Method for diagnosing early signs of sarcopenia and device thereof

Abstract

Provided is a sarcopenia diagnosis method executed by a sarcopenia diagnosis device operated by at least one processor, the sarcopenia diagnosis method comprising the steps of: monitoring gait propulsion variability on the basis of gait data collected during walking; and diagnosing early signs of sarcopenia when the gait propulsion variability is not smaller than a threshold value.

Description

Method and device for diagnosing early signs of sarcopenia

[0001] The present disclosure relates to a diagnostic technique for sarcopenia.

[0002] Sarcopenia is a condition characterized by a decrease in skeletal muscle mass and strength, leading to functional decline, making early diagnosis and intervention crucial.

[0003] A decrease in usual gait speed is known as one of the diagnostic indicators for evaluating sarcopenia.

[0004] During walking, the human body determines its preferred speed based on energy optimization; however, as the body ages, muscle efficiency declines due to reduced lower limb muscle mass and decreased stiffness of the Achilles tendon. Consequently, to compensate for energy loss caused by collisions during walking, the shin muscles must generate greater propulsion to maintain the existing speed, which in turn requires more metabolic energy. If this situation persists, walking speed naturally converges to a lower rate to minimize energy consumption.

[0005] As such, changes in walking speed due to muscle loss can be described as a result that occurs after the process of decreased muscle strength and increased metabolic energy has already been completed. Therefore, diagnostic indicators for sarcopenia based on walking speed have limitations in early diagnosis because they show changes only after the decline in muscle strength has already progressed significantly.

[0006] The present disclosure provides a method and apparatus for diagnosing early signs of sarcopenia by monitoring variability in walking propulsion based on walking data collected during walking.

[0007] The present disclosure provides a method and apparatus for diagnosing early signs of sarcopenia based on the variability of walking propulsion force associated with changes in muscle strength of lower leg muscles, including calf muscles, and providing an early diagnostic biomarker including the variability of walking propulsion force, by collecting walking data including a Ground Reaction Force (GRF) in the fore-and-aft direction, which is expressed as a continuous force that starts from a braking force that slows down forward movement when the foot lands on the ground during walking and is converted into a propulsive force that pushes the body forward.

[0008] The present disclosure provides a method and apparatus for monitoring variability in walking propulsion and walking speed based on walking data collected during walking, and diagnosing early signs of sarcopenia based thereon.

[0009] According to one feature, a method for diagnosing sarcopenia using a sarcopenia diagnostic device operated by at least one processor comprises the step of monitoring gait propulsion variability based on gait data collected during walking, and the step of diagnosing early signs of sarcopenia if the gait propulsion variability is greater than or equal to a threshold value.

[0010] The above walking data includes walking speed, the monitoring step further monitors the variability of the walking speed, and the diagnosing step can diagnose early signs of sarcopenia if the variability of the walking propulsion force and the variability of the walking speed are greater than a threshold set for each.

[0011] The above walking data includes a Ground Reaction Force (GRF) in the fore-and-aft direction, which is expressed as a continuous force that starts from a braking force that slows down forward movement as the foot lands on the ground during walking and is converted into a propulsive force that pushes the body forward by pushing off the ground, and the propulsive force may be associated with changes in muscle strength of the lower limb muscles, including the calf muscles.

[0012] The above monitoring step can monitor the variability of the maximum value of the propulsion force among the ground reaction forces.

[0013] The above monitoring step can monitor the variability of the thrust force within a critical range set based on the maximum value of the thrust force among the ground reaction forces.

[0014] The above monitoring step can monitor the variability of the total amount of propulsion used from the moment when the braking force among the ground reaction forces becomes zero (0) and the center of gravity of the body is located right above the supporting foot until the moment when the propulsion force reaches a maximum value and then becomes zero (0) again.

[0015] The above monitoring step may monitor the variability of the total amount of thrust based on one or more of the average value or integral value of the total amount of thrust.

[0016] After the above diagnostic step, the method may further include a step of providing an early diagnostic biomarker for sarcopenia to a user terminal, the biomarker including gait propulsion variability and gait speed variability that are above each of the above thresholds.

[0017] According to another feature, a sarcopenia diagnostic device comprises a memory and at least one processor that executes instructions stored in the memory, and the processor can be implemented to monitor gait propulsion variability based on gait data collected during walking by executing the instructions, and to diagnose early signs of sarcopenia if the gait propulsion variability is greater than a threshold value.

[0018] The processor can be implemented to monitor the variability of walking propulsion and walking speed based on the walking data, and to diagnose early signs of sarcopenia if the variability of walking propulsion and the variability of walking speed are greater than their respective set thresholds.

[0019] The processor may be implemented to collect walking data including ground reaction force (GRF) in the fore-and-aft direction and to monitor the variability of walking propulsion force associated with changes in muscle strength of lower limb muscles, including calf muscles, among the ground reaction forces.

[0020] The above ground reaction force in the forward and backward directions is composed of a flow of force consisting of continuous braking force and propulsion force, and is expressed in the form of a two-dimensional waveform showing the change in force over time, which reaches a first point where the braking force is maximum at the stage where the foot lands on the ground, then gradually decreases and the braking force is converted into the propulsion force, then reaches a second point where the braking force becomes zero (0), then reaches a third point where the propulsion force is maximum, and then disappears as the foot falls. The processor can be implemented to monitor the variability of the propulsion force set using the value of the third point or the value of a critical interval set based on the third point.

[0021] The processor may be implemented to monitor the variability of the total amount of propulsion used until the moment the propulsion reaches zero (0) after reaching the third point from the second point.

[0022] The above processor may be implemented to monitor the variability of the total amount of thrust based on one or more of the average value or integral value of the total amount of thrust.

[0023] The above processor may be implemented to generate and output early diagnostic biomarkers for sarcopenia, including gait propulsion variability and gait speed variability that are above each of the respective thresholds.

[0024] According to the embodiment, the possibility of early diagnosis of sarcopenia is improved by analyzing ground reaction force that reflects changes in walking propulsion of lower leg muscles, including calf muscles, and capturing early signs of sarcopenia.

[0025] In addition, since sarcopenia is diagnosed using ground reaction force naturally obtained during walking, the user's physical condition can be continuously monitored in a non-invasive manner, allowing for a user-friendly approach.

[0026] In addition, by tracking muscle weakness and increased energy consumption through long-term changes in ground reaction force, it is possible to establish customized prevention and treatment plans based on the individual patient's condition.

[0027] In addition, by quantitatively evaluating energy loss during walking and the compensatory action of the shin muscles, the effectiveness of exercise programs or rehabilitation plans aimed at improving muscle efficiency can be scientifically verified.

[0028] In addition, unlike conventional methods that simply measure walking speed, diagnostic accuracy can be improved by diagnosing early signs of sarcopenia based on walking propulsion and the variability of walking speed.

[0029] FIG. 1 illustrates the connection relationship between a sarcopenia diagnostic device and surrounding components according to one embodiment.

[0030] FIG. 2 is a flowchart illustrating a method for diagnosing sarcopenia according to one embodiment.

[0031] FIG. 3 shows the waveform of a ground reaction force according to one embodiment.

[0032] Figure 4 shows an example of the variability of walking propulsion force in Figure 3.

[0033] Figure 5 shows another example of the variability of walking propulsion force in Figure 3.

[0034] FIG. 6 is a flowchart illustrating a method for diagnosing sarcopenia according to another embodiment.

[0035] FIG. 7 is a block diagram showing the hardware configuration of a sarcopenia diagnostic device according to another embodiment.

[0036] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present disclosure in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0037] Throughout the specification, when a part is described as "including" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0038] Expressions described in the singular in this specification may be interpreted as singular or plural unless explicit expressions such as "one" or "single" are used.

[0039] In this specification, the same reference numeral refers to the same component regardless of the drawing, and "and / or" includes each of the mentioned components and all combinations of one or more.

[0040] In this specification, terms including ordinal numbers, such as 1st, 2nd, etc., may be used to describe various components, but said components are not limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present disclosure, a 1st component may be named a 2nd component, and similarly, a 2nd component may be named a 1st component.

[0041] Additionally, terms such as “…part,” “…unit,” and “…module” described in the specification refer to a unit that processes at least one function or operation, and this may be implemented in hardware, software, or a combination of hardware and software.

[0042] The device of the present disclosure is a computing device configured and connected so that at least one processor can perform the operation of the present disclosure by executing instructions. The computing device may include one or more processors, memory for loading a computer program executed by the processor, and storage for storing the computer program and various data. The computer program includes instructions that cause the processor to perform a method / operation according to various embodiments of the present disclosure and may be stored on a non-transitory computer-readable storage medium. The computer program may be downloaded over a network or sold in the form of a product.

[0043] The processor can perform methods / operations according to various embodiments of the present disclosure by executing instructions. The processor controls the overall operation of each component of the computing device. The processor may be configured to include at least one of a Central Processing Unit (CPU), a Micro Processor Unit (MPU), a Micro Controller Unit (MCU), a Graphic Processing Unit (GPU), or any type of processor well known in the art of the present disclosure.

[0044] Now, a method and apparatus for diagnosing early signs of sarcopenia according to an embodiment of the present invention will be described with reference to the drawings.

[0045] FIG. 1 illustrates the connection relationship between a sarcopenia diagnostic device and surrounding components according to one embodiment.

[0046] Referring to FIG. 1, a sarcopenia diagnostic device (100) according to one embodiment monitors variability in walking propulsion force based on walking data collected during walking, and if the variability in walking propulsion force is greater than a threshold value, it can diagnose early signs of sarcopenia.

[0047] At this time, the walking data may include ground reaction force (GRF) in the forward and backward directions.

[0048] Ground reaction force in the forward and backward directions is expressed as a continuous force that starts as a braking force that slows down forward movement when the foot lands on the ground during walking, and is converted into a propulsive force that pushes the ground forward to propel the body forward, that is, walking propulsion.

[0049] In this case, walking propulsion is associated with changes in the strength of the lower leg muscles. The lower leg muscles may include the calf muscles (Soleus, Gastonemeous), shin muscles (Tibialis), and tendons (Akillestendon Tendon).

[0050] When muscle strength decreases, lower limb muscle strength and calf muscle strength decrease, which not only causes a decrease in propulsion during walking but also reduces muscle efficiency due to a decrease in the elasticity of the Achilles tendon, so more energy must be consumed to walk at the same speed as before, so walking speed is reduced. In this way, the sarcopenia diagnostic device (100) can detect signs of sarcopenia early by monitoring propulsion variability during walking, as the decrease in propulsion precedes the decrease in walking speed.

[0051] The sarcopenia diagnostic device (100) receives walking data from the walking data providing device (200).

[0052] The walking data providing device (200) can measure the ground reaction force in the forward and backward directions during the walking cycle and generate walking data including this, and provide it to the sarcopenia diagnostic device (100).

[0053] The walking data providing device (200) can measure ground reaction force in the forward and backward directions in various ways, and there may be several examples as follows, but the method of measuring ground reaction force is not limited to a specific method.

[0054] According to one example, a walking data providing device (200) can generate a center of mass (CoM) trajectory of a user from a walking video of a user captured at each walking cycle, and estimate ground reaction force using the generated center of mass (CoM) trajectory and an elastic walking model. Here, the elastic walking model includes a leg spring modeled as a spring for the lower limbs of the human body, and a curvy foot modeled as a curved shape for the foot of the human body. Since the estimation of ground reaction force using the center of mass (CoM) trajectory and the elastic walking model in this manner uses known technology, a detailed description is omitted.

[0055] According to another example, the walking data providing device (200) can estimate ground reaction force using inertial data such as acceleration collected through an IMU (Inertial Measurement Unit) sensor, and since it uses known technology, a detailed description is omitted.

[0056] The sarcopenia diagnostic device (100) monitors the variability of the maximum value of the propulsive force among the ground reaction forces in the forward and backward directions according to the walking cycle, and based on this, can diagnose early signs of sarcopenia.

[0057] A sarcopenia diagnostic device (100) monitors the variability according to the walking cycle of the section in which the propulsion force of a threshold range set based on the maximum value of the propulsion force among ground reaction forces appears, and can diagnose early signs of sarcopenia based on this.

[0058] The sarcopenia diagnostic device (100) monitors the variability according to the walking cycle from the moment the braking force among the ground reaction forces becomes zero (0) and the center of gravity of the body is located right above the supporting foot to the point where the propulsion force reaches a maximum value, and based on this, can diagnose early signs of sarcopenia.

[0059] A sarcopenia diagnostic device (100) monitors the variability of the total amount of propulsion used according to the walking cycle from the moment when the braking force among the ground reaction forces becomes zero (0) and the center of gravity of the body is located right above the supporting foot until the propulsion force reaches a maximum value and then becomes zero (0), and based on this, can diagnose early signs of sarcopenia.

[0060] A sarcopenia diagnostic device (100) can generate and output an early sarcopenia diagnostic biomarker that includes large walking propulsion variability above a threshold value.

[0061] When the sarcopenia diagnostic device (100) is implemented as a standalone device or embedded in a wearable device, etc., it can output an early diagnosis biomarker for sarcopenia through a user interface.

[0062] When the sarcopenia diagnostic device (100) is connected to a user terminal (300) via a network, it can transmit an early sarcopenia diagnostic biomarker to the user terminal (300).

[0063] The operation of the sarcopenia diagnostic device (100) is described in detail as follows.

[0064] FIG. 2 is a flowchart illustrating a method for diagnosing sarcopenia according to one embodiment, FIG. 3 shows a waveform of ground reaction force according to one embodiment, FIG. 4 shows one example of variability of walking propulsion force in FIG. 3, and FIG. 5 shows another example of variability of walking propulsion force in FIG. 3.

[0065] Referring to FIG. 2, a sarcopenia diagnostic device (100) collects walking data during walking (S101) and monitors the variability of walking propulsion force according to the walking cycle (S102).

[0066] At this time, the sarcopenia diagnostic device (100) can collect walking data including ground reaction force (GRF) in the forward and backward directions and monitor the variability of walking propulsion force associated with changes in muscle strength of the lower leg muscles, including the calf muscles, among the ground reaction forces.

[0067] A sarcopenia diagnostic device (100) diagnoses early signs of sarcopenia if the monitored gait propulsion variability is greater than a threshold value (S103).

[0068] The sarcopenia diagnostic device (100) generates and outputs an early diagnostic biomarker including gait propulsion variability if it is greater than the threshold value diagnosed in S103 (S104).

[0069] Referring to Fig. 3, the ground reaction force in the forward and backward directions consists of a force flow composed of continuous walking braking force and walking propulsion force.

[0070] At this time, the flow of force consisting of braking force and propulsion force during walking is based on a single walking cycle (ab). A walking cycle can be defined as the time or movement interval from the moment one foot first touches the ground until the same foot touches the ground the next time. Therefore, ground reaction forces in the front-rear direction are formed as these walking cycles (ab) continue in succession.

[0071] The walking cycle (ab) is expressed as a two-dimensional waveform showing the change in force over time from the starting point (a) where the foot lands on the ground, to the first point (P1) where the walking braking force is maximum, then gradually decreasing until the braking force is converted into propulsion force, reaching the second point (P2) where the braking force becomes zero (0), then reaching the third point (P3) where the propulsion force is maximum, and finally to the moment (b) where the propulsion force disappears as the foot lands on the ground again.

[0072] In the two-dimensional waveforms of Figures 3, 4, and 5, the x-axis is set as time, and the y-axis is set as the amplitude, which is the magnitude of the force.

[0073] Since the walking cycle (ab) is continuous during walking, the ground reaction force in the overall forward and backward directions is expressed in the form of a continuous two-dimensional waveform.

[0074] At the moment when walking begins and the heel touches the ground (a), the body continues to move forward due to the inertia of moving forward, and at this time, the foot pushes the ground forward to control the body's speed. As a result, the ground exerts a force that pushes the foot backward as a reaction (negative direction force), that is, a braking force, and the first point (P1) is the moment when the braking force is strongest.

[0075] At the moment the center of gravity of the body passes completely over the supporting foot after passing through the braking phase, the force in the forward and backward directions becomes zero instantaneously. This moment of zero is the second point (P2), that is, the turning point where the braking force ends and the propulsive force pushing the body forward begins.

[0076] Subsequently, as the center of gravity moves forward past the foot, the foot forcefully pushes the body forward for the next step, and the ground applies a force (positive direction force), that is, propulsion, in reaction to this, pushing the foot forward. As the propulsion increases, it reaches a maximum value, i.e., the third point (P3), and then reaches the point (b) where the propulsion becomes zero. Figures 3, 4, and 5 represent this walking motion in the form of a two-dimensional waveform.

[0077] A sarcopenia diagnostic device (100) monitors the variability of walking propulsion whenever a user is walking, and if the variability of walking propulsion over time (e.g., day, month, year, clinical specific cycle, etc.) is greater than a threshold value, it can be diagnosed as an early sign of sarcopenia.

[0078] A sarcopenia diagnostic device (100) monitors one or more of the following as biomarker indicators: the variability of the maximum value of the propulsive force of the ground reaction force in the fore-and-aft direction, the variability of the propulsive force in a certain fore-and-aft ratio (e.g., 10%) set based on the maximum value of the propulsive force, and the variability of the force in the section providing propulsive force from the ground reaction force in the fore-and-aft direction. Based on this, it can diagnose early signs of sarcopenia. This is explained with reference to the drawings as follows.

[0079] The sarcopenia diagnostic device (100) can monitor the variability of the maximum value of the propulsive force of the ground reaction force in the forward and backward directions. That is, the sarcopenia diagnostic device (100) can monitor the variability of the value of the third point (P3) where the propulsive force is maximum.

[0080] At this time, the third point (P3) is the maximum value of the walking propulsion force, and the variability of the third point (P3) is related to changes in the muscle strength of the lower leg muscles resulting from sarcopenia.

[0081] Additionally, referring to FIG. 4, the sarcopenia diagnostic device (100) can monitor the variability of the propulsion force in the cd section, where the propulsion force in the threshold range set based on the third point (P3) appears. At this time, the threshold range can be set as a cd section corresponding to a certain ratio before and after the third point (P3), for example, 10%. The cd section can be set as a point before and after which the force, which is the value of the vertical axis of the third point (P3), becomes 90%. However, such ratios (10%, 90%) are examples and are not limited thereto.

[0082] Additionally, the sarcopenia diagnostic device (100) can monitor the variability of the force in the section providing propulsion from the front-rear ground reaction force.

[0083] The section providing propulsion from ground reaction force in the forward and backward directions is a continuous section of 'P2-P3-b', and the variability of the force in this propulsion-providing section can be calculated through one or more of the average value or integral value of the force in the propulsion-providing section.

[0084] Referring to FIG. 5, the sarcopenia diagnostic device (100) can monitor the variability of the total amount of propulsion force in the front-rear ground reaction force.

[0085] At this time, the total amount of propulsion is the total amount of propulsion used from the moment (P2) when the braking force becomes zero (0) and the center of gravity of the body is located right above the supporting foot, until the propulsion reaches a maximum value (P3) and reaches a point (b) where it becomes zero (0), and can be calculated as the integral value of the area (S).

[0086] Additionally, the area (S2) may represent the total amount of braking force used to decelerate the body moving forward from the time the foot touches the ground until the center of gravity of the body passes over the foot, i.e., until it reaches P2.

[0087] In addition, the sarcopenia diagnostic device (100) can monitor the variability of the total amount of propulsion based on the average value of the force in the section where propulsion is provided.

[0088] The sarcopenia diagnostic device (100) analyzes the variability of propulsion force by setting a threshold value based on the average value and standard deviation (95% confidence level) of propulsion force continuously monitored during the user's daily walking, and if propulsion force variability exceeding this threshold value appears, it can be diagnosed as an early sign of sarcopenia. That is, as a result of monitoring daily walking propulsion force based on one or more of the multiple biomarker indicators described above, if a large variability in walking propulsion force exceeding the average value and standard deviation occurs, it can be determined as a point requiring careful observation for sarcopenia, that is, as an early sign of sarcopenia.

[0089] For analyzing the variability of specific curve segments, areas, peak values, etc. in the two-dimensional waveform representing ground reaction force described above, known methods using standard deviation, integration, etc. may be used, and specific analysis methods are not specified.

[0090] FIG. 6 is a flowchart illustrating a method for diagnosing sarcopenia according to another embodiment.

[0091] At this time, the sarcopenia diagnostic device (100) described in FIGS. 1 to 5 additionally monitors not only variability in walking propulsion but also variability in walking speed, and based on this, can diagnose early signs of sarcopenia.

[0092] Since variability in ground reaction force also causes variability in walking speed, if variability exceeding the average value appears in walking speed, it can be diagnosed as an early sign of sarcopenia. An example of this embodiment is illustrated in FIG. 6. In this case, explanations identical to those in FIG. 1 through 5 are omitted, and the explanation focuses on the added details.

[0093] Referring to FIG. 6, the sarcopenia diagnostic device (100) collects walking data while walking (S201).

[0094] At this time, the walking data may include ground reaction force (GRF) and walking speed.

[0095] Walking speed can be measured in various ways during the user's walking and included in the walking data.

[0096] According to one example, based on research showing that ground reaction force (GRF) is closely correlated with walking speed, walking speed can be estimated based on ground reaction force data measured under various speed conditions, or based on a regression model between the peak value of the force or various feature parameters and walking speed.

[0097] According to another example, the walking cycle and stride length can be analyzed through ground reaction force (GRF), and walking speed can be estimated based on this.

[0098] According to another example, walking speed can be measured during walking through a sensor device (e.g., IMU sensor, etc.) worn by the user, and the measured walking speed can be synchronized with ground reaction force (GRF) and included in the walking data.

[0099] In addition, since walking speed can be estimated using various methods, it is not limited to a specific method.

[0100] The sarcopenia diagnostic device (100) monitors variability in walking propulsion and variability in walking speed based on collected walking data (S202).

[0101] A sarcopenia diagnostic device (100) diagnoses early signs of sarcopenia (S203) when the variability of walking propulsion is greater than a first threshold set for walking propulsion and the walking speed is greater than a second threshold set for walking speed.

[0102] At this time, the second threshold value may be set to a preset average value. The sarcopenia diagnostic device (100) monitors the variability of walking speed over time (e.g., day, month, year, clinical specific cycle, etc.) and may set the average value, standard deviation, etc. of the monitored past walking speeds as the second threshold value.

[0103] The sarcopenia diagnostic device (100) can generate and output early diagnostic biomarkers including gait propulsion variability and gait speed that are above each threshold value, namely, gait propulsion variability that is above the first threshold value and gait speed variability that is above the second threshold value (S204).

[0104] Meanwhile, FIG. 7 is a block diagram showing the hardware configuration of a sarcopenia diagnostic device according to another embodiment.

[0105] Referring to FIG. 7, the sarcopenia diagnostic device (100) described in FIG. 1 to 6 may be a computing device (400) operated by at least one processor.

[0106] A computing device (400) may include one or more processors (401), a memory (402) for loading a program executed by the processor (401), a storage device (403) for storing the program and various data, a communication device (404), and a bus (405) connecting them. In addition, the computing device (400) may include various additional components. The program may include instructions that cause the processor (401) to perform a method / operation according to various embodiments of the present disclosure when loaded into the memory (402). That is, the processor (401) can perform the method / operation according to various embodiments of the present disclosure by executing the instructions. An instruction refers to a series of computer-readable instructions grouped by function, which are components of a computer program and are executed by a processor.

[0107] The processor (401) controls the overall operation of each component of the computing device (400). The processor (401) may be configured to include at least one of a CPU (Central Processing Unit), MPU (Micro Processor Unit), MCU (Micro Controller Unit), GPU (Graphic Processing Unit), or any form of processor well known in the art of the present disclosure. Additionally, the processor (401) may perform operations for at least one application or program for executing a method / operation according to various embodiments of the present disclosure.

[0108] The memory (402) stores various data, commands and / or information. The memory (402) may load one or more computer programs from a storage device (403) to execute a method / operation according to various embodiments of the present disclosure. The memory (402) may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

[0109] The storage device (403) may store a computer program non-temporarily. The storage device (403) may be configured to include non-volatile memory such as ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), flash memory, a hard disk, a removable disk, or any form of computer-readable recording medium well known in the art to which this disclosure belongs. Here, the computer program may include instructions that implement the operations described in FIGS. 1 through 6.

[0110] The communication device (404) supports wired and wireless communication of the computing device (400). To this end, the communication device (404) may be configured to include a communication module well known in the art of the present disclosure.

[0111] The bus (405) provides communication functions between components of the computing device (400). The bus (405) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0112] The processor (401) can be implemented to monitor the variability of propulsion force according to the walking cycle based on walking data collected during walking, as described in FIGS. 1 to 6, and to diagnose early signs of sarcopenia if the variability of propulsion force is greater than a threshold value.

[0113] The processor (401) can be implemented to collect walking data including ground reaction force (GRF) in the forward and backward directions, as described in FIGS. 1 to 6, and to monitor the variability of the propulsive force associated with changes in muscle strength of the lower limb muscles among the ground reaction forces.

[0114] Additionally, the processor (401) can be implemented to monitor walking propulsion variability and walking speed variability based on walking data collected during walking, as described in FIGS. 1 to 6, and to diagnose early signs of sarcopenia based on this.

[0115] The embodiments of the present disclosure described above are not implemented only through devices and methods, but may also be implemented through a program that realizes a function corresponding to the configuration of the embodiments of the present disclosure or a recording medium on which such program is recorded.

[0116] Although embodiments of the present disclosure have been described in detail above, the scope of the present disclosure is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concepts of the present disclosure as defined in the following claims also fall within the scope of the present disclosure.

Claims

1. A method for diagnosing sarcopenia using a sarcopenia diagnostic device operated by at least one processor, wherein A step of monitoring variability in walking propulsion based on walking data collected during walking, and If the above gait propulsion variability is greater than a threshold value, the step of diagnosing early signs of sarcopenia A method for diagnosing sarcopenia including 2. In Paragraph 1, The above walking data includes walking speed, The above monitoring step is, Additionally monitoring the variability of the above walking speed, The above diagnostic step is, A method for diagnosing sarcopenia, wherein if the variability of walking propulsion force and the variability of walking speed are greater than or equal to a threshold set for each, an early sign of sarcopenia is diagnosed.

3. In Paragraph 2, The above walking data is, It includes the front-rear ground reaction force (GRF), which is expressed as a continuous force that starts from the braking force that slows down forward movement as the foot lands on the ground during walking, and converts into a propulsive force that pushes the body forward by pushing off the ground. The above driving force is, A diagnostic method for sarcopenia associated with changes in muscle strength of the lower limb muscles, including the calf muscles.

4. In Paragraph 3, The above monitoring step is, A method for diagnosing sarcopenia that monitors the variability of the maximum value of the propulsive force among the ground reaction forces.

5. In Paragraph 3, The above monitoring step is, A method for diagnosing sarcopenia, which monitors the variability of a propulsion force within a critical range set based on the maximum value of the propulsion force among the ground reaction forces.

6. In Paragraph 3, The above monitoring step is, A method for diagnosing sarcopenia, which monitors the variability of the total amount of propulsion force used from the moment the braking force becomes zero (0) among the ground reaction forces and the center of gravity of the body is located right above the supporting foot until the propulsion force reaches a maximum value and then becomes zero (0) again.

7. In Paragraph 6, The above monitoring step is, A method for diagnosing sarcopenia, which monitors the variability of the total amount of driving force using one or more of the average value or integral value of the total amount of driving force.

8. In Paragraph 3, After the above diagnostic step, A step of providing an early diagnostic biomarker for sarcopenia to a user terminal, comprising gait propulsion variability and gait speed variability that are greater than or equal to each of the above thresholds. A method for diagnosing sarcopenia that further includes 9. Memory, and It includes at least one processor that executes instructions stored in the memory, and The above processor executes the above instructions, A sarcopenia diagnostic device implemented to monitor variability in walking propulsion based on walking data collected during walking, and to diagnose early signs of sarcopenia if the variability in walking propulsion is greater than a threshold value.

10. In Paragraph 9, The above processor A sarcopenia diagnostic device implemented to monitor the variability of walking propulsion force and walking speed based on the walking data, and to diagnose early signs of sarcopenia if the variability of walking propulsion force and the variability of walking speed are greater than or equal to their respective set thresholds.

11. In Paragraph 10, The above processor is, A sarcopenia diagnostic device implemented to collect gait data including ground reaction force (GRF) in the fore-and-aft direction, and to monitor variability in gait propulsion force associated with changes in muscle strength of lower limb muscles, including calf muscles, among the ground reaction forces.

12. In Paragraph 11, The above ground reaction force in the front-rear direction is, It is expressed in the form of a two-dimensional waveform that represents the change in force over time, consisting of a flow of force composed of continuous braking force and propulsion force, reaching a first point where the braking force is maximum at the stage where the foot lands on the ground, then gradually decreasing so that the braking force is converted into propulsion force, reaching a second point where the braking force becomes zero (0), then reaching a third point where the propulsion force is maximum, and then disappearing as the foot falls. The above processor is, A sarcopenia diagnostic device implemented to monitor the variability of the driving force set using the value of the third point or the value of a threshold interval set based on the third point.

13. In Paragraph 12, The above processor is, A sarcopenia diagnostic device implemented to monitor the variability of the total amount of propulsion used from the second point to the third point until the moment it becomes zero (0).

14. In Paragraph 13, The above processor is, A sarcopenia diagnostic device implemented to monitor the variability of the total amount of thrust using one or more of the average value or integral value of the total amount of thrust.

15. In Paragraph 12, The above processor is, A sarcopenia diagnostic device implemented to generate and output early diagnostic biomarkers for sarcopenia, including gait propulsion variability and gait speed variability that are above each of the above thresholds.

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