TWI753716B - Method and system for collecting exercise data - Google Patents

Abstract

The disclosure provides a method and system for collecting exercise data. The method includes: detecting a load weight of the weight training equipment through a first distance sensor installed on the weight training equipment, wherein the weight training equipment is equipped with a reference object; and detecting a movement of the reference object, and estimating the exercise data of the user of the weight training equipment based on the movement of the reference object and the load weight.

Description

Sports data collection method and system

The present invention relates to a motion measurement method and system, and more particularly, to a motion data collection method and system.

With the progress of the times, exercise and fitness has become a very important part of people's lives. Generally speaking, most of the traditional weight training equipment that people can come into contact with can only present the training weight used in the form of counterweights or bars, and cannot provide users with more scientific movements such as the number of movements and exercise power. data.

In addition, although there are other higher-level weight training equipment on the market (for example, using a power source (such as a motor, an air pump, etc.) as a resistance source, the number of training times and the resistance value during training can be obtained through user account management. equipment for sports data), but it is difficult to be accepted by the market due to its high cost.

Therefore, for those skilled in the art, how to design a mechanism capable of collecting user motion data at a lower cost is an important issue.

In view of this, the present invention provides a motion data collection method and system, which can be used to solve the above technical problems.

The present invention provides a sports data collection method, comprising: detecting a load weight of the weight training equipment through a first distance sensor installed on a weight training equipment, wherein a reference object is installed on the weight training equipment; detecting the reference object and a movement data of a user of the weight training equipment is estimated based on the movement situation of the reference object and the load weight.

The present invention provides a motion data collection system, including a first distance sensor and a processor. The first distance sensor is installed on the weight training equipment. The processor is coupled to the first distance sensor, and is configured to: detect a load weight of the weight training equipment through the first distance sensor, wherein the weight training equipment is installed with a reference object; detect a movement of the reference object situation, and estimate a movement data of a user of the weight training equipment based on the movement situation of the reference object and the load weight.

Please refer to FIG. 1 , which is a schematic diagram of a sports data collection system according to a first embodiment of the present invention. As shown in FIG. 1 , in the first embodiment, the athletic data collection system 100 may include a first distance sensor 102 and a processor 104 .

In different embodiments, the first distance sensor 102 may be any single sensor capable of detecting the distance between itself and objects within its detection range (or field of view (FOV)). Or a sensor array, such as an infrared distance sensor (such as a time of flight (ToF) sensor), an ultrasonic distance sensor, etc., but not limited thereto.

The processor 104 is coupled to the first distance sensor 102 and can be a general-purpose processor, a special-purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more Microprocessors, controllers, microcontrollers, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), any other kind incorporating a digital signal processor core integrated circuits, state machines, Advanced RISC Machine (ARM)-based processors, and the like.

In an embodiment of the present invention, the processor 104 may cooperate with the first distance sensor 102 to implement the motion data collection method proposed by the present invention, the details of which are described below.

Please refer to FIG. 2 , which is a flowchart of a method for collecting sports data according to an embodiment of the present invention. The method of this embodiment can be executed by the sports data collection system 100 in FIG. 1 , and the details of each step in FIG. 2 will be described below with the elements shown in FIG. 1 .

First, in step S210, the processor 104 can detect the load weight of the weight training equipment through the first distance sensor 102 installed on the weight training equipment.

In the first embodiment of the present invention, the above-mentioned weight training equipment is, for example, a training equipment with a plurality of stacked weight blocks, so that the user can choose the training weight from them, such as the common pulley pull-down machine, leg extension machine, etc. , but not limited to this.

Please refer to FIGS. 3A and 3B , wherein FIG. 3A is a schematic diagram of a weight block of the weight training apparatus according to the first embodiment of the present invention, and FIG. 3B is a side view of FIG. 3A .

In the first embodiment, the weight training apparatus 300 may have a plurality of weights 311 stacked, and each weight 311 may be provided with a latch hole 311a. In this way, the user of the weight training apparatus 300 can select the required training weight by inserting the reference object 312 (such as a latch) into one of the latch holes 311a of the weight block 311 It can be understood as the load weight of the weight training equipment 300 ), but not limited to this. Generally speaking, the lower the position of the weight block 311 where the reference object 312 is, the higher the corresponding training weight, and vice versa.

In the first embodiment, the latch holes 311a of each weight block 311 can be arranged along a straight line, and the first distance sensor 102 can be arranged on the straight line to detect the first distance sensor 102 and the reference object distance between 312. As shown in FIG. 3A and FIG. 3B , the latch holes 311a of each counterweight 311 can be understood as being arranged along a straight line perpendicular to the ground, and the first distance sensor 102 can be disposed directly under the reference object 312 and move toward the The distance between the first distance sensor 102 and the reference object 312 is detected above. That is, in FIG. 3A , the FOV 102 a of the first distance sensor 102 is detected toward the top of the first distance sensor 102 .

Please refer to FIG. 3C , which is a side view of another weight block according to the first embodiment of the present invention. In FIG. 3C , the shape of each weight block 311 is substantially the same as that shown in FIG. 3A , but the first distance sensor 102 in FIG. 3C can be attached to a certain part of the weight training equipment 300 It is disposed just above the reference object 312 and detects the distance between the first distance sensor 102 and the reference object 312 downward. That is, in FIG. 3C , the FOV 102a of the first distance sensor 102 is directed toward the bottom of the first distance sensor 102 for detection, but the present invention is not limited thereto.

In the first embodiment, the processor 104 can detect the initial distance between the first distance sensor 102 and the reference object 312 through the first distance sensor 102, and estimate the training weight ( That is, the load weight of the weight training apparatus 300). In order to facilitate the understanding of the concept of the present invention, the following will be supplemented with FIG. 4 for further description.

Please refer to FIG. 4 , which is a schematic diagram of a plurality of preset distance intervals according to the first embodiment of the present invention. In the first embodiment, when the reference object 312 is inserted on a certain counterweight 311, the distance measured by the first distance sensor 312 may not be a fixed value, but may vary with time a variable value.

Taking the waveform 411 of FIG. 4 as an example, it is, for example, measured by the first distance sensor 102 of FIG. 3C when the reference object 312 is placed on the counterweight 311 corresponding to 5 kg (ie, the load weight is 5 kg). The obtained distance variation value, that is, the FOV 102 a of the first distance sensor 102 is detected toward the bottom of the first distance sensor 102 . Taking the waveform 412 of FIG. 4 as an example, it is, for example, measured by the first distance sensor 102 of FIG. 3C when the reference object 312 is placed on the counterweight 311 corresponding to 10 kg (ie, the load weight is 10 kg). The obtained distance change value. Taking the waveform 413 of FIG. 4 as an example, it is, for example, measured by the first distance sensor 102 of FIG. 3C when the reference object 312 is placed on the counterweight 311 corresponding to 15kg (ie, the load weight is 15kg). The obtained distance change value. The meanings of the remaining waveforms in FIG. 4 can be inferred according to the above teachings, and will not be repeated here.

Based on the waveforms corresponding to different load weights in FIG. 4 , the processor 104 may accordingly determine a plurality of preset distance intervals corresponding to different load weights. For example, after obtaining the waveform 412 corresponding to the load weight of 10 kg, the processor 104 can estimate, for example, a distance mean value (represented by m1 ) and a distance standard deviation (represented by s1 ) corresponding to the waveform 412 , and use (m1− s1, m1+s1) as the preset distance interval 412a corresponding to the load weight of 10kg. For another example, after obtaining the waveform 413 corresponding to the load weight of 15 kg, the processor 104 can estimate, for example, a distance mean value (represented by m2) and a distance standard deviation (represented by s2) corresponding to the waveform 412, and use ( m2-s2, m2+s2) as the preset distance interval 413a corresponding to the load weight of 15kg. In addition, if 5kg is the lowest load weight, the processor 104 may define, for example, the distance intervals lower than the preset distance interval 412a as the preset distance interval 411a corresponding to the load weight of 5kg, but not limited thereto. The preset distance intervals corresponding to the remaining load weights should be inferred based on the above teachings, and will not be described in detail here.

Therefore, after the processor 104 obtains the initial distance between the first distance sensor 102 and the reference object 312, the processor 104 can determine which of the plurality of preset distance intervals the initial distance belongs to. Assuming that the processor 104 determines that the initial distance belongs to a specific distance interval among the plurality of preset distance intervals, the processor 104 can determine that the load weight of the weight training apparatus 300 is a specific weight corresponding to the specific distance interval.

For example, if the processor 104 determines that the initial distance belongs to the predetermined distance interval 411a, the processor 104 may determine that the load weight of the weight training equipment 300 is the load weight corresponding to the predetermined distance interval 411a (ie, 5kg). Assuming that the processor 104 determines that the initial distance belongs to the preset distance zone 412a, the processor 104 may determine that the load weight of the weight training equipment 300 is the load weight corresponding to the preset distance zone 412a (ie, 10kg). In addition, if the processor 104 determines that the initial distance belongs to the preset distance zone 413a, the processor 104 may determine that the load weight of the weight training equipment 300 is the load weight corresponding to the preset distance zone 413a (ie, 15kg). The corresponding relationship between the remaining initial distances and the load weight of the weight training equipment 300 should be inferred according to the above teachings, and will not be repeated here.

In another embodiment, if the first distance sensor 102 is configured as shown in FIGS. 3A and 3B (ie, the FOV 102a of the first distance sensor 102 is facing the first distance sensor 102 ) ), the distance and load weight in Figure 4 will be arranged in the opposite direction (not shown), or can be adjusted to the same representation as Figure 4 by means of numerical conversion. At this time, the smaller the detected value, the heavier the load is.

Next, in step S220, the processor 104 may detect the movement of the reference object 312, and estimate the movement data of the user of the weight training apparatus 300 based on the movement of the reference object 312 and the load weight. In different embodiments, the aforementioned exercise data may include the number of movements, exercise power, exercise time, rest time, etc., but may not be limited thereto.

In the first embodiment, the processor 104 can detect the movement of the reference object 312 through the first distance sensor 102 . Specifically, it can be understood that there is a first distance between the reference object 312 and the first distance sensor 102 (the minimum value of which can correspond to the above-mentioned initial distance), and the movement of the reference object 312 can be characterized as the first distance distance changes.

In the first embodiment, as the user operates the weight training apparatus 300 , the movement situation (ie, the first distance change situation) of the reference object 312 may be presented as a distance change graph 500 as shown in FIG. 5 . In FIG. 5A (and various embodiments below), the first distance sensor 102 is assumed to be detected in the manner of FIG. 3C . As shown in FIG. 5A , the distance variation graph 500 may include a peak-trough set 501 corresponding to an action group, which includes a plurality of peak-trough pairs 511-515, wherein each peak-trough pair 511-515 may include a continuous 1 trough and 1 peak.

For example, peak-trough pair 511 may include peak 511a and trough 511b, peak-trough pair 512 may include peak 512a and trough 512b, and peak-trough pair 513 may include peak 513a and trough 513b, but not limited thereto.

In one embodiment, the processor 104 may find a plurality of specific peak-trough pairs in the peak-trough pairs 511-515, and use the number of the specific peak-trough pairs as the number of actions of the action group.

In one embodiment, each specific peak-trough pair may include a specific peak and a specific trough, the first specific distance corresponding to one of the specific peak and the specific trough may be greater than the first distance threshold T1, and the other of the specific peak and the specific trough may be greater than the first distance threshold T1. The corresponding second specific distance may be smaller than the second distance threshold value T2, and the first distance threshold value T1 may be greater than the second distance threshold value T2.

In some embodiments, the first distance threshold value T1 and the second distance threshold value T2 may be determined according to the current initial distance. For example, assuming that the current initial distance is X, the first distance threshold value T1 can be set as X-X1, and the second distance threshold value T2 can be set as X-X2, where X2 can be greater than X1. In the situation shown in FIG. 5, the initial distance is approximately 920 mm. In this case, assuming that X1 and X2 are set to be 200 and 400, respectively, the first distance threshold value T1 and the second distance threshold value T2 as shown in FIG. 5A can be obtained accordingly. , but the present invention may not be limited to this.

Taking the peak-trough pair 511 as an example, since the distance corresponding to the peak 511a is greater than the first distance threshold T1, and the distance corresponding to the trough 511b is less than the second distance threshold T2, the processor 104 may assign the peak-trough pair 511 to the peak-trough pair 511. regarded as a specific peak-trough pair. In addition, taking the peak-trough pair 512 as an example again, since the distance corresponding to the peak 512a is greater than the first distance threshold T1, and the distance corresponding to the trough 512b is less than the second distance threshold T2, the processor 104 can convert the peak- A trough pair 512 is considered a specific peak-trough pair. Similarly, the peak-trough pairs 513~515 will also be individually regarded as a specific peak-trough pair.

In other words, in the scenario of FIG. 5 , there are altogether 5 specific peak-trough pairs (ie, peak-trough pairs 511 to 515 ). In this case, the processor 104 will determine that the number of actions performed by the user is 5 times.

From another point of view, when some peak-trough pairs are not determined as specific peak-trough pairs, it means that the user has not moved the selected weight 311 a sufficient distance (ie, the movement is incomplete). , so the processor 104 will not use these peak-to-valley pairs to accumulate the number of actions of the user, but the present invention is not limited to this.

In one embodiment, the processor 104 may also estimate the motion power (denoted by P) in the motion data based on the load weight (denoted by w), the total moving distance of the reference object 312 (denoted by D), and the travel time. In the context of FIG. 5 , the total travel distance of the reference object 312 is, for example, the sum of the individual distance values included in the waveform 599 . In addition, the movement time of the reference object 312 can be understood as the movement time of the user, and it can be represented as a time length TD1, for example. In this case, the processor 104 may first obtain the average moving speed (represented by v) by dividing the total moving distance (ie, D) by the time length TD1. Afterwards, the processor 104 can estimate the motion power according to the above data.

In some embodiments, the processor 104 may estimate the concentric motion power and the eccentric motion power of each action group according to the distance change map, which will be further described below with reference to FIG. 5B .

Please refer to FIG. 5B , which is a distance change graph and a corresponding speed change graph according to an embodiment of the present invention. In this embodiment, the distance change graph 500a may include one peak-to-valley set corresponding to the j-th action group performed by the user (hereinafter referred to as the j-th peak-to-valley set). According to the previous teachings, the jth peak-trough set shown in FIG. 5 can be understood as including 8 specific peak-trough pairs.

In one embodiment, after obtaining the distance change graph 500a of FIG. 5B , the processor 104 may, for example, differentiate the distance change graph 500a with respect to time to generate the velocity change graph 500b shown in the lower half of FIG. 5B , but not limited to this .

As shown in the lower half of FIG. 5B , the rate change graph 500b may include a plurality of time intervals D1 to D8 corresponding to a specific peak-valley pair of the jth peak-valley set, wherein the time intervals D1 to D8 may be individually dependent on The sequence includes a first specific time point, a second specific time point and a third specific time point. In the embodiment of the present invention, the rates corresponding to the first specific time point, the second specific time point, and the third specific time point may be zero.

Afterwards, the processor 104 may define the centripetal time period of the i-th time interval according to the first specific time point and the second specific time point of the i-th time interval of the time intervals D1-D8, where i is a positive integer ( For example, any of 1 to 8). Next, the processor 104 may define the centrifugation time period of the i-th time interval according to the second specific time point and the third specific time point of the i-th time interval.

Taking the time interval D1 as an example, it may include a first specific time point t1 , a second specific time point t2 and a third specific time point t3 in sequence, and the respective corresponding rates are zero. In one embodiment, the processor 104 may define the time interval between the first specific time point t1 and the second specific time point t2 as the centripetal time period of the time interval D1, and define the second specific time point t2 and the second specific time point t2 as the centripetal time period of the time interval D1. The time interval between the three specific time points t3 is defined as the centrifugation time period of the time interval D1, but it may not be limited thereto. In addition, the processor 104 can also determine the individual centripetal time periods and centrifugal time periods of the time intervals D2 to D8 based on the above teachings.

After obtaining the individual centripetal time periods and the centripetal time periods of the time intervals D1 to D8, the processor 104 may determine the centripetal motion power of the jth action group based on the individual centripetal time periods of the time intervals D1 to D8, and The centrifugal exercise power of the j-th action group is determined based on the individual centrifugal time periods of the time intervals D1 to D8.

表示），以及參考物體312在所述第j個動作組中的總向心位移量（以

表示）。之後，處理器104可依據「

」的式子估計所述第j個動作組的向心運動功率，其中m為負載重量，g為重力常數，但可不限於此。 In one embodiment, the processor 104 may determine the average centripetal movement rate of the reference object 312 in the j-th action group (in

), and the total centripetal displacement of the reference object 312 in the j-th action group (with

Express). Afterwards, the processor 104 may

” to estimate the centripetal motion power of the jth action group, where m is the load weight and g is the gravitational constant, but it is not limited to this.

表示），以及參考物體312在所述第j個動作組中的總離心位移量（以

表示）。之後，處理器104可依據「

」的式子估計所述第j個動作組的離心運動功率，但可不限於此。 In another embodiment, the processor 104 may determine the average centrifugal movement rate of the reference object 312 in the j-th action group (in

), and the total centrifugal displacement of the reference object 312 in the jth action group (with

Express). Afterwards, the processor 104 may

The formula for estimating the eccentric power of the jth action group, but not limited to this.

In some embodiments, the exercise data collection system 100 can provide the collected exercise data (number of movements, exercise power (eg, concentric exercise power/eccentric exercise power), exercise time, rest time) to other smart devices , so that the above-mentioned exercise data can be presented to the user of the weight training equipment 300 or other relevant personnel (such as coaches) for reference by the smart device, but it is not limited to this.

Please refer to FIG. 6 , which is a diagram illustrating distance variation corresponding to different load weights according to an embodiment of the present invention. In FIG. 6 , the waveforms shown are, for example, after the user selects a certain load weight (eg, 45kg, 50kg, 55kg, 60kg, 65kg, and 70kg), the first distance sensor 102 in FIG. 3C sets the reference object to the reference object. 312 is a distance graph of the measured first distance. As mentioned earlier, the processor 104 may estimate corresponding motion data, such as the number of actions, motion power, motion time, etc., based on the respective waveforms in FIG. 6 , but it is not limited thereto.

」的式子（下稱第一轉換式）描述，其中x是所述讀值，Y為第一實際距離，

為斜率，

為一常數。 In some embodiments, when the first distance sensor 102 is implemented as a ToF sensor, limited by the characteristics of the first distance sensor 102 itself, the read value detected by the first distance sensor 102 may not be able to be detected. Corresponds correctly to the actual distance between the first distance sensor 102 and the reference object 312 . Specifically, when the first distance between the reference object 312 and the first distance sensor 102 is within a certain first distance range, the first distance sensor 102 should be able to measure the above-mentioned first distance more accurately. a distance. That is, the reading value of the first distance sensor 102 can roughly match the actual first distance (hereinafter referred to as the first actual distance). In this case, the relationship between the read value and the first actual distance can be "

” (hereinafter referred to as the first conversion formula) description, where x is the reading value, Y is the first actual distance,

is the slope,

is a constant.

」的式子（下稱第二轉換式）描述，其中x是所述讀值，Y為第一實際距離，

為斜率（其大於

），

為一常數。 However, when the first distance between the reference object 312 and the first distance sensor 102 is within a certain second distance range, other distances will also appear in the FOV 102a of the first distance sensor 102 Obstructors (eg, the counterweight block 311 ), accordingly, the read value cannot correctly correspond to the first actual distance. Through experiments, the relationship between the reading value and the first actual distance in this case can be "

” (hereinafter referred to as the second conversion formula) description, where x is the reading value, Y is the first actual distance,

is the slope (which is greater than

),

is a constant.

Therefore, in one embodiment, when the processor 104 determines that the current reading value provided by the first distance sensor 102 is within the first distance range, the processor 104 can convert the current reading value into the first reading value according to the first conversion formula. an actual distance. On the other hand, when the processor 104 determines that the current reading value provided by the first distance sensor 102 is within the second distance range, the processor 104 can convert the current reading value into the first actual distance according to the second conversion formula.

Please refer to FIG. 7 , which is a schematic diagram of a plurality of distance ranges according to an embodiment of the present invention. In this embodiment, if it is found that the first distance sensor 102 cannot correctly correspond to the first actual distance after the reading value is greater than 1000mm after the experimental measurement, the processor 104 can set the range of 0mm to 1000mm as the first distance A distance range 710 , and a range greater than 1000 mm is defined as a second distance range 720 .

Afterwards, the processor 104 may generate the first conversion formula based on the relationship between the read value and the first actual distance within the first distance range 710 , and generate the first conversion formula based on the read value and the first actual distance within the second distance range 720 The relationship within yields the second transformation. In this way, the processor 104 can adaptively convert the current reading value into the first actual distance according to the first/second conversion formula according to the current reading value provided by the first distance sensor 102 .

In some embodiments, if the user begins to operate the weight training apparatus 300 after selecting a certain load weight, the reference object 312 will move up and down accordingly during the user's movement, while the first distance sensor 102 detects The resulting reading will vary accordingly. In FIG. 7 , it is assumed that the reading value measured by the first distance sensor 102 during the movement of the user varies within a variation range 730 (eg, 700 mm˜1400 mm). In this case, when the processor 104 determines that the current reading value provided by the first distance sensor 102 is between 700 mm and 1000 mm, the processor 104 can determine that the current reading value is within the first distance range 710 according to the first The conversion formula converts the current reading value into the first actual distance. On the other hand, when the processor 104 determines that the current reading value provided by the first distance sensor 102 is between 1000 mm and 1400 mm, the processor 104 may determine that the current reading value is within the second distance range 720 according to the second conversion The formula converts the current reading value into the first actual distance, but the present invention is not limited to this.

In some embodiments, when the user uses the weight training equipment 300 to perform multiple action groups, the processor 104 may estimate the number of movements corresponding to each action group and the rest between these action groups according to the corresponding distance change graph time.

Please refer to FIG. 8 , which is a diagram of distance variation corresponding to a plurality of action groups according to an embodiment of the present invention. As shown in FIG. 8 , the distance variation graph 800 includes about 23 specific peak-trough pairs in total, and the processor 104 can divide these specific peak-trough pairs into a plurality of peak-trough sets G1~ corresponding to 3 action groups G3.

In one embodiment, the processor 104 may, for example, estimate the time difference between two consecutive specific peak-trough pairs, and may divide these specific peak-trough pairs into peak-trough sets G1 to G3 accordingly. For example, when the processor 104 determines that the time difference between two consecutive specific peak-trough pairs is less than a rest time threshold value T3, the processor 104 can classify the two specific peak-trough pairs as belonging to the same peak - Valley collection. On the other hand, when the processor 104 determines that the time difference between two consecutive specific peak-trough pairs is greater than the rest time threshold value T3, the processor 104 may classify the two specific peak-trough pairs as belonging to different peak-trough pairs. Valley Collection.

For example, it can be seen from FIG. 8 that in each peak-trough set G1-G3, the time difference between two consecutive specific peak-trough pairs is not greater than the rest time threshold value T3. However, since the time difference T41 between the consecutive specific peak-trough pairs G1L and G21 is greater than the rest time threshold value T3, the processor 104 can classify the specific peak-trough pairs G1L and G21 as belonging to different peak-trough sets. Similarly, since the time difference T42 between the consecutive specific peak-trough pairs G2L and G31 is greater than the rest time threshold value T3, the processor 104 can classify the specific peak-trough pairs G2L and G31 as belonging to different peak-trough sets.

In addition, it is assumed that the peak-trough sets G1 and G2 correspond to the first action group and the second action group, respectively. Since the specific peak-trough pairs G1L and G21 belong to the peak-trough sets G1 and G2 corresponding to different action groups, respectively, The time difference T41 between a specific peak-trough pair G1L (ie, the last specific peak-trough pair of the peak-trough set G1) and G21 (ie, the first specific peak-trough pair of the peak-trough set G2) can be defined as Rest time between sets 1 and 2. Similarly, assuming that the peak-trough set G3 corresponds to the third action group, the specific peak-trough pair G2L (ie the last specific peak-trough pair of the peak-trough set G2) and G31 (ie the peak-trough set G3) The time difference T42 between the first specific peak-trough pair) may be defined as the rest time between sets between the second action group and the third action group, but may not be limited thereto.

Please refer to FIG. 9 , which is a schematic diagram of replacing the load weight according to an embodiment of the present invention. In this embodiment, when the load weight is selected as the lightest specific weight, the initial distance between the reference object 312 and the first distance sensor 102 can be referred to as the reference distance RD, and it can be used as whether the user switches Reference for load weight.

Specifically, the processor 104 can determine whether the first distance has maintained the first static waveform P1 for a first static time threshold value T5, wherein the distance corresponding to the first static waveform is higher than the reference distance RD. In FIG. 9, in response to determining that the first distance has maintained the first static waveform P1 for the first static time threshold value T5, the processor 104 can detect whether the first distance has changed to the second static waveform P2, wherein the second static waveform The distance corresponding to P2 is also higher than the reference distance RD.

If so, the processor 104 can further determine whether the first distance has maintained the second resting waveform P2 for the first resting time threshold value T5, and whether the time difference between the first resting waveform P1 and the second resting waveform P2 is less than the second resting time Threshold value T6. If so, it means that the user has switched the load weight to a specific weight corresponding to the second resting waveform P2. Therefore, in response to determining that the first distance has maintained the second static waveform P2 for the first static time threshold value T5, and the time difference between the first static waveform P1 and the second static waveform P2 is smaller than the second static time threshold value T6, processing The device 104 can update the load weight according to the second static waveform P2, and the details thereof can be referred to the related description of FIG. 4, and will not be repeated here.

Please refer to FIG. 10 , which is a schematic diagram of a sports data collection system according to a second embodiment of the present invention. As shown in FIG. 10 , in the second embodiment, the motion data collection system 1000 may include a first distance sensor 1001 , a second distance sensor 1002 and a processor 1004 , wherein the first distance sensor 1001 , the first distance sensor 1001 , the For the respective possible implementations of the two distance sensors 1002 and the processor 1004 , reference may be made to the relevant descriptions of the first distance sensor 102 and the processor 104 , and details are not described herein.

In the second embodiment, the exercise data collection system 1000 can also be used to execute each step in FIG. 2 , but the weight training equipment it applies to is slightly different from the first embodiment, so the corresponding operation details will also be slightly different . The following will be supplemented with FIG. 11 for further description.

Please refer to FIG. 11 , which is a partial schematic diagram of a weight training apparatus according to a second embodiment of the present invention. In the second embodiment, the considered weight training equipment 1100 is, for example, a Smith machine including a barbell 1101, and the first distance sensor 1001 may be an array of distance sensors including a plurality of distance sensing units The measuring device can be connected to the sleeve 1101a of the barbell 1101 through the connecting rod 1112 .

In the second embodiment, when the processor 1004 executes step S210, the processor 1004 can detect through the first distance sensor 1001 the individual thicknesses and the respective thicknesses of the plurality of bars 1121-1123 mounted on the sleeve 1101a. The reference distance between the bars 1121 - 1123 and the first distance sensor 1001 . Afterwards, the processor 1004 can estimate the corresponding weight of each of the bar pieces 1121 to 1123 based on the thickness of each of the bar pieces 1121 to 1123 and the reference distance.

In the second embodiment, the designer can, for example, place each bar piece on the sleeve 1101a in advance in a pre-operation, so that the first distance sensor 1001 can measure the thickness and reference distance of each bar piece, and then The thickness of each bar and the corresponding relationship between the reference distance and the weight of each bar are recorded. In this way, when the first distance sensor 1001 measures the thickness corresponding to a bar (which can be inferred from the number of distance sensing units that detect the bar) and the reference distance, it can be deduced accordingly. The weight of the bar, but the present invention may not be limited to this.

In the second embodiment, after the processor 1004 obtains the weights corresponding to the bars 1121 - 1123 according to the above teachings, the load weight of the weight training equipment 1100 can be estimated accordingly. Generally speaking, the sleeves on both sides of the barbell 1101 on the Smith machine should carry bar pieces of the same weight, so the processor 1004 can use twice the sum of the corresponding weights of the bar pieces 1121-1123 as the load weight of the weight training equipment 1100, But not limited to this.

In addition, in the second embodiment, the considered reference object 1111 may include the first distance sensor 1001 and the connecting rod 1112, and when the processor 1004 executes step S220, the The two distance sensors 1002 detect a specific movement situation of the first distance sensor 1001 as the movement situation of the reference object 1111 .

In the second embodiment, according to the characteristics of the Smith machine, the barbell 1101 will move along a fixed trajectory. Specifically, the barbell 1101 can be fixedly connected to the sliding sleeve 1102 , and the sliding sleeve 1102 can slide on the sliding rail 1199 . In this case, when the user operates the barbell 1101 , the barbell 1101 will drive the sliding sleeve 1102 to slide along the sliding rail 1199 , thereby making the barbell 1101 move along the fixed track 1131 .

In addition, in order to enable the second distance sensor 1002 to detect a specific movement situation of the first distance sensor 1001, the reference connection 1132 between the first distance sensor 1001 and the second distance sensor 1002 can be Designed to be parallel to the fixed track 1131.

In the second embodiment, the distance between the first distance sensor 1001 and the second distance sensor 1002 can be understood as the existence of the second distance, and the movement of the reference object 1111 can be characterized as the distance change of the second distance.

In this case, as the user operates the weight training equipment 1100, the movement of the reference object 1111 can also be presented as a distance change graph 500 as shown in FIG. 5A, and the processor 1004 estimates the user's motion data accordingly. Reference may be made to the relevant description in the first embodiment, which will not be repeated here.

In addition, the exercise data collection system 1000 can also provide the collected exercise data (number of movements, exercise power, exercise time, rest time) to other smart devices, so that the smart device can present the above-mentioned exercise data to the weight User of training equipment 1100 or other relevant personnel (eg, a coach) for reference, but may not be limited thereto.

To sum up, the exercise data collection method and system provided by the present invention can be easily installed on the corresponding weight training equipment, and then measure the exercise data when the user operates the weight training equipment based on the movement of the reference object. Also, since the motion data collection system only includes relatively low-cost components such as microcontrollers and distance sensors, the present invention can provide a solution for collecting user motion data at a relatively low cost.

Although the present invention has been disclosed above by the embodiments, it is not intended to limit the present invention. Anyone with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, The protection scope of the present invention shall be determined by the scope of the appended patent application.

100,1000: Exercise data collection system 102, 1001: First distance sensor 102a:FOV 1002: Second distance sensor 104, 1004: Processor 300,1100: Weight training equipment 311: Counterweight 311a: pin hole 312, 1111: Reference objects 411~413,599: Waveform 411a~413a: Preset distance range 500,500a,800: Distance change graph 500b: Rate change graph 501, G1, G2, G3: crest-trough set 511~515, G1L, G21, G2L, G31: peak-valley pair 511a~515a: crest 511b~515b: valley 710: First distance range 720: Second distance range 730: Range of change 1101: Barbell 1101a: Sleeve 1102: Sliding sleeve 1112: connecting rod 1121~1123: bar piece 1131: Fixed track 1132: Reference connection 1199: Slider S210, S220: Steps D1~D8: time interval T1: first distance threshold value T2: second distance threshold T3: Rest time threshold T41, T42: time difference T5: Threshold value of the first static time T6: second static time threshold value t1: The first specific time point t2: The second specific time point t3: The third specific time point TD1: time length P1: The first waveform P2: Second waveform RD: reference distance

FIG. 1 is a schematic diagram of a sports data collection system according to a first embodiment of the present invention. FIG. 2 is a flowchart of a method for collecting sports data according to an embodiment of the present invention. 3A is a schematic diagram of a weight block of the weight training equipment according to the first embodiment of the present invention. Figure 3B is a side view of Figure 3A. 3C is a side view of another weight block according to the first embodiment of the present invention. FIG. 4 is a schematic diagram of a plurality of preset distance intervals according to the first embodiment of the present invention. FIG. 5A is a distance change diagram according to an embodiment of the present invention. FIG. 5B is a distance change graph and a corresponding velocity change graph according to an embodiment of the present invention. FIG. 6 is a diagram illustrating distance variation corresponding to different load weights according to an embodiment of the present invention. FIG. 7 is a schematic diagram of a plurality of distance ranges according to an embodiment of the present invention. FIG. 8 is a diagram illustrating distance changes corresponding to a plurality of action groups according to an embodiment of the present invention. FIG. 9 is a schematic diagram of replacing the load weight according to an embodiment of the present invention. FIG. 10 is a schematic diagram of a sports data collection system according to a second embodiment of the present invention. 11 is a partial schematic diagram of a weight training apparatus according to a second embodiment of the present invention.

S210, S220: Steps

Claims (18)

A method of athletic data collection comprising: Detecting a load weight of the weight training equipment through a first distance sensor mounted on the weight training equipment, wherein the weight training equipment is mounted with a reference object; and A movement situation of the reference object is detected, and a movement data of a user of the weight training equipment is estimated based on the movement situation of the reference object and the load weight. The method of claim 1, wherein the step of detecting the load weight of the weight training equipment comprises: detecting an initial distance between the first distance sensor and the reference object; and In response to determining that the initial distance belongs to a specific distance interval among a plurality of preset distance intervals, it is determined that the load weight of the weight training equipment is a specific weight corresponding to the specific distance interval. The method of claim 1, wherein the weight training equipment comprises a plurality of stacked weights and a latch, each of the weights has a latch hole, and the latch is configured to be inserted into one of the weights the pin hole, and the reference object includes the pin. The method of claim 1, wherein the step of detecting the movement of the reference object comprises: The movement of the reference object is detected through the first distance sensor. The method of claim 4, wherein a first distance exists between the reference object and the first distance sensor, and the movement of the reference object is characterized by a distance change of the first distance. The method according to claim 5, further comprising: In response to determining that a current reading value of the first distance is within a first distance range, converting the current reading value into a first actual distance according to a first conversion formula; and In response to determining that the current reading value of the first distance is within a second distance range, converting the current reading value into the first actual distance according to a second conversion formula, wherein the first conversion formula and the second conversion formula The formulas correspond to different slopes. The method of claim 1, wherein the weight training equipment is a Smith machine including a barbell, the first distance sensor is an array of distance sensors and is connected to a set of the barbell through a connecting rod tube, and the step of detecting the load weight of the weight training equipment includes: detecting, through the first distance sensor, a thickness of each of the plurality of bars mounted on the sleeve and a reference distance between each bar and the first distance sensor; and Based on the thickness of each of the bar pieces and the reference distance, a weight corresponding to each of the bar pieces is estimated, and the load weight of the weight training equipment is estimated accordingly. The method of claim 7, wherein the load weight of the weight training equipment is 2 times the sum of the weights corresponding to each of the bar pieces. The method of claim 7, wherein the reference object includes the first distance sensor and the connecting rod, and the step of detecting the movement of the reference object comprises: A specific movement situation of the first distance sensor is detected as the movement situation of the reference object through a second distance sensor installed on the weight training equipment. The method of claim 9, wherein a second distance exists between the first distance sensor and the second distance sensor, and the movement of the reference object is characterized by a distance of the second distance changing situation. The method of claim 7, wherein the barbell moves along a fixed trajectory, and a reference line between the first distance sensor and the second distance sensor is parallel to the fixed trajectory. The method of claim 1, wherein the motion data includes a number of motions, and the movement of the reference object is represented by a distance change graph, the distance change graph including a plurality of peak-to-valley pairs, and based on the movement of the reference object The step of estimating the movement data of the user of the weight training equipment by the movement situation and the load weight includes: dividing the peak-trough pairs into a plurality of peak-trough sets corresponding to a plurality of action groups; Find a plurality of specific peak-trough pairs in the jth peak-trough set of the peak-trough sets, wherein each specific peak-trough pair includes a specific peak and a specific trough, the specific peak and the specific peak-trough A first specific distance corresponding to one of the troughs is greater than a first distance threshold, a second specific distance corresponding to the other of the specific peak and the specific trough is smaller than a second distance threshold, and the first distance threshold greater than the second distance threshold; and Taking a number of the specific peak-trough pairs in the jth peak-trough set as the number of movements of the jth movement group in the movement groups. The method of claim 12, wherein the load weight corresponds to an initial distance, the first distance threshold value is separated from the initial distance by a first difference, and the second distance threshold value is separated from the initial distance by a distance second difference. The method according to claim 12, further comprising: Converting the distance variation graph into a velocity variation graph, wherein the velocity variation graph includes a plurality of time intervals corresponding to the specific peak-valley pairs of the jth peak-valley set, and each of the time intervals is in sequence Including a first specific time point, a second specific time point and a third specific time point, the rate corresponding to the first specific time point, the second specific time point and the third specific time point is 0; Define a concentric time period of the i-th time interval according to the first specific time point and the second specific time point of the i-th time interval of the time intervals, wherein i is a positive integer; defining a centrifugation time period of the i-th time interval according to the second specific time point and the third specific time point of the i-th time interval; Determine the concentric motion power of the jth action group based on the concentric time period of each time interval; An eccentric exercise power of the j-th action group is determined based on the centrifugal time period of each of the time intervals. The method of claim 12, wherein the peak-trough sets further include a (j-1)th peak-trough set, the action groups further include a (j-1)th action group, and the jth The time difference between the first specific peak-trough pair in the peak-trough set and the last specific peak-trough pair in the (j-1)th peak-trough set is the jth action group The rest time between a group and the (j-1)th action group, and the rest time between groups is greater than a rest time threshold. The method of claim 1, wherein there is a first distance between the reference object and the first distance sensor, the load weight is one of a plurality of specific weights, and one of the specific weights The lightest specific weight corresponds to a reference distance, and the method further includes: In response to determining that the first distance has maintained a first static waveform for a first static time threshold, detecting whether the first distance has changed into a second static waveform, wherein the first static waveform and the second static waveform Individual corresponding distances are higher than the reference distance; In response to determining that the first distance has maintained the second static waveform up to the first static time threshold, and the time difference between the first static waveform and the second static waveform is less than a second static time threshold, according to the A second resting waveform updates the load weight, wherein the second resting time threshold is higher than the first resting time threshold. The method of claim 1, wherein the motion data includes a motion power, and the motion power is obtained based on the load weight, a total moving distance and a moving time of the reference object. An athletic data collection system comprising: a first distance sensor mounted on a weight training equipment; a processor coupled to the first distance sensor and configured to: Detecting a load weight of the weight training equipment through the first distance sensor, wherein the weight training equipment is mounted with a reference object; and A movement of the reference object is detected, and a movement data of a user of the weight training equipment is estimated based on the movement of the reference object and the load weight.

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