Wearable device and operating method of wearable device

Abstract

A wearable device and an operation method of the wearable device are disclosed. The wearable device may process a state variable 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; selecting a motion type from a plurality of motion types of the determined interaction mode based on gait parameters 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 variable, the gain and the determined control factor.

Description

[0001] This application is a divisional application of the patent application filed on June 24, 2020, with application number 202010591327.6 and titled "Wearable Device and Method of Operating Wearable Device".

[0002] This application claims priority to Korean Patent Application No. 10-2019-0117551, filed on September 24, 2019, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] At least one example embodiment relates to a wearable device. Background Technology

[0004] Recent societal aging has led to a growing number of people experiencing inconvenience and pain due to weakened muscle strength or joint problems caused by aging. Therefore, there is increasing interest in walking aids that enable older users or patients with weakened muscle strength or joint problems to walk with less effort. Furthermore, exercise aids that can help increase muscle strength are under development. Summary of the Invention

[0005] Some example embodiments relate to a method of operating a wearable device.

[0006] In some example embodiments, the operation method may include: processing state variables defined based on user motion information, determining the interaction mode of the wearable device based on a gain associated with the magnitude of the torque of the wearable device, selecting a motion type from multiple motion types of the determined interaction mode based on the user's gait parameters, determining a control factor for the torque based on the selected motion type, and generating the torque based on the processed state variables, the gain, and the determined control factor.

[0007] The steps for processing state variables may include smoothing the state variables.

[0008] In response to a gain greater than or equal to a reference value and being positive, the step of determining the interaction mode may include: selecting a first interaction mode that assists the user in moving the user. In response to a gain greater than or equal to a reference value and being negative, the step of determining the interaction mode may include: selecting a second interaction mode that applies resistance to the user's movement. In response to a gain less than a reference value, the step of determining the interaction mode may include: selecting a third interaction mode that applies high resistance to the user's movement.

[0009] In response to a first gait feature value being less than or equal to a first threshold, the step of selecting a movement type may include: determining the movement type of the wearable device as a walking movement type. In response to a first gait feature value being greater than the first threshold and less than or equal to a second threshold, the step of selecting a movement type may include: determining the movement type as a walking-to-running movement type. In response to a first gait feature value being greater than the second threshold, the step of selecting a movement type may include: determining the movement type as a running movement type.

[0010] The first step state feature value can include the user's rhythm.

[0011] In response to a second gait characteristic value in the gait parameters being greater than a third threshold, the step of selecting the movement type may include: determining the movement type of the wearable device as a high-resistance movement type. In response to a second gait characteristic value being less than a fourth threshold, the step of selecting the movement type may include: determining the movement type as a slow movement type.

[0012] The second gait characteristic value may include the average length of the angle curves of the user's two hip joints during a preset time period.

[0013] In response to a motion type change event that occurs 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 delay in the output timing of a smoothing factor or a torque used to smooth signals obtained by sensing the user's movement.

[0014] In response to a movement type change event that occurs by selecting a walking movement type from the plurality of movement types, the adjustment steps may include: reducing the smoothing factor and increasing the delay.

[0015] In response to a movement type change event that occurs by selecting running from the plurality of movement types, the adjustment steps may include increasing the smoothing factor and decreasing the delay.

[0016] The steps for generating torque may include: applying a gain, a determined control factor, and a compensation factor to the state variables being processed; and generating torque based on the results of the application.

[0017] Motion information can include the angles of the user's two hip joints.

[0018] Some example embodiments relate to a wearable device.

[0019] In some example embodiments, the wearable device may include: a controller configured to: process state variables defined based on user motion information; determine an interaction mode of the wearable device based on a gain associated with the magnitude of the torque of the wearable device; select a motion type from multiple motion types of the determined interaction mode based on the user's gait parameters; determine a control factor for 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 torque under the control of the controller.

[0020] The controller can be configured to smooth state variables.

[0021] In response to a gain greater than or equal to a reference value and being positive, the controller can be configured to: select a first interaction mode that assists the user in moving the user. In response to a gain greater than or equal to a reference value and being negative, the controller can be configured to: select a second interaction mode that applies resistance to the user's movement. In response to a gain less than a reference value, the controller can be configured to: select a third interaction mode that applies high resistance to the user's movement.

[0022] In response to a first gait feature value being less than or equal to a first threshold, the controller can be configured to determine the movement type of the wearable device as walking. In response to a first gait feature value being greater than the first threshold and less than or equal to a second threshold, the controller can be configured to determine the movement type as a transition from walking to running. In response to a first gait feature value being greater than the second threshold, the controller can be configured to determine the movement type as running.

[0023] The first step state feature value can include the user's rhythm.

[0024] In response to a second gait characteristic value in the gait parameters being greater than a third threshold, the controller can be configured to determine the movement type of the wearable device as a high-resistance movement type. In response to a second gait characteristic value being less than a fourth threshold, the controller can be configured to determine the movement type as a slow movement type.

[0025] The second gait characteristic value may include the average length of the angle curves of the user's two hip joints during a preset time period.

[0026] In response to a motion type change event that occurs by selecting a motion type from the plurality of motion types, the controller may be configured to adjust at least one of a delay in the output timing of a smoothing factor or a torque used to smooth signals obtained by sensing the user's movement.

[0027] In response to a motion type change event that occurs by selecting a walking motion type from the plurality of motion types, the controller may be configured to decrease the smoothing factor and increase the delay.

[0028] In response to a motion type change event that occurs by selecting running motion type from the plurality of motion types, the controller can be configured to increase the smoothing factor and decrease the latency.

[0029] The controller can be configured to apply a gain, a defined control factor, and a compensation factor to the state variables being processed.

[0030] Motion information can include the angles of the user's two hip joints.

[0031] Additional aspects of the exemplary embodiments will be set forth in part in the description which follows, and in part will be obvious from the description or may be learned by practice of this disclosure. Attached Figure Description

[0032] These and / or other aspects will become clearer and more readily understood from the following description of exemplary embodiments, taken in conjunction with the accompanying drawings, in which:

[0033] Figures 1 to 3 This is a diagram illustrating an example of a wearable device according to at least one exemplary embodiment;

[0034] Figures 4 to 7 This is a diagram illustrating an example of the operation of a wearable device according to at least one example embodiment;

[0035] Figure 8 This is a diagram illustrating an example of a second gait characteristic value according to at least one example embodiment;

[0036] Figure 9 This is a diagram illustrating an example of a state machine for a wearable device according to at least one example embodiment;

[0037] Figure 10 This is a flowchart illustrating an example of an operation method of a wearable device according to at least one example embodiment; and

[0038] Figure 11 This is a diagram illustrating an example of a wearable device according to at least one example embodiment. Detailed Implementation

[0039] In the following, some exemplary embodiments will be described in detail with reference to the accompanying drawings. Regarding the reference numerals assigned to elements in the drawings, it should be noted that even if the same element is shown in different drawings, the same element will be represented by the same reference numerals as much as possible. Furthermore, in the description of the embodiments, detailed descriptions of well-known related structures or functions will be omitted where such detailed descriptions would lead to a vague interpretation of this disclosure.

[0040] However, it should be understood that there is no intention to limit this disclosure to the specific exemplary embodiments disclosed. Rather, the exemplary embodiments will cover all modifications, equivalents, and alternatives falling within the scope of the exemplary embodiments. Throughout the description of the drawings, the same reference numerals denote the same elements.

[0041] Furthermore, terms such as first, second, A, B, (a), (b), etc., may be used herein to describe components. Each of these terms is not intended to define the nature, order, or sequence of the corresponding component, but only to distinguish the corresponding component from (one or more) other components. It should be noted that if a component is described in the specification as being “connected,” “joined,” or “engaged” to another component, then although the first component may be directly connected, joined, or engaged to the second component, a third component may also be “connected,” “joined,” or “engaged” between the first and second components.

[0042] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. It will also be understood that the terms “comprising” and / or “including” as used herein indicate the presence of the stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0043] It should also be noted that in some alternative implementations, the functions / actions mentioned may not occur in the order shown in the figures. For example, depending on the functions / actions involved, two figures shown consecutively may actually be performed substantially simultaneously or sometimes in reverse order.

[0044] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Unless expressly defined herein, terms (such as those defined in general dictionaries) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and shall not be interpreted as having an idealized or overly formal meaning.

[0045] Furthermore, in the description of the exemplary embodiments, such descriptions will be omitted when it is believed that a detailed description of the structure or function known therefrom after understanding the disclosure of this application would lead to a vague interpretation of the exemplary embodiments.

[0046] Various exemplary embodiments will now be described more fully with reference to the accompanying drawings, which illustrate some exemplary embodiments. In the drawings, the thickness of layers and regions is exaggerated for clarity.

[0047] In the following description, examples will be described in detail with reference to the accompanying drawings, and the same reference numerals in the drawings always denote the same elements.

[0048] Figures 1 to 3 This is a diagram illustrating an example of a wearable device according to at least one example embodiment.

[0049] Reference Figure 1 The wearable device 110 can sense or obtain motion information from the user 120 and generate 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. As another example, the wearable device 110 can generate a drag torque to apply resistance to the user 120 while walking. Reference will be made below. Figure 4 The steps by which the wearable device 110 generates torque are described in more detail.

[0050] For example, wearable device 110 may be configured to be a hip type worn on the hip or thigh of user 120, an ankle type worn on the ankle of user 120, or a knee type worn on the knee of user 120. However, the type of wearable device 110 is not limited to the examples described above. Figure 2 and Figure 3 The wearable device 110 shown is of the hip type.

[0051] Reference Figure 2 and Figure 3 The actuators 210-1 and 210-2 of the wearable device 110 are positioned around the hip joint 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 can be designed such that the actuators 210-1 and 210-2 are positioned around the hip joint of the user 120, and the controller 310 is positioned around the waist of the user 120. However, the positions of the actuators 210-1 and 210-2 and the controller 310 are not limited to... Figure 2 and Figure 3 The example location is shown.

[0052] Figures 4 to 7 This is a diagram illustrating an example of the operation of a wearable device 110 according to at least one example embodiment.

[0053] Reference Figure 4 In operation 410, the wearable device 110 uses sensors to sense the movement of the user 120. That is, the wearable device 110 obtains movement information of the user 120. This movement information may include, for example, the angles of the user 120's two hip joints. Figure 5 As shown, wearable device 110 uses encoders positioned around driver 210-1 to sense or obtain the angle q1(t) of user 120's left hip joint, and uses encoders positioned around driver 210-2 to sense or obtain the angle q of user 120's right hip joint. r (t). When user 120's left leg is like Figure 5 As shown, when moving forward, the angle q1(t) of the left hip joint can be less than 0, and the angle q of the right hip joint... r (t) can be greater than 0. However, according to one example, the angle q1(t) of the left hip joint can be greater than 0, and the angle q of the right hip joint can be greater than 0. r (t) can be less than 0.

[0054] Return to reference Figure 4 In operation 420, wearable device 110 defines state variables. Here, wearable device 110 defines state variables based on motion information from user 120. In a non-limiting example, the state variables may be related to angle q1(t) and angle q r At least one of (t) is associated with it. For example, wearable device 110 is defined with sin(q) r The state variable y corresponds to the difference between (t) and sin(q1(t)). raw (t). However, the present invention is not limited to the above-mentioned state variable y. raw (t), state variable y raw (t) can include by adjusting angle q1(t) and angle q r The state variable obtained by performing any operation on at least one of (t).

[0055] In operation 430, the wearable device 110 sets the state variable y. raw Smoothing (t) is performed. Through smoothing, the state variable y can be obtained... raw (t) Removes noise and smooths the state variable y raw The waveform of (t). For example, wearable device 110 for state variable y raw (t) Perform low-pass filtering. Equation 1 shows an example of the result of smoothing or low-pass filtering.

[0056] [Equation 1]

[0057] y(t)=(1-α)y(t prv)+αy raw (t), (0 < α < 1)

[0058] In Equation 1, y(t) represents the smoothing result. Furthermore, α represents the smoothing factor, y(t) prv ) indicates the previous smoothing result.

[0059] The smoothing result represented by Equation 1 is provided as an example only, and therefore the smoothing result is not limited to what is represented by Equation 1 above. The smoothing result may vary depending on the type of smoothing method or the type of low-pass filter (LPF).

[0060] In operation 440, wearable device 110 determines its interaction mode and movement type based on motion information and gain κ. For example, motion information may include the angle q1(t) of user 120's left hip joint and the angle q of user 120's right hip joint. r (t). Gain κ represents a factor associated with the magnitude of the torque and can be positive or negative. Furthermore, 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 PC, 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.

[0061] In one example, wearable device 110 may determine one of a plurality of interaction modes based on gain κ. Interaction modes may be categorized based on the type of torque or force output by wearable device 110 to user 120. For example, interaction modes may include: a first interaction mode that assists user 120 in movement; a second interaction mode that applies resistance (e.g., a first resistance) to user 120's movement; and a third interaction mode that applies high resistance (e.g., a second resistance) to user 120's movement, wherein the second resistance is higher than the first resistance.

[0062] In one example, wearable device 110 may determine one of multiple movement types among the determined interaction modes based on motion information. Movement types may be categorized based on the user 120's movement speed. For example, the movement types of the first and second interaction modes may differ from those of the third interaction mode. For instance, the first and second interaction modes may include walking, walking-to-running, and running. The third interaction mode may include high-resistance and slow-motion movements.

[0063] The following will refer to Figure 6 Describe the interaction patterns and motion types in more detail.

[0064] Reference Figure 6 In operation 610, the wearable device 110 determines whether the gain κ is greater than or equal to the reference value r.

[0065] 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 positive.

[0066] In operation 630, in response to determining that the gain κ is positive, wearable device 110 selects a first interaction mode and determines the movement type of wearable device 110 based on a first gait feature value. The first gait feature value may include the cadence of user 120. The cadence may be calculated based on the gait information of user 120. In one example, the gait information of user 120 includes the time used when user 120 walks a predetermined number of steps in a predetermined direction (e.g., forward, backward, etc.) and / or the distance walked when user 120 walks a predetermined number of steps in a predetermined direction. For example, the gait information of user 120 includes the time used when user 120 walks two steps forward and / or the distance walked when user 120 walks two steps forward.

[0067] For example, when the reference value r is -5 (r = -5) and the gain κ is +5 (κ = +5), the wearable device 110 selects a first interaction mode. Furthermore, when the first step state characteristic value is less than or equal to a first threshold (e.g., referred to below...), the wearable device 110 selects a first interaction mode. Figure 9 When described in 120), the wearable device 110 determines the movement type of the wearable device 110 as walking. When the first step state characteristic value is greater than a first threshold and less than or equal to a second threshold (e.g., referred to below), the wearable device 110 determines the movement type of the wearable device 110 as walking. Figure 9 When described in step 140), the wearable device 110 determines the movement type of the wearable device 110 as a walking-to-running exercise. When the first step temporal feature value is greater than the second threshold, the wearable device 110 determines the movement type of the wearable device 110 as a running exercise. That is, when the user 120's rhythm is less than a preset range, the wearable device 110 can determine that the user 120 is walking and select the walking exercise type. When the user 120's rhythm 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 exercise type. When the user 120's rhythm is greater than the preset range, the wearable device 110 can determine that the user 120 is running and select the running exercise type.

[0068] In this example, the gain κ is positive, so the wearable device 110 can output a torque to assist the user 120 in his / her movement, regardless of the type of movement selected in the first interaction mode. Although described below, the control factor can vary for each type of movement, so the magnitude of the assist torque can increase in an ordered sequence from walking to running and then to running.

[0069] In operation 640, in response to determining that the gain κ is negative, wearable device 110 selects a second interaction mode and determines the motion type of wearable device 110 based on the first-step state characteristic value. For example, when the reference value r is -5 (r = -5) and the gain κ is -3 (κ = -3), wearable device 110 selects the second interaction mode. As described above with respect to operation 630, wearable device 110 determines one of the following motion types based on the first-step state characteristic value: walking motion type, walking-to-running motion type, and running motion type. In this example, the gain κ is negative, so wearable device 110 can output a torque that applies resistance to the movement of user 120, regardless of the motion type to be selected in the second interaction mode. Although described below, the control factor can vary for each motion type, so the magnitude of the resistance torque can increase in an ordered sequence from walking motion type to walking-to-running motion type and then to running motion type.

[0070] 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 a third interaction mode and determines the movement type of the wearable device 110 based on the second gait characteristic 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. Furthermore, when the second gait characteristic value is greater than a third threshold (e.g., referred to below), the third interaction mode is selected. Figure 9 When the second gait characteristic value is less than 0.5 (as described below), the wearable device 110 determines the movement type of the wearable device 110 as a high-resistance movement type. Figure 9 When the second gait characteristic value of user 120 is greater than the third threshold (0.4), wearable device 110 determines the movement type of wearable device 110 as a slow movement type. That is, when the second gait characteristic value of user 120 is greater than the third threshold, wearable device 110 can select a high-resistance movement type to apply high resistance to the movement of user 120. When the second gait characteristic value of user 120 is less than the fourth threshold, wearable device 110 can determine that user 120 is walking relatively slowly and select a slow movement type to apply a small amount of auxiliary torque to this slow movement of user 120.

[0071] The second gait feature value can represent a value from which the gait characteristics (e.g., walking speed) of user 120 can be estimated during a preset time period. (Refer to below) Figure 8 The second gait eigenvalues ​​are described in more detail.

[0072] Return to reference Figure 4 In operation 450, the wearable device 110 determines a control factor based on a determined type of motion. 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 can influence the response characteristics of the torque and is therefore referred to as a response variable or sensed response variable. Reference will be made below. Figure 7 The determination of the control factors is described in more detail.

[0073] Reference Figure 7 In operation 710, the wearable device 110 verifies whether a motion type change event has occurred. 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, the motion type change event may occur when the motion type determined in operation 440 is different from the previous motion type. Conversely, the motion type change event will not occur when the motion type determined in operation 440 is the same as the previous motion type.

[0074] In operation 720, when a motion type change event occurs, wearable device 110 adjusts the control factor. For example, when the previous motion type was running and a walking motion type was determined in operation 440, the motion type change event occurs, so wearable device 110 changes the previously set control factor (e.g., 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 its corresponding control factor can be mapped to each other in a lookup table. Reference will be made below. Figure 9 A more detailed description of the control factor based on movement type.

[0075] In operation 730, when no motion type change event occurs, the wearable device 110 maintains the previously set control factor.

[0076] Return to reference Figure 4 In operation 460, the wearable device 110 schedules the compensation factor κ based on a determined type of motion. comp Compensation factor κ comp This represents the factor that will be used to compensate for the torque. Furthermore, the compensation factor κ... comp It can be used to adjust or compensate for the linear response to torque generated for each type of motion.

[0077] For example, when the type of running exercise is determined in operation 440, the wearable device 110 can use the compensation factor κ. comp The value is set to 1.2. When the walking motion type is determined in operation 440, the wearable device 110 can compensate for the factor κ. comp The value is set to 1. When a high-resistance motion type is determined in operation 440, the wearable device 110 can adjust the compensation factor κ to 1. comp The value is set to 0.8. When the slow motion type is determined in operation 440, the wearable device 110 can compensate for the factor κ. comp The value is determined to be -5 / κ. In one example, each motion type and its corresponding compensation factor can be mapped to each other in a lookup table.

[0078] In operation 470, the wearable device 110 is based on the smoothing result y(t), gain κ, control factor, and compensation factor κ. comp This generates torque. An example of torque τ(t) can be represented by Equation 2.

[0079] [Equation 2]

[0080] τ(t)=κ·κ comp y(t-Δt)

[0081] For example, wearable device 110 can generate an auxiliary torque in each movement type of the first interaction mode. In this example, the control factor can vary based on each movement type, so even if the gain κ is the same in the walking movement type, the walking-to-running movement type, and the running movement type, the auxiliary torque in the walking movement type can be less than the auxiliary torque in the walking-to-running movement type, and the auxiliary torque in the walking-to-running movement type can be less than the auxiliary torque in the running movement type.

[0082] For example, wearable device 110 can generate drag torque in each movement type of the second interaction mode. In this example, the control factor can vary based on each movement type, so even if the gain κ is the same in the walking movement type, the walking-to-running movement type, and the running movement type, the drag torque in the walking movement type can be less than the drag torque in the walking-to-running movement type, and the drag torque in the walking-to-running movement type can be less than the drag torque in the running movement type.

[0083] For example, wearable device 110 can generate a high-resistance torque in a high-resistance motion type of the third interaction mode. In this example, the high-resistance torque can be stronger or greater than the resistance torque in the second interaction mode. Furthermore, wearable device 110 can generate a relatively small assist torque in a slow motion type of the third interaction mode to assist user 120 in walking slowly.

[0084] Figure 8This is a diagram illustrating an example of a second gait characteristic value according to at least one example embodiment.

[0085] Figure 8 The angle q of user 120's right hip joint is shown. r Example of (t).

[0086] The second gait characteristic value can be a value from which the gait characteristics of user 120 can be estimated over a desired (or, optionally, preset) time period. For example, the second gait characteristic value can be calculated based on each of the angular curve lengths of the two hip joints in the most recent second. Figure 8 In the example, 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 The length of the angle curve q of (t) r_length 810.

[0087] [Equation 3]

[0088]

[0089] In equation 3, Represents the relationship between point (t, q) and its neighboring point (t). prv ,q prv The distances between points 820-1 and 820-2, 820-2 and 820-3, 820-3 and 820-4, 820-4 and 820-5, 820-5 and 820-6, 820-6 and 820-7, 820-7 and 820-8, 820-8 and 820-9, and 820-9 and 820-10 are calculated based on Equation 3. The wearable device 110 calculates the length q of the angular curve by subtracting 1 from the sum of the calculated distances. r_length 810.

[0090] Although Figure 8 Not shown, but as described above, the wearable device 110 can also calculate the angle curve length q of the user 120's left hip joint angle q1(t) during the interval between t-1 and t. l_length .

[0091] In one example, wearable device 110 can calculate the length q of the angular curve. r_length and q l_length The average value is then determined as the second gait characteristic value.

[0092] Figure 9 This is a diagram illustrating an example of the state machine of a wearable device 110 according to at least one example embodiment.

[0093] Figure 9 An example of a state machine is shown when the reference value r is -5 (r = -5).

[0094] For example, when the gain κ is greater than or equal to -5, the wearable device 110 selects either a first interaction mode or a second interaction mode. In this example, when the gain κ is positive, the wearable device 110 selects the first interaction mode. Conversely, when the gain κ is negative, the wearable device 110 selects the second interaction mode. Furthermore, when the gain κ is less than -5, the wearable device 110 selects a third interaction mode.

[0095] <When the gain κ is greater than or equal to -5>

[0096] Reference Figure 9 When the wearable device 110 is operating in walking exercise type 910, and the user 120's pace reaches between 120 and 140, the wearable device 110 changes the walking exercise type 910 to walking-to-running exercise type 920. Due to the change in exercise type, the wearable device 110 adjusts the control factor. That is, because the walking-to-running exercise type 920 is different from the previous walking exercise type 910, 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, wearable device 110 increases the smoothing factor from 0.05 to a value in the range of 0.05 to 0.10, and decreases the delay from 0.25 to a value in the range of 0.20 to 0.25. Conventionally, when operating in a walking motion type, if the user 120's pace reaches between 120 and 140, saturation or torque decay may occur due to smoothing or low-pass filtering, where the torque does not increase proportionally to the walking speed. In contrast, in one or more example embodiments, by changing to a walking-to-running motion type 920 when the pace reaches between 120 and 140, smoothing can be performed using an adjusted smoothing factor, and torque can be generated using an adjusted delay, thus reducing or minimizing saturation and compensating for the amount of torque decay.

[0097] In the walking-to-running exercise type 920, the smoothing factor and delay can be set values ​​within their respective ranges. However, the values ​​are not limited to the examples shown. For instance, in the walking-to-running exercise type 920, the smoothing factor and delay can be values ​​within their respective ranges that match the user's pace 120. In this example, when the user's pace 120 is 120, the smoothing factor can be 0.055 and the delay can be 0.205. When the user's pace 120 is 130, the smoothing factor can be 0.075 and the delay can be 0.225. When the user's pace 120 is 140, the smoothing factor can be 0.095 and the delay can be 0.245.

[0098] While the wearable device 110 is operating in walking mode 910, if the user 120's pace reaches 140, the wearable device 110 changes the walking mode 910 to running mode 930. Because of the change in mode, the wearable device 110 adjusts control factors. For example, the wearable device 110 may increase the smoothing factor α and / or decrease the delay Δt. Figure 9 In the example, wearable device 110 increases the smoothing factor from 0.05 to 0.1 and decreases the latency from 0.25 to a value in the range of 0.15 to 0.20. Furthermore, wearable device 110 uses a compensation factor κ... comp The setting is adjusted from 1 to 1.2. Typically, when operating in a walking motion type, if the user's tempo exceeds 140 at 120, torque attenuation may occur due to smoothing or low-pass filtering. In contrast, in one or more example embodiments, by switching to a running motion type 930 when the tempo 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. Therefore, the amount of torque attenuation can be compensated.

[0099] While the wearable device 110 is operating in a walking-to-running exercise type 920, when the user 120's pace reaches 110, the wearable device 110 changes the walking-to-running exercise type 920 to a walking exercise type 910. Due to the change in exercise type, the wearable device 110 adjusts control factors. For example, the wearable device 110 may decrease the smoothing factor and / or increase the latency. Figure 9 In the example, wearable device 110 reduces the smoothing factor to 0.05 and increases the latency to 0.25.

[0100] While the wearable device 110 is operating in a walking-to-running exercise type 920, if the user 120's pace reaches 150 or higher, the wearable device 110 will change the walking-to-running exercise type 920 to a running exercise type 930. Due to the change in exercise type, the wearable device 110 adjusts control factors. For example, the wearable device 110 may increase the smoothing factor and / or decrease the delay. Furthermore, the wearable device 110 may increase the compensation factor. When the user 120's pace exceeds 140, torque decay can occur as described above. Therefore, the wearable device 110 can use an adjusted smoothing factor to perform smoothing and use an adjusted delay and an adjusted compensation factor to generate torque so that torque decay does not occur when changing from walking-to-running exercise type 920 to running exercise type 930.

[0101] While the wearable device 110 is operating in running exercise type 930, when the user 120's pace reaches 110, the wearable device 110 changes the running exercise type 930 to walking exercise type 910. Due to the change in exercise type, the wearable device 110 adjusts control factors. For example, the wearable device 110 may decrease the smoothing factor and / or increase the latency. In addition, the wearable device 110 may adjust the compensation factor from 1.2 to 1.

[0102] As described above, the control factor can be varied for each of the movement types 910, 920, and 930. That is, with the same gain κ, following an ordered sequence from walking movement type 910 to walking to running movement type 920 and then to running movement type 930, the smoothing factor α can be increased, and the delay Δt can be decreased. By adjusting the control factor as described above, the magnitude of the torque can increase linearly and stably as the user 120's movement speed increases. Furthermore, the magnitude of the torque can increase linearly and stably as the gain increases linearly. Therefore, the linear response characteristics and control stability of the wearable device 110 can be improved.

[0103] <When gain κ is less than -5>

[0104] While the wearable device 110 is operating under a high-resistance motion type 940, q length When q is less than 0.4, the wearable device 110 changes from a high-resistance motion type 940 to a slow motion type 950. That is, while the wearable device 110 is operating in a high-resistance motion type 940, when q... length When q is less than 0.4, the wearable device 110 changes the high-resistance motion type 940 to a slow motion type 950, and when q length When the resistance is less than 0.5 and greater than or equal to 0.4, the wearable device 110 maintains a high-resistance motion type 940. Figure 9 In the example, although the high-resistance motion type 940 changes to the slow motion type 950, the smoothing factor and delay may remain unchanged. Similarly, when the slow motion type 950 changes to the high-resistance motion type 940, the smoothing factor and delay may remain unchanged. However, the example is not limited to what has been described above, and at least one of the smoothing factor and delay may be configured to change in response to a change in motion type.

[0105] When the user's movement speed increases under high-resistance motion type 940, the magnitude of the torque also increases linearly and stably. Furthermore, when the gain increases linearly, the magnitude of the torque also increases linearly and stably. Therefore, under high-resistance motion type 940, the linear response characteristics and control stability of the wearable device 110 can be improved.

[0106] As described above, conventionally, when operating in the walking motion type, if the user 120's rhythm reaches a threshold, due to smoothing or low-pass filtering, saturation or torque decay may occur where the torque increases disproportionately to the walking speed. Furthermore, when operating in the walking motion type, if the user 120's rhythm exceeds a second threshold higher than the first threshold, torque decay may occur due to smoothing or low-pass filtering.

[0107] In contrast, in one or more example embodiments, when the gain κ is greater than or equal to a reference value, the wearable device 110 can operate in a first reference mode or a second reference mode to adjust control factors (e.g., smoothing factor α and delay Δt) by switching between walking motion type 910, walking-to-running motion type 920, and running motion type 930 based on rhythm, such that as the rhythm increases from walking motion type 910 to walking-to-running motion type 920 and then to running motion type 930, the smoothing factor α increases in an ordered order, and the delay Δt decreases in an ordered order. Therefore, in the first interaction mode (i.e., the assist mode when the gain κ is positive), during movement between motion types 910 and 930, the magnitude of the assist torque can increase linearly and stably with the increase of the user 120's rhythm, and in the second interaction mode (i.e., the resistance mode when the gain κ is negative), during movement between motion types 910 and 930, even if the gain κ remains the same across multiple motion types, the magnitude of the resistance torque can increase linearly and stably with the increase of the user 120's rhythm. Furthermore, when the gain κ is less than the reference value, the wearable device 110 can operate in a third reference mode to maintain constant control factors (e.g., smoothing factor α and delay Δt) in both high-resistance motion type 940 and slow motion type 950, such that the smoothing factor α is relatively high compared to the second interaction mode to generate stronger drag torque or relatively smaller auxiliary torque, while the delay Δt is relatively short. Therefore, the linear response characteristics and control stability of the wearable device 110 can be improved.

[0108] Figure 10 This is a flowchart illustrating an example of the operation method of a wearable device 110 according to at least one example embodiment.

[0109] Reference Figure 10 In operation 1010, wearable device 110 processes state variables defined based on motion information of user 120. For example, wearable device 110 can process state variable y raw (t) is used for smoothing.

[0110] In operation 1020, wearable device 110 determines its interaction mode based on a gain associated with the magnitude of the torque, wherein the gain can be input by the user. In some other example embodiments, instead of the user inputting the gain and the controller 310 determining the interaction mode based on the input gain, the user can input an interaction mode, and the controller 310 can determine a default gain associated with the input interaction mode.

[0111] In operation 1030, the wearable device 110 selects a movement type from the determined interaction patterns based on the user 120's gait parameters. 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 and second gait feature values ​​described above.

[0112] In operation 1040, the wearable device 110 determines the torque control factor based on the selected motion type. For example, the wearable device 110 may search a lookup table for the control factor corresponding to the selected motion type. Alternatively, the wearable device 110 may determine the control factor corresponding to the selected motion type via a regression function or regression analysis. In one example, the control factor for each motion type may be optimized through training.

[0113] In operation 1050, the wearable device 110 generates torque based on the processed state variables, gain, and determined control factors. The processed state variables may correspond to y(t) as described above. The wearable device 110 can generate torque by applying different control factors to each type of motion. In this way, the linear response characteristics and control stability of the wearable device 110 can be improved.

[0114] For the above references Figure 10 For a detailed description of the operating method, please refer to the above. Figures 1 to 9 The content described.

[0115] Figure 11 This is a diagram illustrating an example of a wearable device 110 according to at least one example embodiment.

[0116] Reference Figure 11 The wearable device 110 includes a controller 310 and a driver 1110.

[0117] In addition, wearable device 110 may include a user interface (UI) device and one or more sensors. The UI device may be configured to receive input from a user of a gain associated with the magnitude of the torque. The UI device may include various suitable devices (e.g., switches, knobs, and jog dials) configured to set exercise modes. The UI device may be replaced by an external remote control or smart device and may not need to be included in wearable device 110. The sensors may be angle sensors (e.g., potentiometers, absolute encoders, or incremental encoders) configured to measure the angle of a joint.

[0118] The controller 310 can be implemented as processing circuitry (such as hardware including logic circuitry), a hardware / software combination (such as a processor executing software), or a combination thereof and memory. For example, the processing circuitry 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 circuitry can be executing the functions described above. Figures 1 to 10 The described wearable device 110 has dedicated processing circuitry for overall operation. For example, controller 310 can process state variables defined based on motion information of user 120 and determine the interaction mode of wearable device 110 based on a gain associated with the magnitude of torque, wherein the gain can be input by user 120 via a user interface (UI) device. In some other example embodiments, instead of user inputting gain and controller 310 determining the interaction mode based on the input gain, user can input an interaction mode, and controller 310 can determine a default gain associated with the input interaction mode. Furthermore, controller 310 can select a motion type from the determined interaction modes based on user 120's gait parameters and determine a control factor for torque based on the selected motion type. Controller 310 can then control driver 1110 based on the processed state variables, gain, and determined control factor. Therefore, the processing circuitry can improve the functionality of wearable device 110 itself by linearly and stably increasing the magnitude of torque as user 120's motion speed increases, thereby improving the linear response characteristics and control stability of wearable device 110.

[0119] The driver 1110 can generate torque under the control of the controller 310.

[0120] Wearable device 110 may include a single driver (e.g., such as...) Figure 11 The driver 1110 shown herein, or includes multiple drivers (e.g., such as...). Figure 2 and Figure 3 The drives shown are 210-1 and 210-2.

[0121] For the above references Figure 11 For a detailed description of the wearable device 110, please refer to the above-mentioned references. Figures 1 to 10 The content described.

[0122] The units and / or modules described herein may be implemented using hardware and software components. For example, hardware components may include microphones, amplifiers, bandpass filters, audio-to-digital converters, and processing devices. Processing devices may be implemented using one or more hardware devices configured to perform and / or execute program code by performing arithmetic, logical, and input / output operations. One or more processing devices may include processors, controllers, arithmetic logic units, digital signal processors, microcomputers, field-programmable arrays, programmable logic units, microprocessors, or any other device capable of responding to and executing instructions in a defined manner. The processing device may run an operating system (OS) and one or more software applications running on the OS. The processing device may also access, store, manipulate, process, and create data in response to the execution of software. For simplicity, the description of processing devices is used as the singular; however, those skilled in the art will understand that processing devices may include multiple processing elements and various types of processing elements. For example, a processing device may include multiple processors or a processor and a controller. Furthermore, different processing configurations are feasible (such as parallel processors).

[0123] Software may include computer programs, code segments, instructions, or combinations thereof, to independently or jointly instruct and / or configure a processing device to operate as needed, thereby turning the processing device into a dedicated processor. Software and data may be permanently or temporarily implemented in any type of machine, component, physical or virtual device, computer storage medium, or apparatus, or in propagated signal waves capable of providing instructions or data to or being interpreted by the processing device. Software may also be distributed across networked computer systems, enabling it to be stored and executed in a distributed manner. Software and data may be stored on one or more non-transitory computer-readable recording media.

[0124] The methods according to the above example embodiments can be recorded in a non-transitory computer-readable medium, which includes program instructions for implementing the various operations of the above example embodiments. 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 specifically designed and constructed for the purposes of the example embodiments, or they may be of a type 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 magnetic tapes), optical media (such as CD-ROMs, DVDs, and / or Blu-ray discs), magneto-optical media (such as optical discs), and hardware devices specifically configured to store and execute program instructions (such as read-only memory (ROM), random access memory (RAM), flash memory (e.g., 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 executable by a computer using an interpreter. The aforementioned devices may be configured to act as one or more software modules to perform the operations of the above example embodiments, or vice versa.

[0125] Some exemplary embodiments have been described above. However, it should be understood that various modifications can be made to these exemplary embodiments. For example, suitable results may be achieved if the described techniques are performed in a different order and / or if the components in the described system, architecture, apparatus, or circuit are combined in a different manner and / or replaced or supplemented by other components or their equivalents. Therefore, other embodiments are within the scope of the claims.

Claims

1. A method for operating a wearable device, the method comprising: Obtain the angle of the user's hip joint; Based on the user's gait information, the user's first movement type is determined, and based on the hip joint angle and the first control factor value obtained under the first movement type, a first torque is output to the user; as well as When the user's movement speed changes, a second movement type different from the first movement type is determined, and a second torque is output to the user based on the hip joint angle and the second control factor value obtained under the second movement type. Gait information is based on either the time or distance a user spends walking. The first control factor value for the first torque under the first motion type is different from the second control factor value for the second torque under the 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 includes: Based on the gain associated with the torque magnitude of the wearable device, the interaction mode of the wearable device is determined as either a first interaction mode that assists the user in moving 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 a gain that is a positive number greater than or equal to the reference value, the interaction mode is determined as the first interaction mode; and In response to a gain that is 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 a delay of a first delay value associated with the output timing of the first torque. The second torque is output after being delayed by a second delay value associated with the 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 a user in the second movement type is greater than the first rhythm of a user in the first movement type, the second delay value is less than the first delay value.

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

7. The operating method according to claim 6, wherein, When the second rhythm of a user in the second movement type is greater than the first rhythm of a user in the first movement 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 used to filter the hip joint angle obtained under the first motion type, and The second control factor value includes a second smoothing factor value used to filter the hip joint angle obtained under the second motion type.

9. A wearable device, comprising: sensor; drive; as well as The controller is configured to control the driver. The controller is configured as follows: Use sensors to obtain the angle of the user's hip joint; Based on the user's gait information, the user's first movement type is determined, and based on the hip joint angle and first control factor value obtained under the first movement type, the actuator outputs a first torque; and When the user's movement speed changes, a second motion type different from the first motion type is determined, and the driver outputs a second torque based on the hip joint angle and the second control factor value obtained under the second motion type. Gait information is based on either the time or distance a user spends walking. The first control factor value for the first torque under the first motion type is different from the second control factor value for the second torque under the 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 the interaction mode of the wearable device based on the gain associated with the magnitude of the torque of the wearable device: either a first interaction mode that assists the user in moving or a second interaction mode that applies resistance to the user's movement.

11. The wearable device according to claim 10, wherein, The controller is configured as follows: In response to a gain that is a positive number greater than or equal to the reference value, the interaction mode is determined as the first interaction mode; and In response to a gain that is 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 a delay of a first delay value associated with the output timing of the first torque. The second torque is output after being delayed by a second delay value associated with the 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 a user in the second movement type is greater than the first rhythm of a user in the first movement type, the second delay value is less than the first delay value.

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

15. The wearable device according to claim 14, wherein, When the second rhythm of a user in the second movement type is greater than the first rhythm of a user in the first movement 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 used to filter the hip joint angle obtained under the first motion type, and The second control factor value includes a second smoothing factor value used to filter the hip joint angle obtained under the second motion type.

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