Intelligent tension sensing training device
By using a combination of core sheath conductive composite yarn and solid elastic cylindrical parts in the sensing tension training device, combined with multi-channel capacitive signal acquisition and deep learning model, the adaptability and interactivity problems of the existing devices are solved, and efficient three-dimensional force monitoring and personalized training guidance are achieved.
Patent Information
- Application Number
- CN202510419179.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-03
- Publication Date
- 2025-08-08
AI Technical Summary
The existing sensing tension training devices are difficult to adapt to the physical characteristics and training needs of different users, cannot adjust training parameters in real time, lack of resolution of kinematic parameters, lack of effective user interaction design, and low user autonomy.
The sensing tension rope composed of core-sheath conductive composite yarn and solid elastic cylindrical parts is used to identify and guide user actions through multi-channel capacitive signal acquisition and deep learning model, and combine Bluetooth module to realize data synchronization, providing a personalized training solution.
Accurate monitoring of internal forces in three-dimensional space is achieved, the accuracy of training pattern recognition and user interaction experience is improved, and the user's autonomy and training effect are enhanced.
Smart Images
Figure CN120445472A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of sensor training devices, and in particular to an intelligent tension sensor training device. Background Art
[0002] Existing sensor tension training devices are often difficult to design to meet the physical characteristics and training needs of different users. During training, the user's physical condition and training needs will change as the training progresses, but existing equipment often finds it difficult to adjust training parameters in real time and cannot provide personalized training plans.
[0003] Most existing sensor-based tension training devices rely on a single sensor signal (such as a force sensor or position sensor). Force-sensitive resistors cannot capture dynamic acceleration characteristics, while inertial measurement units struggle to distinguish between active force and inertial components. This data gap results in insufficient resolution of kinematic parameters for complex training exercises (such as oblique cable presses and rotational cable explosive power training), resulting in generally low training pattern recognition accuracy. Consequently, data collection is incomplete and cannot accurately reflect the training intention and movement status.
[0004] However, existing systems generally adopt a single-point sensor layout (usually concentrated on the handle or end anchor point), which can only obtain the scalar value of the axial tension and cannot solve the resultant force vector distribution in three-dimensional space, resulting in error tolerance in motion trajectory analysis.
[0005] Finally, due to the lack of effective user interaction design, existing equipment often requires professional operation and guidance during use, and users are less convenient and motivated to operate independently. Summary of the Invention
[0006] The purpose of the present invention is to provide an intelligent tension sensing training device to address the above-mentioned deficiencies in the prior art.
[0007] The purpose of the present invention is to provide an intelligent tension sensing training device, comprising a sensor tension rope, a detection module, a microcontroller and a display; The sensing tension rope includes a core-sheath conductive composite yarn and a solid elastic cylindrical member, wherein a plurality of the core-sheath conductive composite yarns are interwoven in warp and weft to form a braided layer tightly wrapped around the solid elastic cylindrical member; the core-sheath conductive composite yarn includes a conductive yarn, a first elastic yarn, and a second elastic yarn, wherein the conductive yarn is wrapped around the first elastic yarn, and the second elastic yarn forms an insulating braided shell interlaced and wrapped around the conductive yarn; The detection module includes multiple collection channels, the core-sheath conductive composite yarns at one end of the sensing tension rope are grouped in two, each group of core-sheath conductive composite yarns is electrically connected to one collection channel of the detection module, and the detection module is connected to the microcontroller; The detection module is used to collect and detect the capacitance signal from the sensor pull rope and transmit the capacitance signal to the microcontroller; The microcontroller processes and identifies the collected capacitance signal, determines whether the user has performed the correct pulling movement, outputs the judgment result and provides language interaction guidance, and generates a specific average pulling force value and outputs it for display on the display.
[0008] Furthermore, the intersection nodes of the multiple groups of core-sheath conductive composite yarns are evenly distributed on the curved wall of the solid elastic cylindrical member.
[0009] Furthermore, the number of the core-sheath conductive composite yarns is 8-32, and the intersection nodes of at least four groups of core-sheath conductive composite yarns are evenly distributed on the curved wall of the solid elastic cylindrical member.
[0010] Furthermore, the elastic coefficient of the braided layer is not greater than the elastic coefficient of the solid elastic cylindrical member.
[0011] Furthermore, the intercept of the braided layer is 18-36 mm.
[0012] Furthermore, the conductive yarn is conductive silver yarn; the first elastic yarn is spandex; and the second elastic yarn is nylon.
[0013] Furthermore, it includes handles respectively connected to one end of the sensor pull rope, the handles include a shell, and the detection module, microcontroller, and display are arranged in the shell.
[0014] Furthermore, it also includes a Bluetooth module, which is electrically connected to the microcontroller to realize wireless data transmission to the smart device.
[0015] Furthermore, a database of various training actions is pre-set on the microcontroller and embedded with a deep learning model. Users can conduct training directly. The deep learning model quickly identifies the content of the training actions, compares them with the standards in the database, and provides language interaction guidance.
[0016] Furthermore, the preparation method of the sensor pull rope includes the following steps: S1, wrapping the conductive yarn around the first elastic yarn to form a conductive elastic composite yarn; S2. The second elastic yarn is wound onto a plurality of yarn feeding shafts using a winding machine. The yarn feeding shafts are then fixed to the yarn disc of the high-speed braiding machine. The conductive elastic composite yarn is wound onto a fixed spool and fed into the axis of the high-speed braiding machine through a pre-tightening device. The plurality of second elastic yarns are interwoven to form an insulating braided outer shell. The braided outer shell is interlaced and wound around the conductive elastic composite yarn as the disc rotates. The high-speed braiding machine is operated to ultimately form a core-sheath conductive composite yarn. S3. The solid elastic cylindrical part is wound on a constant bobbin and fed into the axis of the high-speed braiding machine through a pre-tightening device. At the same time, the core-sheath conductive composite yarn is wound on multiple yarn feeding shafts. Under the action of the high-speed rope braiding machine, it is interwoven to form a braided layer, tightly wrapped around the solid elastic cylindrical part to form a sensor tension rope. The present invention embeds a solid elastic cylinder as the core inside the braided layer formed by the core-sheath conductive composite yarn to form a sensing tension rope with excellent elasticity and strength. The capacitance signal output is achieved by utilizing the change in contact area of the braided layer with the change in tension and the change in electrode spacing with the change in tension. The detection module accurately measures the capacitance change rate and adopts multi-channel acquisition to collect capacitance signals extending along the length direction of the braided layer to monitor the force conditions in all directions. The microcontroller can accurately identify each action and compare it with the data in the database, output the judgment result and provide language interaction guidance. At the same time, the microcontroller processes this data and converts the information into force value for display on the display screen.
[0017] The sensing tension rope remains tight during the stretching process, and the change in capacitance is not only caused by its own stretching, but also by the force of the solid elastic cylindrical member. That is, under the action of the coupling force, each node of the system is compressed flatter and fits more tightly, resulting in a larger contact area and a smaller distance between the two electrodes, so that the change in its electrical signal is more obvious and the sensitivity is higher at the same stretching rate. The intelligent tension sensing training device of the present invention has good stability, fast response speed, and high sensitivity, which enhances the human-computer interaction experience and allows data to be synchronized to smart devices, providing a convenient real-time monitoring function. Moreover, the sensing points of the present application are distributed throughout the entire sensing tension rope, not the end, and the vector distribution of each force in three-dimensional space can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 It is a structural schematic diagram of the sensing tension rope of the present invention; Figure 2 Schematic diagram of the force structure of a tension rope formed by a braided layer without a solid elastic cylindrical member in the center under tension; Figure 3 and Figure 4 Schematic diagram of the force structure of the sensing tension rope of the present invention under tension; Figure 5 is a cross-sectional diagram of the sensing tension rope; Figure 6 This is a comparison chart of the tensile signals of the sensing tension ropes with weaving intercepts of 14mm, 22mm, 28mm, and 36mm; Figure 7 Schematic diagram of the structure of the core-sheath conductive composite yarn; Figure 8This is a structural diagram of an embodiment of an intelligent tension sensing training device of the present invention; Figure 9 A comparison chart of the tensile signals of a tension rope formed by a braided layer without a solid elastic cylindrical member in the center (hollow structure inside) and the sensing tension rope of the present invention (composite fitting structure); Figure 10 A sensitivity comparison chart of a tension rope formed by a braided layer without a solid elastic cylindrical member in the center (inner hollow structure) and a sensing tension rope (composite fitting structure) of the present invention; Figure 11 is the response time of the rapid stretching of the sensing tension rope of the present invention; Figure 12 The tensile signal of the sensing tension rope of the present invention at different tensile speeds; Figure 13 and Figure 14 It is the capacitance change of the sensing tension rope of the present invention at different stretching rates. DETAILED DESCRIPTION
[0019] The following are specific embodiments of the present invention and the accompanying drawings to further describe the technical solutions of the present invention, but the present invention is not limited to these embodiments.
[0020] The purpose of the present invention is to provide an intelligent tension sensing training device, comprising a sensor tension rope, a detection module, a microcontroller and a display; like Figure 1 As shown, the sensing tension rope includes a core-sheath conductive composite yarn and a solid elastic cylindrical member. Multiple core-sheath conductive composite yarns are interwoven in warp and weft to form a braided layer tightly wrapped around the solid elastic cylindrical member. The core-sheath conductive composite yarn includes a conductive yarn, a first elastic yarn, and a second elastic yarn. The conductive yarn is wrapped around the first elastic yarn, and the second elastic yarn forms an insulating braided shell interlaced around the conductive yarn. The detection module includes multiple collection channels. The core-sheath conductive composite yarns at one end of the sensing tension rope are grouped in two. Each group of core-sheath conductive composite yarns is electrically connected to a collection channel of the detection module. The detection module is connected to a microcontroller. A detection module is used to collect and detect the capacitance signal from the sensor pull rope and transmit the capacitance signal to the microcontroller; The microcontroller processes and identifies the collected capacitance signals, determines whether the user has performed the correct pulling movement, outputs the judgment results and provides language interaction guidance, and generates a specific average pulling force value and outputs it on the display.
[0021] An intelligent tension sensing training device of the present invention embeds a solid elastic cylinder as the core inside the braided layer formed by the core-sheath conductive composite yarn to form a sensing tension rope with excellent elasticity and strength. The contact area change of the braided layer with the tension and the electrode spacing change with the tension are used to realize the capacitance signal output. The detection module accurately measures the capacitance change rate and adopts multi-channel acquisition to collect the capacitance signals extending along the length direction of the braided layer to realize the monitoring of the force conditions in all directions. The microcontroller can accurately identify each action and compare it with the data in the database, and output the judgment result and provide language interaction guidance. At the same time, the microcontroller processes this data and converts the information into force value for display on the display screen.
[0022] Figure 2 Schematic diagram of the force structure of the tension rope formed by the braided layer without a solid elastic cylinder in the center under tension. Figure 3 and Figure 4 This is a schematic diagram of the force structure of the sensor pull rope of the present invention under tension. When not in use, the braided layer is tightly wrapped around the solid elastic cylindrical part and applies a certain pressure to the solid elastic cylindrical part. The elastic coefficient of the braided layer is not greater than the elastic coefficient of the solid elastic cylindrical part. After stretching, the braided layer on the outer layer continues to apply pressure to the inner solid elastic cylindrical part. The sensor pull rope remains tight during the stretching process. The solid elastic cylindrical part hinders the deformation of the braided layer and applies an outward force. The change in capacitance is not only caused by its own stretching, but also by the force of the solid elastic cylindrical part (such as Figure 3 . Figure 4 ), that is, under the action of coupling force, each node of the system is compressed flatter and fits more tightly, resulting in a larger contact area and a smaller distance between the two electrodes, making the electrical signal change more obvious and the sensitivity higher at the same stretching rate (such as Figure 5 shown).
[0023] This can be confirmed by the following formula:
[0024] The smaller d (spacing), the larger S (contact area), and the greater the capacitance.
[0025] In order to more accurately monitor the stress in all directions, the intersection nodes of multiple groups of core-sheath conductive composite yarns are evenly distributed on the curved wall of the solid elastic cylindrical part. The number of core-sheath conductive composite yarns is 8-32, and the intersection nodes of at least 4 groups of core-sheath conductive composite yarns are evenly distributed on the curved wall of the solid elastic cylindrical part. Figure 5As shown, in this embodiment, the braided layer consists of 16 spindles. Eight spindles are taken from four directions for positioning, and are represented by numbers in groups (Group 1, Group 2, Group 3, and Group 4). The four groups are connected to four acquisition channels, respectively, to obtain four channels of electrical signals. This multi-channel design can identify whether there are any angle changes during the tension process.
[0026] For example, during straight-line stretches (such as upright cable rows), the performance of all four channels is similar. However, during curved movements (such as lateral cable raises, where one end of the cable is secured at the ankle and the other at the shoulder), the force distribution becomes uneven due to the curved nature of the movement. Due to the presence of the solid elastic cylinder, the outer channels have a larger overall radius during curved movements, while the inner channels have a smaller radius. This results in different forces and signals from different channels. Deep learning based on this feature can identify different movements.
[0027] By changing the ratio of the number of yarn feed shafts and constant spools, as well as the various weaving parameters, different specifications of sensor pull ropes can be obtained. The more core-sheath conductive composite yarns there are, the more sensor capacitors can be produced. However, since the sensor signal is caused by tension, considering the rotation and slippage of the tension rope and the fixed length of the final product, and due to the limitations of the machine itself, the weaving intercept of the braided layer can only be adjusted between 18-36mm. At the same yarn tightness, the shorter the intercept, the more nodes there are and the denser the weaving. The entire force form can be divided into: slip rotation; then slip rotation with compression caused by stretching; and finally compression caused by stretching alone. Among them, the denser the braid, the greater the interference of rotation and slippage. Compared with the capacitance change caused by rotation and slippage, the capacitance change caused by compression caused by stretching will be greater. This change is the dominant factor in the capacitance change. Therefore, the experiment is divided into four cases, 14mm, 22mm, 28mm, and 36mm. Among them, 14mm and 22mm first rotate and slide, and then undergo tension and compression; 28mm is subjected to tension and compression at the same time as rotation and compression, and 36mm is only subjected to tension and compression.
[0028] Figure 6 The following is a comparison of the tensile signal of the sensing tension rope with braiding lengths of 14mm, 22mm, 28mm, and 36mm. Based on comprehensive performance considerations, a braiding length of 28mm was used for mass production.
[0029] In one embodiment, Figure 7 As shown, the conductive yarn can be conductive silver yarn; the first elastic yarn is spandex; and the second elastic yarn is nylon.
[0030] like Figure 8As shown, an intelligent tension sensing training device of the present invention may further include a handle connected to one end of the sensing tension rope, the handle including a shell, a detection module, a microcontroller, and a display arranged in the shell. Depending on actual needs, a handle or other woven parts may also be provided at the other end.
[0031] A Bluetooth module can also be included, electrically connected to the microcontroller to enable wireless data transmission to a smart device, such as a mobile phone or tablet. This smart device can monitor and save training data in real time. This implementation is well-established technology. For example, the microcontroller can be a K210 single-chip microcomputer. The microcontroller transmits electricity to the conductive yarn to form a circuit, and a detection module (which can be a capacitive sensor) collects and detects the electrical signal. The microcontroller then transmits the signal to the smart device via the Bluetooth module.
[0032] A database of various training actions is pre-set on the microcontroller and embedded with a deep learning model. The user can conduct training directly. The deep learning model quickly identifies the content of the training action, compares it with the standards in the database, and provides language interaction guidance. The deep learning model here can be LSTM or CNN, etc., which is not limited here. The data in the database is the corresponding capacitance signal obtained by training according to the established actions.
[0033] For example, when users use the present invention for training, there is a risk of incorrect movements. This product has a pre-set database of various rehabilitation movements, and patients can directly train. The deployed deep learning model can quickly identify the movement content and compare it with the standards in the database. For example, if the rotation angle is too large (the electrical signal of a certain channel is abnormally high), language interaction guidance will be given.
[0034] This product acquires electrical signals and smoothes them through hardware filtering (RC low-pass filtering). However, during training, users may experience weakness, and repeated training may lead to insufficient strength, causing jitter, or swaying with amplitude. In the original motion image, all four channels show fluctuations caused by jitter and impact (lower levels are filtered out, and fluctuations can only be displayed after reaching the set threshold), triggering a voice alarm.
[0035] The microcontroller transmits electricity to the capacitor to form a circuit. The detection module (which can be a capacitive sensor) receives the electrical signal. The MCU then transmits the signal via Bluetooth to a host computer (the cloud, for online data analysis, effectively digitizing the movements). Simultaneously, a deep learning model deployed within the MCU performs motion classification, recognition, and comparison. Upon receiving the results, the MCU executes the corresponding command, such as verbal instructions (excessive force, excessive jitter, and fatigue). The screen also converts the capacitive signal into a force value in real time.
[0036] In the present invention, the preparation method of the sensor pull rope may include the following steps: S1, wrapping the conductive yarn around the first elastic yarn to form a conductive elastic composite yarn; S2. The second elastic yarn is wound onto a plurality of yarn feeding shafts using a winding machine. The yarn feeding shafts are then fixed to the yarn disc of the high-speed braiding machine. The conductive elastic composite yarn is wound onto a fixed spool and fed into the axis of the high-speed braiding machine through a pre-tightening device. The plurality of second elastic yarns are interwoven to form an insulating braided outer shell. The braided outer shell is interlaced and wound around the conductive elastic composite yarn as the disc rotates. The high-speed braiding machine is operated to ultimately form a core-sheath conductive composite yarn. S3. The solid elastic cylindrical part is wound on a constant bobbin and fed into the axis of the high-speed braiding machine through a pre-tightening device. At the same time, the core-sheath conductive composite yarn is wound on multiple yarn feeding shafts. Under the action of the high-speed rope braiding machine, it is interwoven to form a braided layer, which is tightly wrapped around the solid elastic cylindrical part to form a sensing tension rope.
[0037] The detailed preparation process of the sensor pull rope provided in this embodiment is as follows: The conductive silver yarn is wrapped around the spandex to form a conductive elastic composite yarn; The nylon fibers are wound onto a yarn spool using a winding machine, and the yarn spool is then fixed to the yarn disc of a high-speed braiding machine, with the number of winding turns being 3,000. The conductive elastic composite yarn is wound onto a constant bobbin and fed into the axis of the high-speed braiding machine through a pre-tightening device. There are 16 yarn feed shafts and 1 constant bobbin. Multiple nylon strands are interwoven to form an insulating braided shell, and the braid is interlaced and wound around the conductive elastic composite yarn as the disc rotates. The high-speed braiding machine operates at a braiding speed of 15 r / min, a winding speed of 3 m / min, and a braiding spacing of 80 mm. The reciprocating motion from one disc to another facilitates the tight weaving of the nylon onto the conductive elastic composite yarn, forming a core-sheath conductive composite yarn. At this point, the core-sheath conductive composite yarn is produced, which is woven into several meters and wound around 16 spindles.
[0038] Next, prepare an 8mm diameter solid elastic cylinder and 16 newly wound spindles of core-sheath conductive composite yarn. First, the rubber rope is wound onto a constant bobbin and fed into the axis of a high-speed braiding machine via a preloaded device. Simultaneously, 16 spindles of core-sheath conductive composite yarn are wound onto the high-speed braiding machine, interweaving to form a braided shell that tightly wraps around the solid elastic cylinder. The high-speed braiding machine operates at a braiding speed of 20 r / min, a winding speed of 5 m / min, and various braiding pitches. The reciprocating motion from one disc to another helps the core-sheath conductive composite yarn tightly wrap around the rubber rope, forming the sensing tension rope.
[0039] The sensing tension rope prepared in this embodiment and the tension rope of the same length formed by the braided layer without the solid elastic cylindrical member were assembled into intelligent tension sensing training devices for testing.
[0040] Figure 9 This is a comparison diagram of the tensile signals of a tension rope formed by a braided layer without a solid elastic cylindrical part in the center (internally hollow structure) and the sensing tension rope of the present invention (composite fitting structure). It can be seen from the figure that the sensing tension rope of the present invention has higher sensitivity.
[0041] Figure 10 This is a sensitivity comparison diagram of a tension rope formed by a braided layer without a solid elastic cylindrical part in the center (inner hollow structure) and a sensing tension rope (composite fitting structure) of the present invention. It can be seen from the figure that the sensing tension rope of the present invention has higher sensitivity.
[0042] Figure 11 is the response time of the rapid stretching of the sensor pull rope of the present invention. It can be seen from the figure that the sensor pull rope of the present invention has a fast response speed.
[0043] Figure 12 It is the stretching signal of the sensing tension rope of the present invention at different stretching speeds.
[0044] Figure 13 and Figure 14 It is the capacitance change of the sensing tension rope of the present invention at different stretching rates.
[0045] As can be seen from the above test figures, the sensor tension rope of the present invention has good electrical performance and high sensitivity, which improves the stability and response speed of the product, meets the market demand for high-performance sensor fiber ropes, and expands its application potential in smart homes, sports monitoring, healthcare and other fields.
[0046] Any matters not mentioned above shall be subject to the existing technology.
[0047] Although some specific embodiments of the present invention have been described in detail through examples, those skilled in the art should understand that the above examples are for illustration only and are not intended to limit the scope of the present invention. Those skilled in the art of the present invention may make various modifications or additions to the described specific embodiments or replace them in similar ways, but they will not deviate from the direction of the present invention or exceed the scope defined by the appended claims. Those skilled in the art should understand that any modifications, equivalent replacements, improvements, etc. made to the above embodiments based on the technical essence of the present invention should be included in the scope of protection of the present invention.
Claims
1. An intelligent tension sensor training device, characterized by: It includes a sensing pull rope, a detection module, a microcontroller and a display; The sensing tension rope includes a core-sheath conductive composite yarn and a solid elastic cylindrical member, wherein a plurality of the core-sheath conductive composite yarns are interwoven in warp and weft to form a braided layer tightly wrapped around the solid elastic cylindrical member; the core-sheath conductive composite yarn includes a conductive yarn, a first elastic yarn, and a second elastic yarn, wherein the conductive yarn is wrapped around the first elastic yarn, and the second elastic yarn forms an insulating braided shell interlaced and wrapped around the conductive yarn; The detection module includes multiple collection channels, the core-sheath conductive composite yarns at one end of the sensing tension rope are grouped in two, each group of core-sheath conductive composite yarns is electrically connected to one collection channel of the detection module, and the detection module is connected to the microcontroller; The detection module is used to collect and detect the capacitance signal from the sensor pull rope and transmit the capacitance signal to the microcontroller; The microcontroller processes and identifies the collected capacitance signal, determines whether the user has performed the correct pulling movement, outputs the judgment result and provides language interaction guidance, and generates a specific average pulling force value and outputs it for display on the display.
2. The intelligent tension sensor training device according to claim 1, characterized in that: The intersection nodes of multiple groups of core-sheath conductive composite yarns are evenly distributed on the curved surface wall of the solid elastic cylindrical part.
3. The intelligent tension sensor training device according to claim 1, characterized in that: The number of the core-sheath conductive composite yarns is 8-32, and the intersection nodes of at least four groups of core-sheath conductive composite yarns are evenly distributed on the curved surface wall of the solid elastic cylindrical member.
4. The intelligent tension sensor training device according to claim 1, characterized in that: The elastic coefficient of the braided layer is not greater than the elastic coefficient of the solid elastic cylindrical member.
5. The intelligent tension sensor training device according to claim 1, characterized in that: The intercept of the braided layer is 18-36 mm.
6. The intelligent tension sensor training device according to claim 1, characterized in that: The conductive yarn is conductive silver yarn; the first elastic yarn is spandex; and the second elastic yarn is nylon.
7. The intelligent tension sensor training device according to claim 1, characterized in that: It includes handles respectively connected to one end of the sensor pull rope, the handles include a shell, and the detection module, microcontroller and display are arranged in the shell.
8. The intelligent tension sensor training device according to claim 1, characterized in that: It also includes a Bluetooth module, which is electrically connected to the microcontroller to achieve wireless data transmission to the smart device.
9. The intelligent tension sensor training device according to claim 1, characterized in that: A database of various training actions is pre-set on the microcontroller and embedded with a deep learning model. Users can conduct training directly. The deep learning model quickly identifies the content of the training actions, compares them with the standards in the database, and provides language interaction guidance.
10. An intelligent tension sensor training device according to any one of claims 1 to 9, characterized in that: The preparation method of the sensing pull rope comprises the following steps: S1, wrapping the conductive yarn around the first elastic yarn to form a conductive elastic composite yarn; S2. The second elastic yarn is wound onto a plurality of yarn feeding shafts using a winding machine. The yarn feeding shafts are then fixed to the yarn disc of the high-speed braiding machine. The conductive elastic composite yarn is wound onto a fixed spool and fed into the axis of the high-speed braiding machine through a pre-tightening device. The plurality of second elastic yarns are interwoven to form an insulating braided outer shell. The braided outer shell is interlaced and wound around the conductive elastic composite yarn as the disc rotates. The high-speed braiding machine is operated to ultimately form a core-sheath conductive composite yarn. S3. The solid elastic cylindrical part is wound on a constant bobbin and fed into the axis of the high-speed braiding machine through a pre-tightening device. At the same time, the core-sheath conductive composite yarn is wound on multiple yarn feeding shafts. Under the action of the high-speed rope braiding machine, it is interwoven to form a braided shell, which is tightly wrapped around the solid elastic cylindrical part to form a sensing tension rope.