Wearable device and operating method of wearable device

Abstract

Disclosed are a wearable device and a method for operating the wearable device. The wearable device processes state variables defined based on motion information of a user, determines an interaction mode of the wearable device based on a gain associated with a magnitude of a torque of the wearable device, selects a motion type from a plurality of motion types of the determined interaction mode based on a gait parameter of the user, determines a control factor of the torque based on the selected motion type, and generates the torque based on the processed state variables, the gain, and the determined control factor.

Description

[0001] This application claims priority from Korean Patent Application No. 10-2019-0117551 filed on September 24, 2019, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference in their entirety. Technical Field

[0002] At least one example embodiment relates to a wearable device. Background Art

[0003] The recent aging of society has resulted in an increasing number of people experiencing inconvenience and pain due to decreased muscle strength or joint problems caused by aging. Consequently, there is growing interest in walking assistance devices that enable elderly users or patients with decreased muscle strength or joint problems to walk with less effort. Furthermore, exercise assistance devices that can help increase a person's muscle strength are being developed. Summary of the Invention

[0004] Some example embodiments relate to a method of operating a wearable device.

[0005] In some example embodiments, the operating method may include: processing state variables defined based on motion information of a user, determining an interaction mode of the wearable device based on a gain associated with a magnitude of a torque of the wearable device, selecting a motion type from a plurality of motion types of the determined interaction mode based on a gait parameter of the user, determining a control factor of the torque based on the selected motion type, and generating a torque based on the processed state variables, the gain, and the determined control factor.

[0006] The step of processing the state variable may include smoothing the state variable.

[0007] In response to the gain being greater than or equal to a reference value and being a positive number, determining the interaction mode may include selecting a first interaction mode that assists the user in moving the user. In response to the gain being greater than or equal to a reference value and being a negative number, determining the interaction mode may include selecting a second interaction mode that applies resistance to the user's movement. In response to the gain being less than the reference value, determining the interaction mode may include selecting a third interaction mode that applies high resistance to the user's movement.

[0008] In response to the first gait parameter having a first gait characteristic value less than or equal to a first threshold, selecting the motion type may include determining the wearable device's motion type as a walking motion type. In response to the first gait characteristic value being greater than the first threshold and less than or equal to a second threshold, selecting the motion type may include determining the motion type as a walking-to-running motion type. In response to the first gait characteristic value being greater than a second threshold, selecting the motion type may include determining the motion type as a running motion type.

[0009] The first posture feature value may include the user's rhythm.

[0010] In response to the second gait characteristic value in the gait parameters being greater than a third threshold, selecting the exercise type may include determining the exercise type of the wearable device as a high-resistance exercise type. In response to the second gait characteristic value being less than a fourth threshold, selecting the exercise type may include determining the exercise type as a slow exercise type.

[0011] The second gait feature value may include an average value of angle curve lengths of two hip joints of the user during a preset period.

[0012] In response to a motion type change event occurring by selecting a motion type from the plurality of motion types, the step of determining the control factor may include adjusting at least one of a smoothing factor to be used to smooth a signal obtained by sensing the user's movement or a delay in an output timing of the torque.

[0013] In response to a motion type change event occurring by selecting the walking motion type from the plurality of motion types, the adjusting may include decreasing a smoothing factor and increasing a delay.

[0014] In response to an exercise type change event occurring by selecting the running exercise type from the plurality of exercise types, the adjusting may include increasing a smoothing factor and decreasing a delay.

[0015] The generating of the torque may include: applying the gain, the determined control factor, and the compensation factor to the processed state variable; and generating the torque based on a result of the applying.

[0016] The motion information may include the angles of the user's two hip joints.

[0017] Some example embodiments relate to a wearable device.

[0018] In some example embodiments, the wearable device may include: a controller configured to: process state variables defined based on motion information of a user, determine an interaction mode of the wearable device based on a gain associated with a magnitude of a torque of the wearable device, select a motion type from a plurality of motion types of the determined interaction mode based on a gait parameter of the user, determine a control factor of the torque based on the selected motion type, and control a driver based on the processed state variables, the gain, and the determined control factor; and a driver configured to: generate a torque under the control of the controller.

[0019] The controller can be configured to smooth the state variables.

[0020] In response to the gain being greater than or equal to a reference value and being a positive number, the controller may be configured to select a first interaction mode that assists the user in moving the user. In response to the gain being greater than or equal to the reference value and being a negative number, the controller may be configured to select a second interaction mode that applies resistance to the user's movement. In response to the gain being less than the reference value, the controller may be configured to select a third interaction mode that applies high resistance to the user's movement.

[0021] In response to the first gait parameter having a first gait characteristic value less than or equal to a first threshold, the controller may be configured to determine the motion type of the wearable device as a walking motion type. In response to the first gait characteristic value being greater than the first threshold and less than or equal to a second threshold, the controller may be configured to determine the motion type as a walking-to-running motion type. In response to the first gait characteristic value being greater than a second threshold, the controller may be configured to determine the motion type as a running motion type.

[0022] The first posture feature value may include the user's rhythm.

[0023] In response to a second gait characteristic value in the gait parameters being greater than a third threshold, the controller may be configured to determine the motion type of the wearable device as a high-resistance motion type. In response to the second gait characteristic value being less than a fourth threshold, the controller may be configured to determine the motion type as a slow motion type.

[0024] The second gait feature value may include an average value of angle curve lengths of two hip joints of the user during a preset period.

[0025] In response to a motion type change event occurring by selecting a motion type from the plurality of motion types, the controller may be configured to adjust at least one of a smoothing factor used to smooth a signal obtained by sensing the user's movement or a delay in output timing of a torque.

[0026] In response to a motion type change event occurring by selecting a walking motion type from the plurality of motion types, the controller may be configured to decrease a smoothing factor and increase a delay.

[0027] In response to an exercise type change event occurring by selecting a running exercise type from the plurality of exercise types, the controller may be configured to increase a smoothing factor and decrease a delay.

[0028] The controller may be configured to apply gains, determined control factors, and compensation factors to the processed state variables.

[0029] The motion information may include the angles of the user's two hip joints.

[0030] Additional aspects of example embodiments will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] These and / or other aspects will become clear and more readily understood from the following description of example embodiments taken in conjunction with the accompanying drawings, in which:

[0032] Figures 1 to 3 is a diagram illustrating an example of a wearable device according to at least one example embodiment;

[0033] Figures 4 to 7 is a diagram illustrating an example of an operation of a wearable device according to at least one example embodiment;

[0034] Figure 8 is a diagram illustrating an example of a second gait feature value according to at least one example embodiment;

[0035] Figure 9 is a diagram illustrating an example of a state machine of a wearable device according to at least one example embodiment;

[0036] Figure 10 is a flowchart illustrating an example of a method of operating a wearable device according to at least one example embodiment; and

[0037] Figure 11 is a diagram illustrating an example of a wearable device according to at least one example embodiment. DETAILED DESCRIPTION

[0038] Hereinafter, some example embodiments will be described in detail with reference to the accompanying drawings. With respect to the reference numerals assigned to the elements in the drawings, it should be noted that even if the same elements are shown in different drawings, the same elements will be represented by the same reference numerals as much as possible. In addition, in the description of the embodiments, when it is considered that a detailed description of a well-known related structure or function will lead to an obscure interpretation of the present disclosure, such description will be omitted.

[0039] However, it should be understood that there is no intention to limit this disclosure to the specific example embodiments disclosed. On the contrary, the example embodiments will cover all modifications, equivalents, and alternatives that fall within the scope of the example embodiments. Throughout the description of the drawings, the same reference numerals represent the same elements.

[0040] In addition, terms such as first, second, A, B, (a), (b), etc. may be used herein to describe components. Each of these terms is not used to limit the nature, order, or sequence of the corresponding component, but is only used to distinguish the corresponding component from (one or more) other components. It should be noted that if it is described in the specification that a component is "connected," "coupled," or "engaged" to another component, although the first component may be directly connected, coupled, or engaged to the second component, a third component may also be "connected," "coupled," or "engaged" between the first and second components.

[0041] The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular is intended to include the plural. It will also be understood that the terms "comprise" and / or "include" when used herein specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0042] It should also be noted that in some optional embodiments, the functions / actions mentioned may not occur in the order mentioned in the figures. For example, two figures shown in succession may actually be performed substantially simultaneously or may sometimes be performed in the reverse order, depending on the functions / actions involved.

[0043] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure of this application belongs. Unless explicitly defined as such herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and not as an idealized or overly formal meaning.

[0044] Furthermore, in the description of the exemplary embodiments, when it is considered that a detailed description of structures or functions known therefrom after understanding the disclosure of the present application will lead to obscure interpretation of the exemplary embodiments, such description will be omitted.

[0045] Various example embodiments will now be described more fully with reference to the accompanying drawings, in which some example embodiments are shown. In the drawings, the thicknesses of layers and regions are exaggerated for clarity.

[0046] Hereinafter, examples will be described in detail with reference to the accompanying drawings, and like reference numerals in the drawings refer to like elements throughout.

[0047] Figures 1 to 3 is a diagram illustrating an example of a wearable device according to at least one example embodiment.

[0048] Reference Figure 1 , the wearable device 110 can sense or obtain the motion information of the user 120, and generate a torque based on the sensed or obtained motion information and various factors. For example, the wearable device 110 can generate an assist torque to assist the user 120 in walking. For another example, the wearable device 110 can generate a resistance torque to apply resistance to the user 120 when walking. Figure 4 The steps of generating torque by the wearable device 110 are described in more detail.

[0049] For example, the wearable device 110 may be configured as a hip type to be worn on the hip joint or thigh of the user 120, an ankle type to be worn on the ankle of the user 120, or a knee type to be worn on the knee of the user 120. However, the type of the wearable device 110 is not limited to the examples described above. Figure 2 and Figure 3 The wearable device 110 shown in FIG. 1 is of the hip type.

[0050] Reference Figure 2 and Figure 3 , the drivers 210-1 and 210-2 of the wearable device 110 are positioned around the hip joints of the user 120, and the controller 310 of the wearable device 110 is positioned around the waist of the user 120. That is, the hip-type wearable device 110 may be designed such that: the drivers 210-1 and 210-2 are positioned around the hip joints of the user 120, and the controller 310 is positioned around the waist of the user 120. However, the positions of the drivers 210-1 and 210-2 and the controller 310 are not limited to Figure 2 and Figure 3 Example locations shown in .

[0051] Figures 4 to 7 is a diagram illustrating an example of an operation of the wearable device 110 according to at least one example embodiment.

[0052] Reference Figure 4 In operation 410, the wearable device 110 uses a sensor to sense the motion of the user 120. That is, the wearable device 110 obtains the motion information of the user 120. The motion information may include, for example, the angles of the two hip joints of the user 120. Figure 5 As shown in FIG, the wearable device 110 uses the encoders positioned around the actuator 210-1 to sense or obtain the angle q1(t) of the left hip joint of the user 120, and uses the encoders positioned around the actuator 210-2 to sense or obtain the angle q1(t) of the right hip joint of the user 120. r (t). When the left leg of the user 120 is Figure 5As shown in FIG, when moving forward, the angle q1(t) of the left hip joint can be less than 0, and the angle q r (t) may be greater than 0. However, according to one example, the angle q1(t) of the left hip joint may be greater than 0, and the angle q r (t) can be less than 0.

[0053] Return to reference Figure 4 In operation 420, the wearable device 110 defines a state variable. Here, the wearable device 110 defines the state variable based on the motion information of the user 120. In a non-limiting example, the state variable may be related to the angle q1(t) and the angle q r (t) is associated with at least one of the following. For example, the wearable device 110 defines r The state variable y corresponds to the difference between sin(q1(t)) and sin(q1(t)) raw (t). However, the present invention is not limited to the above state variable y raw (t), state variable y raw (t) may include the angle q1(t) and the angle q r (t) is a state variable obtained by performing any operation on at least one of them.

[0054] In operation 430, the wearable device 110 adjusts the state variable y raw (t) is smoothed. By smoothing, the state variable y raw (t) removes noise and smoothes the state variable y raw For example, the wearable device 110 is raw (t) Perform low-pass filtering. Equation 1 shows an example of a smoothed result or a low-pass filtered result.

[0055] [Equation 1]

[0056] y(t)=(1-α)y(t prv )+αy raw (t), (0<α<1)

[0057] In Equation 1, y(t) represents the smoothing result. In addition, α represents the smoothing factor, y(t prv ) represents the previous smoothing result.

[0058] The smoothing result represented by Equation 1 is provided only as an example, and thus the smoothing result is not limited to that represented by the above Equation 1. The smoothing result may vary based on the type of smoothing method or the type of low-pass filter (LPF).

[0059] In operation 440, the wearable device 110 determines the interaction mode and motion type of the wearable device 110 based on the motion information and the gain κ. For example, the motion information may include the angle q1(t) of the left hip joint of the user 120 and the angle q1(t) of the right hip joint of the user 120. r (t). Gain κ represents a factor associated with the magnitude of the moment and can be a positive or negative number. In addition, gain κ can be received as input from user 120. For example, user 120 can input gain κ to a user interface (UI) device (e.g., a tablet personal computer (PC), a smartphone, etc.), and wearable device 110 can receive gain κ from the UI device. According to one example, user 120 can input gain κ to wearable device 110.

[0060] In one example, the wearable device 110 may determine one of a plurality of interaction modes based on the gain κ. The interaction modes may be categorized based on the type of torque or force output by the wearable device 110 to the user 120. For example, the interaction modes may include a first interaction mode that assists the user 120 in movement, a second interaction mode that applies resistance (e.g., a first resistance) to the movement of the user 120, and a third interaction mode that applies a high resistance (e.g., a second resistance) to the movement of the user 120, wherein the second resistance is higher than the first resistance.

[0061] In one example, wearable device 110 may determine one of multiple motion types for the determined interaction mode based on the motion information. The motion types may be categorized based on the movement speed of user 120. For example, the motion types of the first and second interaction modes may be different from the motion type of the third interaction mode. For example, the first and second interaction modes may include a walking motion type, a walk-to-run motion type, and a running motion type. The third interaction mode may include a high-resistance motion type and a slow motion type.

[0062] In the following we will refer to Figure 6 Determining interaction modes and motion types is described in more detail.

[0063] Reference Figure 6 , in operation 610, the wearable device 110 determines whether the gain κ is greater than or equal to a reference value r.

[0064] In operation 620, in response to determining that the gain κ is greater than or equal to the reference value r, the wearable device 110 determines whether the gain κ is a positive number.

[0065] In operation 630, in response to determining that the gain κ is a positive number, the wearable device 110 selects the first interaction mode and determines the motion type of the wearable device 110 based on the first posture feature value. The first posture feature value may include the cadence of the user 120. The cadence may be calculated based on the gait information of the user 120. In one example, the gait information of the user 120 includes the time taken when the user 120 walks a predetermined number of steps in a predetermined direction (e.g., a forward direction, a backward direction, etc.) and / or the distance walked when the user 120 walks the predetermined number of steps in the predetermined direction. For example, the gait information of the user 120 includes the time taken when the user 120 walks two steps forward and / or the distance walked when the user 120 walks two steps forward.

[0066] For example, when the reference value r is -5 (r=-5) and the gain κ is +5 (κ=+5), the wearable device 110 selects the first interaction mode. In addition, when the first step feature value is less than or equal to the first threshold value (for example, Figure 9 120 described above), the wearable device 110 determines the motion type of the wearable device 110 as the walking motion type. When the first step feature value is greater than the first threshold and less than or equal to the second threshold (for example, Figure 9 140 described above), the wearable device 110 determines the motion type of the wearable device 110 as the walking to running motion type. When the first step feature value is greater than the second threshold value, the wearable device 110 determines the motion type of the wearable device 110 as the running motion type. That is, when the cadence of the user 120 is less than the preset range, the wearable device 110 can determine that the user 120 is walking and select the walking motion type. When the cadence of the user 120 is within the preset range, the wearable device 110 can determine that the user 120 is walking relatively fast and select the walking to running motion type. When the cadence of the user 120 is greater than the preset range, the wearable device 110 can determine that the user 120 is running and select the running motion type.

[0067] In this example, the gain κ is a positive number, so the wearable device 110 can output a torque that assists the user 120 in his / her movement regardless of the motion type to be selected in the first interaction mode. Although described below, the control factor can be changed for each motion type, so the magnitude of the assist torque can be increased in an orderly sequence starting from the walking motion type, then to the walking motion type, and then to the running motion type.

[0068] In operation 640, in response to determining that the gain κ is a negative number, the wearable device 110 selects the second interaction mode and determines the motion type of the wearable device 110 based on the first stance feature value. For example, when the reference value r is -5 (r=-5) and the gain κ is -3 (κ=-3), the wearable device 110 selects the second interaction mode. As described above with respect to operation 630, the wearable device 110 determines one of the walking motion type, the walk-to-run motion type, and the running motion type based on the first stance feature value. In this example, the gain κ is a negative number, so the wearable device 110 may output a torque that applies resistance to the movement of the user 120, regardless of the motion type to be selected in the second interaction mode. Although described below, the control factor may vary for each motion type, so the magnitude of the resistance torque may increase in an ordered sequence from the walking motion type to the walk-to-run motion type and then to the running motion type.

[0069] In operation 650, in response to determining in operation 610 that the gain κ is less than the reference value r, the wearable device 110 selects the third interaction mode and determines the motion type of the wearable device 110 based on the second gait feature value. For example, when the reference value r is -5 (r=-5) and the gain κ is -7 (κ=-7), the wearable device 110 selects the third interaction mode. In addition, when the second gait feature value is greater than a third threshold value (for example, which will be referred to below), the wearable device 110 selects the third interaction mode. Figure 9 When the second gait feature value is less than the fourth threshold value (for example, the fourth threshold value will be referred to below), the wearable device 110 determines the motion type of the wearable device 110 as the high resistance motion type. Figure 9 0.4) described above, the wearable device 110 determines the motion type of the wearable device 110 as a slow motion type. That is, when the second gait feature value of the user 120 is greater than the third threshold, the wearable device 110 may select the high-resistance motion type to apply high resistance to the motion of the user 120. When the second gait feature value of the user 120 is less than the fourth threshold, the wearable device 110 may determine that the user 120 is walking relatively slowly and select the slow motion type to apply a small assist torque to the slow motion of the user 120.

[0070] The second gait feature value may represent a value from which the gait characteristics (eg, walking speed) of the user 120 during a preset period can be estimated. Figure 8 The second gait feature value is described in more detail.

[0071] Return to reference Figure 4In operation 450, the wearable device 110 determines a control factor based on the determined motion type. The control factor may include at least one of a delay Δt associated with the output timing of the torque and a smoothing factor α. Although described below, the control factor may affect the response characteristics of the torque and is therefore referred to as a response variable or a sensed response variable. Figure 7 Describe the determination of control factors in more detail.

[0072] Reference Figure 7 In operation 710, the wearable device 110 verifies whether a motion type change event occurs. For example, the wearable device 110 verifies whether the motion type determined in operation 440 is the same as the previous motion type. In this example, when the motion type determined in operation 440 is different from the previous motion type, a motion type change event may occur. Conversely, when the motion type determined in operation 440 is the same as the previous motion type, a motion type change event does not occur.

[0073] In operation 720, when the motion type change event occurs, the wearable device 110 adjusts the control factor. For example, when the previous motion type is the running motion type and the walking motion type is determined in operation 440, the motion type change event occurs, so the wearable device 110 changes the previously set control factor (for example, the control factor corresponding to the running motion type) to the control factor corresponding to the walking motion type. In one example, each motion type and the corresponding control factor can be mapped to each other in a lookup table. Figure 9 Adjusting control factors based on motion type is described in more detail.

[0074] In operation 730, when the exercise type change event does not occur, the wearable device 110 maintains the previously set control factor.

[0075] Return to reference Figure 4 In operation 460, the wearable device 110 schedules the compensation factor κ based on the determined motion type. comp Compensation factor κ comp Indicates the factor that will be used to compensate for the magnitude of the moment. In addition, the compensation factor κ comp It can be used to adjust or compensate for the linear response of torque to each type of motion.

[0076] For example, when the running exercise type is determined in operation 440, the wearable device 110 may set the compensation factor κ to comp When the walking motion type is determined in operation 440, the wearable device 110 may set the compensation factor κ to comp When the high resistance exercise type is determined in operation 440, the wearable device 110 may set the compensation factor κ tocomp When the slow motion type is determined in operation 440, the wearable device 110 may set the compensation factor κ to c o mp Determined as -5 / κ. In one example, each motion type and corresponding compensation factor may be mapped to each other in a lookup table.

[0077] In operation 470, the wearable device 110 calculates the value of the smoothed result y(t), the gain κ, the control factor, and the compensation factor κ based on the smoothed result y(t). c o mp An example of the torque τ(t) can be expressed by Equation 2.

[0078] [Equation 2]

[0079] τ(t)=κ·κ comp y(t-Δt)

[0080] For example, the wearable device 110 may generate an assist torque in each motion type of the first interaction mode. In this example, the control factor may vary based on each motion type. Therefore, even if the gain κ is the same in the walking motion type, the walk-to-run motion type, and the running motion type, the assist torque in the walking motion type may be smaller than the assist torque in the walk-to-run motion type, and the assist torque in the walk-to-run motion type may be smaller than the assist torque in the running motion type.

[0081] For another example, the wearable device 110 may generate a resistance torque for each motion type of the second interaction mode. In this example, the control factor may vary for each motion type. Thus, even if the gain κ is the same for the walking motion type, the walk-to-run motion type, and the running motion type, the resistance torque for the walking motion type may be smaller than the resistance torque for the walk-to-run motion type, and the resistance torque for the walk-to-run motion type may be smaller than the resistance torque for the running motion type.

[0082] For another example, the wearable device 110 may generate a high resistance torque in the high resistance motion type of the third interaction mode. In this example, the high resistance torque may be stronger or greater than the resistance torque in the second interaction mode. In addition, the wearable device 110 may generate a relatively small assist torque in the slow motion type of the third interaction mode to assist the user 120 in walking slowly.

[0083] Figure 8 is a diagram illustrating an example of a second gait feature value according to at least one example embodiment.

[0084] Figure 8 The angle q of the right hip joint of the user 120 is shown r Example of (t).

[0085] The second gait feature value may be a value from which the gait characteristics of the user 120 in a desired (or, optionally, preset) time period can be estimated. For example, the second gait feature value may be calculated based on each of the angular curve lengths of the two hip joints in the most recent 1 second. Figure 8 In the example of FIG, as shown by Equation 3, the wearable device 110 calculates the angle q of the right hip joint during the interval between t-1 and t r (t) Angle curve length q r_length 810.

[0086] [Equation 3]

[0087]

[0088] In Equation 3, Represents the relationship between a point (t,q) and its adjacent points (t prv ,q prv ). Based on Equation 3, the wearable device 110 calculates each of the distance between point 820-1 and point 820-2, the distance between point 820-2 and point 820-3, the distance between point 820-3 and point 820-4, the distance between point 820-4 and point 820-5, the distance between point 820-5 and point 820-6, the distance between point 820-6 and point 820-7, the distance between point 820-7 and point 820-8, the distance between point 820-8 and point 820-9, and the distance between point 820-9 and point 820-10. The wearable device 110 calculates the angle curve length q by subtracting 1 from the sum of the calculated distances. r_length 810.

[0089] Although Figure 8 Not shown, but as described above, the wearable device 110 may also calculate the angle curve length q of the angle q1(t) of the left hip joint of the user 120 during the interval between t-1 and t l_length .

[0090] In one example, the wearable device 110 may calculate the angle curve length q r_length and q l_length and determine the average value as the second gait feature value.

[0091] Figure 9 is a diagram illustrating an example of a state machine of the wearable device 110 according to at least one example embodiment.

[0092] Figure 9 An example of a state machine when the reference value r is -5 (r=-5) is shown.

[0093] For example, when the gain κ is greater than or equal to -5, the wearable device 110 selects the first interaction mode or the second interaction mode. In this example, when the gain κ is a positive number, the wearable device 110 selects the first interaction mode. Conversely, when the gain κ is a negative number, the wearable device 110 selects the second interaction mode. Furthermore, when the gain κ is less than -5, the wearable device 110 selects the third interaction mode.

[0094] <When Gain κ is Greater than or Equal to -5>

[0095] Reference Figure 9 , when the cadence of the user 120 reaches between 120 and 140 while the wearable device 110 is operating in the walking motion type 910, the wearable device 110 changes the walking motion type 910 to the walking to running motion type 920. Since the motion type has changed, the wearable device 110 adjusts the control factor. That is, since the walking to running motion type 920 is different from the walking motion type 910 which was the previous motion type, the wearable device 110 adjusts the control factor. For example, the wearable device 110 may increase the smoothing factor α and / or reduce the delay Δt. Figure 9 In the example of , the wearable device 110 increases the smoothing factor from 0.05 to a value within a range from 0.05 to 0.10, and decreases the delay from 0.25 to a value within a range from 0.20 to 0.25. Conventionally, when the cadence of the user 120 reaches between 120 and 140 while operating in the walking motion type, saturation or torque decay, in which the torque does not increase in proportion to the walking speed, may occur due to smoothing or low-pass filtering. In contrast, in one or more example embodiments, by changing to the walking to running motion type 920 when the cadence reaches between 120 and 140, smoothing may be performed using an adjusted smoothing factor, and torque may be generated using an adjusted delay, thereby reducing or minimizing saturation and compensating for the decay of the torque.

[0096] In the walk-to-run motion type 920, the smoothing factor and delay can be set values ​​within their respective ranges. However, the values ​​are not limited to the example shown. For example, in the walk-to-run motion type 920, the smoothing factor and delay can be set values ​​within their respective ranges to match the cadence of user 120. In this example, when the cadence of user 120 is 120, the smoothing factor can be 0.055 and the delay can be 0.205. When the cadence of user 120 is 130, the smoothing factor can be 0.075 and the delay can be 0.225. When the cadence of user 120 is 140, the smoothing factor can be 0.095 and the delay can be 0.245.

[0097] When the user's 120 cadence reaches 140 while the wearable device 110 is operating in the walking motion type 910, the wearable device 110 changes the walking motion type 910 to the running motion type 930. As the motion type changes, the wearable device 110 adjusts the control factor. For example, the wearable device 110 may increase the smoothing factor α and / or decrease the delay Δt. Figure 9 In the example of , the wearable device 110 increases the smoothing factor from 0.05 to 0.1 and decreases the delay from 0.25 to a value in the range from 0.15 to 0.20. In addition, the wearable device 110 increases the compensation factor κ comp Adjusted from 1 to 1.2. Conventionally, when the cadence of user 120 exceeds 140 while operating in the walking motion type, torque attenuation may occur due to smoothing or low-pass filtering. In contrast, in one or more example embodiments, by changing to the running motion type 930 when the cadence is greater than or equal to 140, smoothing can be performed using an adjusted smoothing factor, and torque can be generated using an adjusted delay and an adjusted compensation factor. Thus, the amount of torque attenuation can be compensated.

[0098] When the user's 120 cadence reaches 110 while the wearable device 110 is operating in the walk to run motion type 920, the wearable device 110 changes the walk to run motion type 920 to the walk motion type 910. Since the motion type changes, the wearable device 110 adjusts the control factor. For example, the wearable device 110 may reduce the smoothing factor and / or increase the delay. Figure 9 In the example of , the wearable device 110 decreases the smoothing factor to 0.05 and increases the delay to 0.25.

[0099] When the cadence of user 120 reaches 150 or greater while wearable device 110 is operating in the walk-to-run motion type 920, wearable device 110 changes the walk-to-run motion type 920 to the run motion type 930. Due to the change in motion type, wearable device 110 adjusts the control factors. For example, wearable device 110 may increase the smoothing factor and / or reduce the delay. In addition, wearable device 110 may increase the compensation factor. When the cadence of user 120 exceeds 140, torque decay may occur as described above. Therefore, wearable device 110 may perform smoothing using the adjusted smoothing factor and generate torque using the adjusted delay and the adjusted compensation factor, so that torque decay does not occur when changing from the walk-to-run motion type 920 to the run motion type 930.

[0100] When the user's cadence reaches 110 while the wearable device 110 is operating in the running motion type 930, the wearable device 110 changes the running motion type 930 to the walking motion type 910. Due to the change in motion type, the wearable device 110 adjusts the control factors. For example, the wearable device 110 may reduce the smoothing factor and / or increase the delay. Furthermore, the wearable device 110 may adjust the compensation factor from 1.2 to 1.

[0101] As described above, the control factor can be varied for each of motion types 910, 920, and 930. That is, with the same gain κ, the smoothing factor α can be adjusted to increase, and the delay Δt can be adjusted to decrease, in an ordered sequence from walking motion type 910, to walking, to running motion type 920, and then to running motion type 930. By adjusting the control factor as described above, the magnitude of the torque can be linearly and steadily increased as the motion speed of user 120 increases. Furthermore, the magnitude of the torque can be linearly and steadily increased as the gain linearly increases. Consequently, the linear response characteristics and control stability of wearable device 110 can be improved.

[0102] <When Gain κ is Less than -5>

[0103] While the wearable device 110 is operating under the high resistance exercise type 940, q length When q is less than 0.4, the wearable device 110 changes the high resistance exercise type 940 to the slow exercise type 950. That is, while the wearable device 110 is operating under the high resistance exercise type 940, when q length When q is less than 0.4, the wearable device 110 changes the high resistance exercise type 940 to the slow exercise type 950, and when q length When is less than 0.5 and greater than or equal to 0.4, the wearable device 110 maintains the high resistance exercise type 940. Figure 9 In the example of , although the high-resistance exercise type 940 is changed to the slow exercise type 950, the smoothing factor and the delay may not change. Similarly, also in the case where the slow exercise type 950 is changed to the high-resistance exercise type 940, the smoothing factor and the delay may not change. However, the example is not limited to the content described in the foregoing, and at least one of the smoothing factor and the delay may be configured to change in response to the change of the exercise type.

[0104] When the user's 120 exercise speed increases during high-resistance exercise type 940, the torque increases linearly and steadily. Furthermore, when the gain increases linearly, the torque also increases linearly and steadily. Therefore, during high-resistance exercise type 940, the wearable device 110's linear response characteristics and control stability can be improved.

[0105] As described above, conventionally, when the cadence of the user 120 reaches a threshold while operating in a walking motion type, saturation or torque attenuation in which the torque does not increase in proportion to the walking speed may occur due to smoothing or low-pass filtering, and when the cadence of the user 120 exceeds a second threshold that is higher than the first threshold while operating in a walking motion type, torque attenuation may occur due to smoothing or low-pass filtering.

[0106] In contrast, in one or more example embodiments, when the gain κ is greater than or equal to the reference value, the wearable device 110 may operate in a first reference mode or a second reference mode to adjust the control factors (e.g., the smoothing factor α and the delay Δt) by switching between the walking motion type 910, the walk-to-run motion type 920, and the running motion type 930 based on the cadence, such that the smoothing factor increases by α in an ordered sequence, and the delay Δt decreases in an ordered sequence as the cadence increases from the walking motion type 910 to the walk-to-run motion type 920 and then to the running motion type 930. Thus, in the first interaction mode (i.e., the assistance mode when the gain κ is positive), the magnitude of the assisting torque may increase linearly and steadily as the cadence of the user 120 increases during movement between motion types 910 and 930, and in the second interaction mode (i.e., the resistance mode when the gain κ is negative), the magnitude of the resistance torque may increase linearly and steadily as the cadence of the user 120 increases during movement between motion types 910 and 930, even if the gain κ remains the same across multiple motion types. In addition, when the gain κ is less than the reference value, the wearable device 110 can operate in the third reference mode to maintain a constant control factor (e.g., smoothing factor α and delay Δt) in both the high resistance exercise type 940 and the slow exercise type 950, so that the smoothing factor α is relatively high to generate a stronger resistance torque or a relatively smaller assist torque, and the delay Δt is relatively short compared to the second interaction mode. Therefore, the linear response characteristics and control stability of the wearable device 110 can be improved.

[0107] Figure 10 is a flowchart illustrating an example of an operating method of the wearable device 110 according to at least one example embodiment.

[0108] Reference Figure 10 In operation 1010, the wearable device 110 processes a state variable defined based on the motion information of the user 120. For example, the wearable device 110 may process the state variable y raw (t) Smoothing.

[0109] In operation 1020, the wearable device 110 determines an interaction mode of the wearable device 110 based on a gain associated with the magnitude of the torque, wherein the gain may be input by the user. In some other example embodiments, rather than the user inputting the gain and the controller 310 determining the interaction mode based on the input gain, the user may input the interaction mode and the controller 310 may determine a default gain associated with the input interaction mode.

[0110] In operation 1030, the wearable device 110 selects a motion type from the determined motion types of the interaction mode based on the gait parameters of the user 120. The gait parameters may include at least one gait feature value representing gait or walking characteristics. For example, the gait parameters may include at least one of the first gait feature value and the second gait feature value described above.

[0111] In operation 1040, the wearable device 110 determines a control factor for the torque based on the selected motion type. For example, the wearable device 110 may search a lookup table for a control factor corresponding to the selected motion type. In another example, the wearable device 110 may determine the control factor corresponding to the selected motion type using a regression function or regression analysis. In one example, the control factor for each motion type may be optimized through training.

[0112] In operation 1050, the wearable device 110 generates a torque based on the processed state variable, the gain, and the determined control factor. The processed state variable may correspond to y(t) described above. The wearable device 110 can generate a torque by applying different control factors to each motion type. This can improve the linear response characteristics and control stability of the wearable device 110.

[0113] For the above reference Figure 10 For a detailed description of the operation method described, please refer to the Figures 1 to 9 The content of the description.

[0114] Figure 11 is a diagram illustrating an example of a wearable device 110 according to at least one example embodiment.

[0115] Reference Figure 11 , the wearable device 110 includes a controller 310 and a driver 1110 .

[0116] In addition, the wearable device 110 may include a user interface (UI) device and one or more sensors. The UI device may be configured to receive input of a gain associated with the magnitude of the torque from the user. The UI device may include various appropriate devices configured to set the exercise mode (e.g., switches, knobs, and jog dials). The UI device may be replaced with an external remote control or smart device and may not need to be included in the wearable device 110. The sensor may be an angle sensor configured to measure the angle of the joint (e.g., a potentiometer, an absolute encoder, or an incremental encoder).

[0117] The controller 310 may be implemented as a processing circuit (such as hardware including a logic circuit), a hardware / software combination (such as a processor that executes software), or a combination thereof and a memory. For example, the processing circuit may more specifically include but is not limited to: a central processing unit (CPU), an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit, a microprocessor, an application specific integrated circuit (ASIC), etc. The processing circuit may be the execution of the above-mentioned Figures 1 to 10 The controller 310 may process state variables defined based on the motion information of the user 120 and determine an interaction mode for the wearable device 110 based on a gain associated with a torque, where the gain may be input by the user 120 via a user interface (UI). In some other exemplary embodiments, rather than the user inputting a gain and the controller 310 determining the interaction mode based on the input gain, the user may input an interaction mode and the controller 310 may determine a default gain associated with the input interaction mode. Furthermore, the controller 310 may select a motion type from the determined interaction mode based on the gait parameters of the user 120 and determine a torque control factor based on the selected motion type. The controller 310 may then control the driver 1110 based on the processed state variables, the gain, and the determined control factor. Thus, the processing circuitry may improve the functionality of the wearable device 110 by linearly and steadily increasing the torque as the speed of the user 120's motion increases, thereby improving the linear response characteristics and control stability of the wearable device 110.

[0118] The driver 1110 may generate torque under the control of the controller 310 .

[0119] The wearable device 110 may include a single driver (e.g., Figure 11 ), or include multiple drivers (e.g., Figure 2 and Figure 3 2 and 210 - 2 shown in FIG.

[0120] For the above reference Figure 11 For a detailed description of the wearable device 110, please refer to the above referenced Figures 1 to 10 The content of the description.

[0121] The units and / or modules described herein can be implemented using hardware components and software components. For example, the hardware components may include a microphone, an amplifier, a bandpass filter, an audio digital converter, and a processing device. The processing device can be implemented using one or more hardware devices configured to implement and / or execute program code by performing arithmetic operations, logical operations, and input / output operations. The one or more processing devices may include a processor, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a field programmable array, a programmable logic unit, a microprocessor, or any other device capable of responding to and executing instructions in a defined manner. The processing device can run an operating system (OS) and one or more software applications running on the OS. The processing device can also access, store, manipulate, process, and create data in response to the execution of the software. For simplicity, the description of the processing device is used as a singular; however, those skilled in the art will understand that the processing device may include multiple processing elements and multiple types of processing elements. For example, the processing device may include multiple processors or a processor and a controller. In addition, different processing configurations are possible (such as parallel processors).

[0122] Software may include computer programs, code segments, instructions, or some combination thereof to independently or collectively instruct and / or configure a processing device to operate as desired, thereby turning the processing device into a special-purpose processor. Software and data may be embodied permanently or temporarily in any type of machine, component, physical or virtual device, computer storage medium or device, or in a propagating signal wave capable of providing instructions or data to or being interpreted by a processing device. Software may also be distributed on networked computer systems so that the software is stored and executed in a distributed manner. Software and data may be stored by one or more non-transitory computer-readable recording media.

[0123] The method according to the above-mentioned example embodiment can be recorded in a non-transitory computer-readable medium, which includes program instructions for implementing the various operations of the above-mentioned example embodiment. The medium may also include data files, data structures, etc., alone or in combination with the program instructions. The program instructions recorded on the medium may be program instructions specially designed and constructed for the purpose of the example embodiment, or they may be of a type that is well known and available to those skilled in the art of computer software. Examples of non-transitory computer-readable media include magnetic media (such as hard disks, floppy disks, and tapes), optical media (such as CD-ROM disks, DVDs, and / or Blu-ray discs), magneto-optical media (such as optical discs), and hardware devices specially configured to store and execute program instructions (such as read-only memory (ROM), random access memory (RAM), flash memory (for example, USB flash drives, memory cards, memory sticks, etc.). Examples of program instructions include both machine code (such as machine code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The above-mentioned devices can be configured to act as one or more software modules to perform the operations of the above-mentioned example embodiments, or vice versa.

[0124] A number of example embodiments have been described above. However, it should be understood that various modifications may be made to these example embodiments. For example, suitable results may be achieved if the described techniques are performed in a different order and / or if components in the described systems, architectures, devices, or circuits are combined in a different manner and / or replaced or supplemented with other components or their equivalents. Therefore, other implementations are within the scope of the following claims.

Claims

1. A method for operating a wearable device, the method comprising: Get the user's hip angle; determining a first motion type of the user based on the user's gait information and outputting a first torque to the user based on an angle of the hip joint and a first control factor value obtained in the first motion type; as well as When the user's movement speed changes, a second motion type different from the first motion type is determined and a second torque is output to the user based on the angle of the hip joint obtained in the second motion type and the second control factor value. The gait information is based on the time or distance the user walks. wherein a first control factor value for a first torque under a first motion type is different from a second control factor value for a second torque under a second motion type, The first control factor value includes a first delay value associated with the output timing of the first torque, and the second control factor value includes a second delay value associated with the output timing of the second torque.

2. The operating method according to claim 1, further comprising: The interaction mode of the wearable device is determined based on the gain associated with the torque magnitude of the wearable device to be a first interaction mode that assists the user in performing the user's movement or a second interaction mode that applies resistance to the user's movement.

3. The operating method according to claim 2, wherein: The steps to determine the interaction mode include: In response to the gain being a positive number greater than or equal to the reference value, determining the interaction mode as the first interaction mode; and In response to the gain being a negative number greater than or equal to the reference value, the interaction mode is determined to be the second interaction mode.

4. The operating method according to claim 1, wherein: The first torque is output after being delayed by a first delay value associated with an output timing of the first torque, The second torque is output after being delayed by a second delay value associated with an output timing of the second torque, and The first delay value and the second delay value are different from each other.

5. The operating method according to claim 4, wherein: When the second rhythm of the user in the second motion type is greater than the first rhythm of the user in the first motion type, the second delay value is less than the first delay value.

6. The operating method according to claim 1, wherein: The first torque is output after the angle of the hip joint obtained under the first motion type is filtered. The second torque is output after filtering the angle of the hip joint obtained under the second motion type, and The first smoothing factor value used to filter the angle of the hip joint obtained in the first motion type is different from the second smoothing factor value used to filter the angle of the hip joint obtained in the second motion type.

7. The operating method according to claim 6, wherein: When the second rhythm of the user in the second motion type is greater than the first rhythm of the user in the first motion type, the second smoothing factor value is greater than the first smoothing factor value.

8. The operating method according to claim 1, wherein: The first control factor value includes a first smoothing factor value for filtering the angle of the hip joint obtained under the first motion type, and The second control factor value includes a second smoothing factor value for filtering the angle of the hip joint obtained under the second motion type.

9. A wearable device comprising: sensor; Driver; as well as A controller configured to control the driver, The controller is configured as follows: Using sensors to obtain the angle of the user's hip joint; determining a first motion type of the user based on the user's gait information and controlling the driver to output a first torque based on the angle of the hip joint obtained in the first motion type and a first control factor value; and When the user's movement speed changes, a second motion type different from the first motion type is determined, and the driver is controlled to output a second torque based on the angle of the hip joint obtained in the second motion type and the second control factor value. The gait information is based on the time or distance the user walks. wherein a first control factor value for a first torque under a first motion type is different from a second control factor value for a second torque under a second motion type, The first control factor value includes a first delay value associated with the output timing of the first torque, and the second control factor value includes a second delay value associated with the output timing of the second torque.

10. The wearable device according to claim 9, wherein: The controller is configured to determine an interaction mode of the wearable device as a first interaction mode that assists the user in moving the user or a second interaction mode that applies resistance to the user's movement based on a gain associated with a torque magnitude of the wearable device.

11. The wearable device according to claim 10, wherein: The controller is configured as: In response to the gain being a positive number greater than or equal to the reference value, determining the interaction mode as the first interaction mode; and In response to the gain being a negative number greater than or equal to the reference value, the interaction mode is determined to be the second interaction mode.

12. The wearable device according to claim 9, wherein: The first torque is output after being delayed by a first delay value associated with an output timing of the first torque, The second torque is output after being delayed by a second delay value associated with an output timing of the second torque, and The first delay value and the second delay value are different from each other.

13. The wearable device according to claim 12, wherein: When the second rhythm of the user in the second motion type is greater than the first rhythm of the user in the first motion type, the second delay value is less than the first delay value.

14. The wearable device according to claim 9, wherein: The first torque is output after the angle of the hip joint obtained under the first motion type is filtered. The second torque is output after filtering the angle of the hip joint obtained under the second motion type, and The first smoothing factor value used to filter the angle of the hip joint obtained in the first motion type is different from the second smoothing factor value used to filter the angle of the hip joint obtained in the second motion type.

15. The wearable device according to claim 14, wherein: When the second rhythm of the user in the second motion type is greater than the first rhythm of the user in the first motion type, the second smoothing factor value is greater than the first smoothing factor value.

16. The wearable device according to claim 9, wherein: The first control factor value includes a first smoothing factor value for filtering the angle of the hip joint obtained under the first motion type, and The second control factor value includes a second smoothing factor value for filtering the angle of the hip joint obtained under the second motion type.

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