ASSEMBLY, SYSTEM AND METHOD FOR IMPROVED TRAINING

Abstract

A training machine assembly comprises at least one control device and at least one training resistance. Each of the at least one training resistance comprises at least one training resistance value, such as a force applied towards the user contact element, e.g. a handle. The training resistance value can also comprise a function or a vector, for example a function linking a speed of movement of a user and/or a user contact element and a force applied against said movement. The control device can be a control device for controlling the training machine assembly. The training resistance can comprise an actuator. The actuator can comprise an electric motor. The training resistance can comprise a weight. The training resistance can comprise an element configured to provide a resistance against a movement of the user. The training machine assembly can comprise at least one camera.

Claims

1. A training machine assembly, comprising (i) at least one control device, and (ii) at least one training resistance, wherein each training resistance comprises at least one training resistance value, and wherein the at least one training resistance is automatically adjustable and the training machine assembly is configured to adjust the at least one training resistance.

2. The training machine assembly according to claim 1, wherein the training machine assembly is configured to generate heart-rate data of a user.

3. The training machine assembly according to claim 2, wherein the training machine assembly is configured to generate heart-rate data of user based on image data captured by at least one camera.

4. The training machine assembly according to claim 3, wherein the training machine assembly is configured to adjust the at least one training resistance based on the heart-rate of the user.

5. The training machine assembly according to claim 4, comprising at least one automatically adjustable user support element.

6. The training machine assembly according to claim 5, wherein the training machine assembly is configured to (a) generate skeleton data of a user based on image data captured by at least one camera; (b) compare the skeleton data to skeleton-reference data; and to (c) thus generate skeleton-comparison data.

7. The training machine assembly according to claim 6, wherein the training machine assembly is configured to (d) generate skeleton-trajectory data of a user based on image data captured by the at least one camera; and (e) compare the skeleton-trajectory data to skeleton-reference data and to thus generating trajectory-comparison data.

8. The training machine assembly according to claim 7, wherein the training machine assembly is configured to adjust at least one of the at least one automatically adjustable user support element based on at least one of the skeleton-comparison data and the trajectory-comparison data.

9. A system, comprising: (a) a plurality of training machine assemblies, each comprising at least one training resistance, wherein each training resistance comprises at least one training resistance value; (b) a communication network that is configured to enable data transmission between a plurality of the training machine assemblies of the system; and (c) a data processing system.

10. The system according to claim 9, with at least one of the training machine assemblies according to claim 1.

11. The system according to claim 9, wherein the system is configured to generate skeleton data of the at least one user using training machine assemblies of the system based on image data captured by at least one camera, and wherein the system is further configured to use at least one boundary condition corresponding to geometries of the respective training machine assemblies.

12. The system according to claim 11, wherein (c) at least one training machine assembly of the system comprises at least one automatically adjustable user support element; (d) the system is configured to automatically adjust at least one automatically adjustable user support element based on comparison data; (e) the system is configured to compare the skeleton data to skeleton-reference data and to thus generating skeleton-comparison data, and the comparison data comprise at least a portion of the skeleton-comparison data.

13. The system according to claim 9, wherein (g) the system is configured to process user plan data; (h) the user plan data comprise data relating to a training resistance for at least one exercise; and the system is configured to adjust at least one of the training resistances based on at least a portion of the user plan data.

14. The system according to claim 13, wherein the system is configured to adapt the user plan data.

15. A method comprising: (a) using a plurality of training machine assemblies; (b) providing a plurality of training resistances to at least one user; (c) transmitting data between the training machine assemblies; and (d) using a data processing system.

16. The method according to claim 15, wherein the method comprises using at least one camera.

17. The method according to claim 15, wherein the method comprises generating heart-rate data of the user.

18. The method according to claim 17, wherein the heart-rate data comprise heart-rate variability data and the method comprises adjusting the at least one training resistance based on the heart-rate variability data.

19. The method according to claim 15, wherein the method comprises generating skeleton data.

20. The method according to claim 19, wherein the method further comprises: (e) generating the skeleton data of the user based on image data captured by at least one of at least one camera; and (f) using boundary conditions corresponding to geometries of the respective training machine assemblies.

21. The method according to claim 19, wherein the method further comprises: (g) automatically adjusting at least one user support element based on comparison data; (h) comparing the skeleton data to skeleton-reference data and thus generating skeleton-comparison data; wherein the comparison data comprise at least a portion of the skeleton-comparison data.

22. The method according to claim 15, wherein (i) the method comprises processing user plan data; (j) the user plan data comprise data relating to a training resistance for at least one user exercise; and (k) the method comprises adjusting at least one of the training resistances based on at least a portion of the user plan data.

Description

BRIEF FIGURE DESCRIPTION

[2233] FIG. 1 shows a user training at a training machine assembly.

[2234] FIG. 2 shows a user at different points in time using a system in a circuit training.

[2235] FIG. 3 shows a perspective of a camera on a user using a training machine assembly.

[2236] FIG. 4 shows a heart-rate as well as training resistances of a user training in a circuit setup.

DETAILED FIGURE DESCRIPTION

[2237] FIG. 1 shows a user using a training machine assembly 10. The training machine assembly 10 comprises at least one user support element 16. The training machine assembly in this case is a leg press, however, the exact training machine assembly is merely exemplary. For example, the training machine assembly can be a training machine assembly for strength training.

[2238] In FIG. 1, the at least one user support element 16 are two user support elements: A seat on which the user sits, as well as a back rest. The user support element can be automatically adjusted by the training machine assembly.

[2239] The training machine assembly further comprises a contact element, here, a sliding foot rest, to which the training resistance 14 is applied. The training resistance 14 can be for example generated by at least one of an electric motor, a pneumatic or a hydraulic cylinder and weight stack. Here, the training resistance 14 is a force, indicated by the letter F in FIG. 1.

[2240] The training machine assembly 10 further comprises a camera 20. The camera 20 captures image data 40 of the user using the training machine assembly. The image data 40 that the camera captures can be video data.

[2241] The training machine assembly 10 further comprises a control device 30. However, the training machine assembly can also only be connected to the control device 30, for example in a case where there is a system comprising a plurality of training machine assemblies and the system comprises at least one control device 30 controlling the training machine assemblies 10.

[2242] The camera 20 is configured to transmitting the image data 40 to the control device 30. However, the camera can also be configured for transmitting the image data 40 to a data processing system 32.

[2243] FIG. 2 shows a system, comprising four training machine assemblies 10, 10′, 10″ and 10″′. FIG. 2 further shows the user, indicated by the letter A, at three different points in time, t.sub.0, t.sub.1 and t.sub.2. The system is set up for circuit training.

[2244] The user starts to exercise at t0 at the training machine assembly 10. The user then goes to the second machine 10′, for example t1, where he rests and then continues training, for example at t2. The user continues this training mode also for the following training machine assemblies 10″ and 10″′. In FIG. 2, four training machine assemblies 10 are shown, however, there could also be more or less training machine assemblies, and the user can interact with all or just with some of them.

[2245] While the user exercises, the training machine assembly 10, 10′, 10″, 10″′ generates data, for example regarding the heart-rate of the user or regarding a proper exercising style, as will be discussed in the context of FIG. 3. The training machine assemblies can for example also generate image data 40, as discussed in the context of FIG. 1. The training machine assemblies transmit these data to the data-processing system 32.

[2246] The system is configured to operate based on the generated data.

[2247] For example, when the user exercises improperly, the system can instruct the user to exercise properly. The system can be configured for outputting corresponding instruction data in such a case. The system can also be configured for generating corresponding instruction data in such a case.

[2248] The system can also be configured for adapting the training resistances of the training machine assemblies based on the generated data.

[2249] The system can be configured for generating further data based on the image data, such as heart-rate data corresponding to the user, skeleton data, which may refer to a physiology of the user, and skeleton-trajectory data, which may refer to a trajectory of parts or portions of the body of the user, for example to joints of the user.

[2250] FIG. 3 shows image data 40 corresponding to a camera 20 associated with a training machine assembly 10.

[2251] The system can be configured for generating skeleton trajectory data based on the image data. As can be seen in FIG. 3, the system can for example be configured for determining a vertical position Al of the upper end of the head of the user. The system is can further be configured for determining the positions B1 and B2 relating to a left and right upper arm or shoulder of the user.

[2252] The system is configured for comparing the skeleton trajectory data to skeleton reference data. The system can thus for example identify improper exercising of the user.

[2253] The system uses boundary conditions for generating the skeleton trajectory data, which boundary conditions relate to the position of the user. When the user sits on the training machine assembly 10, then his/her chest, arms and head are visible. Also, the user faces the camera 20. Hence, an orientation of the user relative to the training machine assembly is known. One boundary condition can for example relate to the orientation of the user. Another boundary condition can relate to an expected position of the chest of the user.

[2254] These boundary conditions can for example lead to a reduced need of computing power. They can also lead to an improved reliability of the generated data.

[2255] The system can further be configured for generating the heart-rate data of the user based on the image data 40, as discussed above. The system can then be configured for also using boundary conditions. A boundary condition can for example be a limitation of where the head of the user can be located while the user sits on the machine.

[2256] FIG. 4 shows the heart-rate of the user training in a circuit setup on the lower half of the figure (indicated by “HR”/HR.sub.max/HR.sub.min) and a measure for the training resistance value of the training resistance associated with a respective user exercise on the upper half (indicated by “Load”/L1/L2). The training resistance value is indicated relative to a standard training resistance value corresponding to the exercise and the user.

[2257] In FIG. 4, the circuit training comprises five exercises. Obviously, the training could also comprise less or more exercises.

[2258] At the beginning of an exercise, the heart-rate of the user typically rises. For an optimal training effect, the heart rate of the user should be between HR.sub.min and HR.sub.max at the end of each exercise.

[2259] As can be seen, the heart-rate of the user is below HR.sub.min at the end of the first exercise E1. In such a case, the system is configured to increase the training resistance value. This can for example be performed during an exercise. It can also be performed at a next exercise, as can be seen in FIG. 4: The system increases the training resistance of the second exercise E2 with respect to the normal user-specific training resistance corresponding to E2. As can be seen, the heart-rate of the user increases during the second exercise so that it is between HR.sub.max and HR.sub.min at the end of the second exercise.

[2260] As regards the training resistance value, the system applies the same setting also to the third exercise E3. In other words, the training value is increase to a similar or same degree as it was for E2. However, the heart-rate of the user exceeds the range of HR.sub.min and HR.sub.max at the end of E3. The system reacts by reducing the training resistance value for E4 and E5.

[2261] The system can also be configured to determine the level to which the heart-rate of the user drops in between the exercises, as well as a steepness of the heart-rate of the user. These can all be measures for a fitness and/or the exhaustion of the user.

[2262] Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”.

[2263] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), . . . , followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.

[2264] While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.