then the processor <NUM> may obtain a mean of the general normal gait speed as the reference mean of the first specific value, and then take the specific ratio of the mean as the reference difference factor based on the relevant literature documents (e.g., "<NPL>" or "<NPL>"). For example, assuming that the specific ratio is <NUM>%, then the reference bases corresponding to the gait speed may be, for example but not limited to, <NUM>%, <NUM>%, <NUM>%, <NUM>%, and <NUM>% of M.

For another example, assuming that the forward torso inclination angle is the first specific value under consideration, then the processor <NUM> may obtain a mean of the general normal forward torso inclination angle as the reference mean of the first specific value, and then take the specific ratio of the mean as the reference difference factor based on the relevant literature documents (e.g., "<NPL>"). For example, assuming that the specific ratio is <NUM>%, then the reference bases corresponding to the forward torso inclination angle may be, for example but not limited to, <NUM>%, <NUM>%, <NUM>%, <NUM>%, and <NUM>% of M. For other first specific values, the processor <NUM> may determine the corresponding reference bases based on the above teaching, the details of which will not be repeatedly described herein.

In some embodiments, the processor <NUM> may also find a first reference value corresponding to the first specific value from the reference step feature values and the reference walking limb feature values of each group member in the specific group. After that, the processor <NUM> may then obtain a mean and a standard deviation of the first reference value of each group member, and define the mean and the standard deviation respectively as the reference mean (i.e., M) and the reference difference factor (i.e., S) of the first specific value.

For example, assuming that the first specific value is the stride length of the user <NUM>, then the processor <NUM> may find the stride length of each group member as the first reference value of each group member, and accordingly estimate a mean and a standard deviation of the stride length of each group member. After that, the processor <NUM> may take the mean and the standard deviation as the reference mean (i.e., M) and the reference difference factor (i.e., S) of the first specific value, and accordingly determine the reference bases corresponding to the stride length.

For another example, assuming that the first specific value is the gait speed of the user <NUM>, then the processor <NUM> may find the gait speed of each group member as the first reference value of each group member, and accordingly estimate a mean and a standard deviation of the gait speed of each group member. After that, the processor <NUM> may take the mean and the standard deviation as the reference mean (i.e., M) and the reference difference factor (i.e., S) of the first specific value, and accordingly determine the reference bases corresponding to the gait speed.

After obtaining an N number of map values of the N number of specific values, the processor <NUM> may perform the weighting operation on the map values to generate the weighting operation result. In an embodiment, the respective weights of the N number of map values may be determined by the designer depending on the needs. For example, assuming that the N number of specific values are the gait speed and the torso inclination angle of the user <NUM>, then after mapping the gait speed and the torso inclination angle of the user <NUM> into two corresponding map values, the processor <NUM> may obtain the corresponding weighting operation result based on formula " P<NUM> × W<NUM> + P<NUM> × W<NUM> ", where P<NUM> and P<NUM> are the map values respectively corresponding to the gait speed and the torso inclination angle, and W<NUM> and W<NUM> are weights (both of which may be <NUM>%, for example) respectively corresponding to P<NUM> and P<NUM>. Nonetheless, the disclosure is not limited thereto.

After that, the processor <NUM> may determine whether the weighting operation result satisfies the second statistical standard. In some embodiments, the processor <NUM> may determine the second statistical standard based on a mechanism below.

For example, the processor <NUM> may obtain an N number of reference values corresponding to the N number of specific values from the reference step feature values and the reference walking feature values of each group member of the specific group. Following the above example, assuming that the gait speed and the torso inclination angle of the user <NUM> are the N number of specific values under consideration, then the processor <NUM> may obtain the gait speed and the torso inclination angle of each group member as the N number of reference values of each group member.

After that, the processor <NUM> may map the N number of reference values of each group member into a plurality of reference map values according to the reference bases corresponding to each specific value, where each reference map value falls within the predetermined range. In an embodiment, the processor <NUM> may map the N number of reference values of each group member into the corresponding reference map values with reference to mapping the first specific value of the user <NUM> into the corresponding first map value. Therefore, the details will not be repeatedly described herein.

Then, the processor <NUM> may perform a weighting operation on the N number of reference map values of each group member to generate a reference weighting operation result of each group member. Following the above example, after mapping the gait speed and the torso inclination angle of a certain group member into two corresponding reference map values, the processor <NUM> may obtain the corresponding reference weighting operation result based on formula " P'<NUM> × W<NUM> + P'<NUM> × W<NUM> ", where P'<NUM> and P'<NUM> are the reference map values respectively corresponding to the gait speed and the torso inclination angle of the certain group member.

After that, the processor <NUM> may determine the second statistical standard based on the reference weighting operation result of each group member. In an embodiment, the processor <NUM> may, for example, take the last <NUM>% of the reference weighting operation results of the group members as the second statistical standard. In this case, in response to determining that the weighting operation result of the user <NUM> falls within the last <NUM>% of the reference weighting operation results of the group member, the processor <NUM> may determine that the weighting operation result of the user <NUM> satisfies the second statistical standard. On the other hand, in response to determine that the weighting operation result of the user <NUM> falls within the top <NUM>% of the reference weighting operation results of the group member, the processor <NUM> may determine that the weighting operation result of the user <NUM> does not satisfy the second statistical standard. Nonetheless, the disclosure is not limited thereto.

In an embodiment, in the case where it is determined that the gait of the user <NUM> belongs to an abnormal gait, the processor <NUM> may further determine whether the gait of the user <NUM> belongs to a non-neuropathic gait or a neuropathic gait.

In an embodiment, the processor <NUM> may determine whether the stride-to-stride variation of the user <NUM> satisfies a third statistical standard. If so, the processor <NUM> may determine that the gait of the user <NUM> belongs to a neuropathic gait, and in the opposite case, the processor <NUM> may determine that the gait of user belongs to a non-neuropathic gait.

In an embodiment, the processor <NUM> may determine the third statistical standard based on the stride-to-stride variation of each group member in the specific group. For example, the processor <NUM> may take the first <NUM>% of the stride-to-stride variations of the group members as the third statistical standard. In this case, in response to determining that the stride-to-stride variation of the user <NUM> falls within the top <NUM>% of the stride-to-stride variations of the group members, the processor <NUM> may determine that the stride-to-stride variation of the user <NUM> satisfies the third statistical standard. On the other hand, in response to determining that the stride-to-stride variation of the user <NUM> falls within the last <NUM>% of the stride-to-stride variations of the group members, the processor <NUM> may determine that the stride-to-stride variation of the user <NUM> does not satisfy the third statistical standard. Nonetheless, the disclosure is not limited thereto.

In an embodiment, in response to determining that the gait of the user <NUM> belongs to an abnormal gait, the processor <NUM> may also provide a corresponding enablement suggestion.

For example, assuming that the gait of the user <NUM> is a non-neuropathic gait (e.g., gait abnormality resulting from bow legs, knock knees, or the like), the processor <NUM> may provide a strength training suggestion corresponding to the non-neuropathic gait as the enablement suggestion. In an embodiment, the strength training suggestion may base its content on the relevant literature documents of physical therapy (e.g., literature documents of strength training for treatment of bow legs or knock knees). Nonetheless, the disclosure is not limited thereto.

In addition, assuming that the gait of the user <NUM> belongs to a neuropathic gait (e.g., gait abnormality caused by Parkinson's disease or Alzheimer's disease), then the processor <NUM> may provide a rhythmic gait training suggestion corresponding to the neuropathic gait as the enablement suggestion. For the content of the rhythmic gait training suggestion, reference may be made to literature documents, for example but not limited to, "<NPL>" and "<NPL>".

Claim 1:
A gait evaluating device (<NUM>), comprising:
a storage circuit, storing a programming code;
a processor, coupled to the storage circuit and accessing the programming code to perform:
obtaining (S510), from a pressure detection device (<NUM>), a plurality of pressure values (PV) of a user (<NUM>) walking on the pressure detection device (<NUM>), wherein the pressure values (PV) correspond to a plurality of steps of the user (<NUM>);
obtaining (S520) a plurality of step feature values of the user (<NUM>) based on the pressure values (PV);
obtaining (S530) a plurality of walking limb feature values when the user (<NUM>) walks on the pressure detection device (<NUM>) based on a sensing data provided by at least one limb sensing device (<NUM>-13Z),
wherein
the at least one limb sensing device comprises at least a first video camera and a second video camera having different imaging ranges, and wherein
the processor is adapted to perform:
obtaining, at a t-th time point, a first walking image captured by the first video camera when the user walks on the pressure detection device, and obtaining a first skeleton diagram in the first walking image;
obtaining, at the t-th time point, a second walking image captured by the second video camera when the user walks on the pressure detection device, and obtaining a second skeleton diagram in the second walking image, wherein the first skeleton diagram and the second skeleton diagram correspond to a first human body;
projecting, based on a relative position between the first video camera and the second video camera, the first skeleton diagram and the second skeleton diagram into a first integrated skeleton diagram, the first integrated skeleton diagram comprising a plurality of joint angles at the t-th time point, wherein the joint angles correspond to a plurality of joints of the first human body; and
in response to determining that the first integrated skeleton diagram satisfies a specified condition, obtaining a plurality of angle values of the joint angles, and taking the angle values as the walking limb feature values of the user at the t-th time point; and
evaluating (S540) a gait of the user (<NUM>) based on the step feature values and the walking limb feature values.