TWI828589B - Weightlifting assistance system with body balance and safety monitoring is provided - Google Patents

Abstract

A weightlifting assistance system with body balance and safety monitoring is provided. The assistance system is designed for an individual user account and comprises a static detection unit, a dynamic detection unit, and a weightlifting monitoring unit. The static detection unit performs static detection to obtain static improper bending data and trunk-limb ratio data. The dynamic detection unit performs dynamic detection to obtain dynamic balance data. The weightlifting monitoring unit provides weightlifting assessment and real-time monitoring during weightlifting exercises. The weightlifting assessment adjusts the initial weightlifting load for the athlete based on the basic physical fitness data and trunk-limb ratio data. Furthermore, it adjusts the weight ratio between the left and right sides during weightlifting based on the static improper bending data and dynamic balance data. This system assists athletes in safely performing weightlifting exercises while maintaining body balance.

Description

Retraining assistance system with limb balance and safety monitoring

The present invention relates to a retraining auxiliary system, in particular to a retraining auxiliary system with limb balance and safety monitoring.

According to the press, heavy training is a high-intensity physical exercise method. However, there are often some shortcomings when implementing heavy training, and these shortcomings may have a negative impact on the effectiveness and safety of athletes. First, physical imbalances during heavy training may occur due to physical condition factors, such as incorrect or unstable postures. This imbalance may increase the risk of injury and reduce the effectiveness of training.

For example, performing complex heavy training movements such as lifting weights or squatting when the body is tilted or the center of gravity is unstable may cause abnormal loading of muscles or joints, increasing the likelihood of injury. For example, some people may have problems such as inconsistent lengths of hands and feet or scoliosis. These factors will affect the balance and stability of the body. This results in the body's imbalance and instability becoming more obvious during heavy training, increasing the risk of injury. If heavy training is carried out without the guidance of a professional coach, the risk of sports injuries is higher. In summary, heavy training is generally There are physical imbalances, instability, body structure problems, and lack of professional guidance during training. These shortcomings may cause athletes to easily feel tired and increase the risk of injury during heavy training. Therefore, how to improve heavy training through technology The deficiencies that existed at the time are still technical problems that this creation aims to improve.

In view of this, the inventor has many years of experience in the manufacturing, development and design of related products. After detailed design and careful evaluation in view of the above goals, he finally arrived at a truly practical invention.

The technical problem to be solved by the present invention is to provide a retraining assistance system with limb balance and safety monitoring in view of the above-mentioned deficiencies in the existing technology.

The athlete creates a personal information account in an auxiliary system, and the personal information account records a basic physical fitness data, and the auxiliary system executes a static detection unit, a dynamic detection unit and a retraining monitoring unit for the personal information account; the static detection unit Static detection is performed according to the training cycle. The static detection is equipped with at least one photography equipment in front and side of the athlete to obtain images, and then the backbone analysis technology analyzes the images to obtain a static improper bending data and a trunk limb proportion data; The dynamic detection unit performs dynamic detection according to the training cycle. The dynamic detection unit is equipped with the photography equipment in front and side of the athlete to obtain the image. A gyroscope is installed on each shoulder of the athlete. When performing dynamic detection, The backbone analysis technology analyzes the three-dimensional coordinate changes of the image frame and the above-mentioned gyroscope, and analyzes and obtains a dynamic balance data; and the retraining monitoring unit provides retraining evaluation and real-time monitoring of the execution when the athletes perform retraining, and the retraining evaluation is based on the The basic physical fitness data and the trunk and limb proportion data adjust the athlete's initial retraining weight, and adjust the weight ratio of the left and right sides during retraining based on the static improper bending data and the dynamic balance data. Real-time monitoring is installed on both the front and side of the athlete. The photography equipment is provided to obtain images, analyze the images to determine whether the improper bending of the athlete's body is compensated through weight ratio, and the gyroscope is installed on the athlete's shoulders to monitor the athlete's heavy training status. This helps athletes safely perform retraining exercises while keeping their limbs balanced.

The static test includes performing a standing posture test and a sitting posture test. The standing posture test is that the athlete stands with his feet shoulder-width apart and his hands droop naturally. The sitting posture test is that the athlete can sit with his knees bent vertically and his feet touching each other. Sit on a chair on the floor and keep your upper body upright.

The dynamic detection includes performing an overhead squat detection and a running detection. The overhead squat detection is when the athlete raises his arms and moves from a standing posture to a squatting posture. The running detection is when the athlete is walking fast, jogging or jogging on the treadmill. Run at least one of the tests.

Among them, the heavy training items assisted by the auxiliary system include a back-bar squat item and a barbell shoulder press item. The back-bar squat item is a back-and-forth squatting action with both legs while pressing a weight-lifting bar on the back shoulder. The barbell shoulder press item The pushing event is to hold the weightlifting bar flat with both hands and raise the hands in a reciprocating motion, and the weight training monitoring unit performs the weight training evaluation by adjusting the total weight of the weightlifting bar and the weight ratio of both ends of the weightlifting bar.

The gyroscope can be installed at both ends of the weightlifting bar to detect the balance of the two ends of the weightlifting bar when the athlete performs the back-bar squat event and the barbell shoulder press event, and performs the barbell shoulder press event with The speed at which the value of the gyroscope changes determines the degree of shaking of the hands.

Among them, the gyroscope can be installed on the athlete's knees. The numerical difference between the two gyroscopes located at the knees analyzes the balance of the legs, and the numerical change speed of the two gyroscopes can be used to determine the balance of the legs. The degree of jitter.

The heavy training monitoring unit integrates the data of each athlete's static detection, dynamic detection and execution of heavy training items to create a heavy training process chart. The heavy training process chart displays the static improper bending data, the dynamic balance data and the heavy training data. result.

The auxiliary system uses network communication to connect a cloud database and an AI personal secretary. The cloud database collects the training process charts of all athletes and provides relevant training records through big data methods. The AI personal secretary Use deep learning methods to analyze the above-mentioned retraining process chart to adjust the retraining plan suitable for each athlete. The retraining plan includes retraining intensity, retraining time, diet control and weight management.

The auxiliary system has a built-in voice communication unit. The voice communication unit executes the voice dialogue function between the athlete and the AI personal secretary. The AI personal secretary uses natural language processing methods to convert the athlete's voice into a format that can be processed, and then the AI personal secretary converts the athlete's voice into a format that can be processed by the AI personal secretary. The results of the AI personal secretary's calculations provide voice explanations to the athletes. The AI personal secretary can answer questions related to weight training and weight management through the AI personal secretary, and the AI personal secretary can guide how to implement the weight training plan.

Among them, a face recognition image is created in the personal data account of the auxiliary system, and athletes can quickly log in to the auxiliary system through image shooting and face recognition methods, and the training records in the heavy training monitoring unit can be updated in real time.

The main purpose of the present invention is that the auxiliary system adjusts the athlete's initial retraining weight based on the basic physical fitness data and the trunk and limb proportion data, and adjusts the left and right sides during retraining based on the static improper bending data and the dynamic balance data. Weight ratio, and real-time monitoring and analysis of the image to determine whether to compensate for the athlete's improper body curvature through weight ratio, thereby improving the athlete's body stability during heavy training, and then installing the gyroscope on the athlete's shoulders, thereby It assists athletes to safely perform heavy training exercises with their limbs balanced, thereby improving the athlete's durability. At the same time, it balances both sides of the body to make heavy training movements more stable and prevent sports injuries.

Other objects, advantages, and novel features of the present invention will become more apparent from the following detailed description and associated drawings.

In order to enable your review committee to have a further understanding and understanding of the purpose, characteristics and effects of the present invention, please cooperate with the following (simple explanation of the drawings) to elaborate as follows:

First, please view from Figure 1, Figure 2 and Figure 3, a heavy training auxiliary system with limb balance and safety monitoring. Athletes create a personal data account 101 in an auxiliary system 10. The personal data account 101 records a Basic physical fitness data 102. The basic physical fitness data 102 includes basic physical fitness such as gender, age, height, weight, etc. The auxiliary system 10 can be constructed in a computer or a network server, and can be used on a stand-alone or network basis. It is used in the line connection mode, and the auxiliary system 10 executes a static detection unit 11, a dynamic detection unit 12 and a retraining monitoring unit 13 for the personal data account 101, wherein:

As shown in Figures 4 and 5, the static detection unit 11 performs static detection according to the training cycle. The static detection is equipped with at least one photography device 14 in front and side of the athlete to obtain images, which are then analyzed by backbone analysis technology. The image screen obtains a static improper bending data 111 and a trunk limb proportion data 112. To further explain, the static detection includes performing a standing posture detection 113 and a sitting posture detection 114. The standing posture detection 113 is that the athlete's feet are the same width as the shoulders. Stand with your hands hanging down naturally, and use image backbone technology to analyze the front-view and side-view shots to analyze the relative positions of the head, back, hips, knees, and ankles, and determine whether the athlete's stance is improperly bent. For example, front-view images can reveal scoliosis, long and short hands, long and short feet, and other improper curvatures. For example, side-view images can reveal improper curvatures such as forward tilt of the spine and forward shoulder and neck. In addition, the static posture is inappropriate. The curvature data 111 can be weighted and calculated in a digital way to obtain the balance ratio between the left and right sides of the body. That is, when an athlete analyzes and measures 40% of the left side's bearing capacity and 60% of the right side's bearing capacity due to scoliosis, If the same weight is applied to both sides for heavy training, the athlete's balance will be easily affected due to the smaller strength of the left side. Not only will the body tend to lean to one side, but it is also more likely to cause sports injuries. Therefore, further test the athlete before performing heavy training. The physical condition of the athlete is necessary. In addition, considering that the athlete's hip joint is blocked by clothing and it is not easy to obtain its position through image analysis, the static detection unit 11 can additionally perform the sitting posture detection 114. The sitting posture detection 114 is when the athlete is sitting. You can sit on a chair with your knees bent vertically and your feet on the ground, and keep your upper body upright. This can improve the accuracy of the backbone analysis technology through the vertical bending of your legs, and effectively distinguish the intersection of the trunk and thighs. Determine the position of the hip joint, and construct the position and length of each bone in conjunction with the stance detection 113 to accurately analyze whether the upper and lower limbs are different in length, or to determine whether there is spinal curvature.

As shown in Figure 6 again, the dynamic detection unit 12 performs dynamic detection according to the training cycle. The dynamic detection unit is equipped with the photography equipment 14 in front and on the side of the athlete to obtain images, and then at each shoulder position of the athlete. A gyroscope 15 is installed. When performing dynamic detection, the backbone analysis technology is used to analyze the three-dimensional coordinate changes of the image and the gyroscope 15 to obtain a dynamic balance data 121. To further explain, the dynamic detection includes performing an overhead squat detection. 122 and a running test 123. The overhead squat test 122 is when the athlete raises his arms high and moves from a standing position to a squatting position. Whether the angle of raising the left and right arms is the same and whether the raising angle is tilted can be assisted. Determine the athlete's control over the arms and movement habits. When squatting with the arms raised high, the changes in the angles of the hips, knees, ankles and arm raising angles from standing to squatting can be analyzed to obtain When performing the overhead squat test 122 on the dynamic stability, when it is judged that the stability of the athlete's left side is weaker than the right side, the gap ratio between the balance states of the left and right sides will be moderately reduced, and the stability analysis of the left and right sides can also be further It is divided into the upper body and the lower body. On the other hand, the running detection 123 is at least one detection content of the athlete walking fast, jogging or running fast on the treadmill. The running detection 123 can be performed through the photography device 14 and the gyroscope 15 Integrated analysis, analyzing the swing state of the shoulders and knees during running, to understand the stable state of the body when traveling at a constant speed, and the dynamic balance data 121 can be weighted and calculated in a digital manner, thereby obtaining the left side of the body , the gap ratio of the right side's stability, that is, when the athlete's left hand stability is 55% and the right hand's stability is 45% due to force application skills or habit factors, the left hand's weight will be appropriately increased, and when judging the left and right hands' stability, the When the smoothness difference is too large, the initial retraining weight can be further reduced, thereby improving the safety of retraining.

As shown in Figures 7 and 8, the retraining monitoring unit 13 provides retraining evaluation and real-time monitoring when the athlete performs retraining. The retraining evaluation is based on the basic physical fitness data 102 and the trunk limb proportion data 112 to adjust the athlete. The initial retraining weight, and the weight ratio of the left and right sides during retraining is adjusted based on the static improper bending data 111 and the dynamic balance data 121. The real-time monitoring is to install the photography equipment 14 in front and side of the athlete. Obtain the image, analyze the image to determine whether the improper curvature of the athlete's body is compensated through the weight ratio, thereby improving the athlete's body stability during heavy training, and then installing the gyroscope 15 on the athlete's shoulders, thereby During the retraining process, it is determined whether to compensate for the dynamic balance data 121, thereby assisting athletes to safely perform retraining exercises in a balanced state of limbs, wherein retraining evaluation and real-time monitoring are applied to the photography equipment 14 and the gyroscope 15 The method is equivalent to dynamic detection.

As shown in Figure 9, the heavy training monitoring unit 13 integrates the data of each static test, dynamic test and execution of the heavy training items of the athlete to create a heavy training process chart 131. The heavy training process chart 131 displays the static improper bending data. 111. The dynamic balance data 121 and the retraining results, through the retraining process chart 131, allow athletes to intuitively understand their own progress speed, and appropriately adjust their own retraining plan, and the retraining process chart 131 can better Provide application for others to learn to adopt better initial training weight and planning training plan. Among them, a face recognition image resource 103 is established in the personal data account 101 of the auxiliary system 10, which quickly allows athletes to log in to the auxiliary system 10 through image shooting and face recognition methods, and updates the retraining monitoring unit 13 in real time. The training records in the device can be used by multiple people at the same time and improve the convenience of use.

As shown in Figures 7 and 8, the assistive system 10 performs auxiliary heavy training projects including a back-bar squat project 132 and a barbell shoulder press project 133. The back-bar squat project 132 is performed by pressing and holding on the back shoulder. A weightlifting bar 16 and a reciprocating double-leg squat action. The barbell shoulder press event 133 is to hold the weightlifting bar 16 with both hands flat and reciprocate the action of raising both hands. When the athlete performs heavy training, the personal information is established in the auxiliary system 10 Account 101, and input the basic physical fitness data 102 and create the face recognition map 103. Next, the static detection unit 11 performs static detection to obtain the static improper bending data 111 and the trunk limb proportion data 112, and then dynamically The detection unit 12 performs dynamic detection to obtain the dynamic balance data 121, thereby using the auxiliary system 10 to analyze the athlete's physical condition and improper body bending, and after comprehensive evaluation, instruct the athlete's suitable heavy training intensity, that is, before selecting the weightlifting bar The initial weight of 16 prevents athletes from choosing a heavy training intensity that exceeds their endurance when performing the back bar squat event 132 and the barbell shoulder press event 133. Among them, static detection and dynamic detection can be performed periodically, such as a Once a month, quarterly or half a year, you can gradually adjust the appropriate training weight and weight training plan when the weight training results are produced. In addition, the retraining monitoring unit 13 performs retraining evaluation by adjusting the total weight of the weightlifting bar 16 and the weight ratio of both ends of the weightlifting bar 16, which mainly analyzes the weightlifting through the static improper bending data 111 and the dynamic balance data 121. How much weight should be attached to bar 16? This can reduce the burden on the weaker side of the body, thereby improving the athlete's durability. At the same time, balancing both sides of the body can make heavy training movements more stable and prevent sports injuries.

In another embodiment of the present invention, as shown in Figures 7 to 9, the gyroscope 15 can be installed at the athlete's knees, and the numerical difference between the two gyroscopes 15 located at the knees can be used to analyze both legs. The balance of the athlete's left and right legs is determined by the numerical change speed of the two gyroscopes 15. When the numerical difference between the athlete's left and right legs is too large, the auxiliary system 10 will remind the athlete to pay attention and record it in the heavy training process. In Figure 131, the athlete can correct his posture or change the heavy training plan afterwards. When it is known that the legs are shaking too much, the athlete may be too tired. The auxiliary system 10 can remind the athlete to stop training and take a rest, so as to effectively improve his performance. The safety of retraining. In another embodiment of the present invention, the gyroscope 15 can be installed at both ends of the weightlifting bar 16 to detect the movement of the two ends of the weightlifting bar 16 when the athlete performs the back-bar squat event 132 and the barbell shoulder press event 133. Balance, and in the barbell shoulder press event 133, the shaking degree of the hands is known by the numerical change speed of the gyroscope 15. Among them, the gyroscope 15 installed at both ends of the weightlifting bar 16 can replace the one installed on the athlete. The gyroscope 15 on the shoulders can effectively analyze the stability of the athlete during heavy training, and can also reduce the inconvenience of the athlete wearing the gyroscope 15. In addition, it can also be used to detect the shaking of the hands. In the same way, when it is learned that the hands are shaking too much, the assistance system 10 reminds the athlete to stop training and take a rest, so as to effectively improve the safety of heavy training.

As shown in Figures 2 and 9, the auxiliary system 10 is connected to a cloud database 17 and an AI personal secretary 18 through network communication. The cloud database 17 collects the retraining process charts 131 of all athletes, and Relevant training records are searched through big data methods, and the AI personal secretary 18 analyzes the above-mentioned retraining process chart 131 using deep learning methods to adjust a retraining plan suitable for each athlete. The retraining plan includes retraining intensity, weight training time, diet control and weight management. The auxiliary system 10 is built with a voice communication unit 104. The voice communication unit 104 executes the voice dialogue function between the athlete and the AI personal secretary 18. The AI personal secretary 18 uses natural language processing methods to convert the athlete's voice into a format that can be processed. , and then use the AI personal secretary 18 to provide voice explanations to the athletes based on the calculation results, so that the AI personal secretary 18 can answer questions related to weight training and weight management, and the AI personal secretary 18 can guide how to implement the weight training plan.

The above is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention; that is, all equivalent changes and modifications made according to the patent scope of the present invention should still belong to the present invention. within the scope covered by the patent.

[Invention] 10: Auxiliary system 101:Personal information account 102: Basic physical fitness data 103: Face recognition data 104: Voice communication unit 11:Static detection unit 111: Static improper bending data 112: Trunk limb proportion data 113:Standing posture detection 114: Sitting posture detection 12:Dynamic detection unit 121: Dynamic balance data 122: Overhead squat detection 123:Running detection 13:Retrain the monitoring unit 131:Retraining process chart 132: Back Bar Squat Event 133:Barbell shoulder press event 14: Photography equipment 15: Gyroscope 16: Weightlifting bar 17:Cloud database 18:AI personal secretary

Figure 1 is a block diagram of the operation flow of the present invention. Figure 2 is a schematic diagram of the equipment of the present invention. Figure 3 is a schematic diagram of the module of the auxiliary system of the present invention. Figure 4 is a schematic diagram (1) of static detection according to the present invention. Figure 5 is a schematic diagram (2) of static detection according to the present invention. Figure 6 is a schematic diagram of dynamic detection according to the present invention. Figure 7 is a schematic diagram of the back-bar squat project of the present invention. Figure 8 is a schematic diagram of the barbell shoulder press event according to the present invention. Figure 9 is a schematic diagram of the retraining process chart of the present invention.

10: Auxiliary system

101:Personal information account

102: Basic physical fitness data

103: Face recognition data

104: Voice communication unit

14: Photography equipment

15: Gyroscope

16: Weightlifting bar

17:Cloud database

18:AI personal secretary

Claims (8)

A heavy training auxiliary system with limb balance and safety monitoring, including: athletes create a personal data account in an auxiliary system, the personal data account records a basic physical fitness data, and the auxiliary system performs a static detection on the personal data account unit, a dynamic detection unit and a retraining monitoring unit; the static detection unit performs static detection according to the training cycle, and the static detection is equipped with at least one photography equipment in front and side of the athlete to obtain images, which are then analyzed by backbone analysis technology The image frame obtains a static improper bending data and a trunk limb proportion data; the dynamic detection unit performs dynamic detection according to the training cycle. The dynamic detection unit is equipped with the photography equipment in front and side of the athlete to obtain the image frame, and then performs dynamic detection on the athlete's body. A gyroscope is installed at each shoulder position. During dynamic detection, backbone analysis technology is used to analyze the three-dimensional coordinate changes of the image and the gyroscope to obtain a dynamic balance data; and the retraining monitoring unit is used for retraining the athletes. Provides retraining evaluation and real-time monitoring of execution. The retraining evaluation is to adjust the athlete's initial retraining weight based on the basic physical fitness data and the trunk limb proportion data, and adjust the retraining time based on the static improper bending data and the dynamic balance data. The weight ratio of the left and right sides is monitored in real time. The photography equipment is installed in front and side of the athlete to obtain the image. The image is analyzed to determine whether the weight ratio is used to compensate for the improper bending of the athlete's body, and then the athlete's shoulder position is The gyroscope is installed to monitor the athlete's retraining status, thereby assisting the athlete to safely perform retraining exercises while keeping the limbs balanced; The heavy training monitoring unit integrates the data of each athlete's static detection, dynamic detection and execution of heavy training items to create a heavy training process chart. The heavy training process chart displays the static improper bending data, the dynamic balance data and the heavy training results. , the auxiliary system is connected to a cloud database and an AI personal secretary through network communication. The cloud database collects the training process charts of all athletes and provides relevant training records through big data methods. The AI personal secretary Use deep learning methods to analyze the above-mentioned retraining process chart to adjust the retraining plan suitable for each athlete. The retraining plan includes retraining intensity, retraining time, diet control and weight management. The heavy training assistance system with limb balance and safety monitoring as described in claim 1, wherein the static detection includes performing a standing posture detection and a sitting posture detection, and the standing posture detection is that the athlete stands with his feet and shoulder width apart and his legs The hand is naturally drooped. The sitting posture test is for the athlete to sit on a chair with the knees bent vertically and the feet touching the ground, while keeping the upper body upright. The heavy training assistance system with limb balance and safety monitoring as described in claim 1, wherein the dynamic detection includes performing an overhead squat detection and a running detection. The overhead squat detection is when the athlete raises his arms high and moves from a standing position. To the squatting position, the running test is at least one test of the athlete performing fast walking, jogging or fast running on the treadmill. The heavy training auxiliary system with limb balance and safety monitoring as described in claim 1, wherein the auxiliary system performs auxiliary heavy training projects including a back bar squat project and a barbell shoulder press project, and the back bar squat project It is an action of holding a weightlifting bar with the back shoulder and reciprocating the action of squatting with both legs. The barbell shoulder press event is an action of holding the weightlifting bar flat with both hands and reciprocating with both hands. In addition, the retraining monitoring unit performs retraining evaluation by adjusting the ratio of the total weight of the weightlifting bar to the weight ratio of both ends of the weightlifting bar. As described in claim 4, the weight training assistance system with limb balance and safety monitoring, wherein the gyroscope can be installed at both ends of the weight lifting bar, when the athlete performs the back bar squat event and the barbell shoulder press During the event, the balance of both ends of the weightlifting bar is detected, and the shaking degree of the hands is known based on the numerical change speed of the gyroscope during the barbell shoulder press event. As claimed in claim 4, the heavy training auxiliary system with limb balance and safety monitoring, wherein the gyroscope can be installed at the athlete's knees, and the numerical difference between the two gyroscopes located at the knees is analyzed. The balance of the legs, and the shaking degree of the legs can be known from the numerical change speed of the two gyroscopes. The retraining auxiliary system with body balance and safety monitoring as described in claim 1, wherein the auxiliary system has a built-in voice communication unit, and the voice communication unit performs the voice dialogue function between the athlete and the AI personal secretary, and the AI personal secretary The secretary uses natural language processing methods to convert the athlete's voice into a format that can be processed, and then uses the AI personal secretary's calculation results to provide voice explanations to the athlete. The AI personal secretary can then answer questions related to weight training and weight management through the AI personal secretary, and The AI personal secretary will guide you on how to implement the retraining plan. A retraining auxiliary system with body balance and safety monitoring as described in request item 1, wherein a face recognition image resource is created in the personal data account of the auxiliary system, and the image capturing method and the face recognition method are used to quickly Athletes log in to the auxiliary system and instantly update the training records in the heavy training monitoring unit.

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