Method and device for mobile training data acquisition and analysis of strength training

1. A method for mobile acquisition of training data, the method comprising:

affixing a mobile device to a body segment;

determining sensor values in movement patterns via said mobile device;

calculating training data from said sensor values using said mobile device;

storing said training data in a first storage unit in said mobile device;

transmitting said training data from said mobile device to a computer via a data interface;

selecting a strength-training exercise X with a set movement pattern and a training utensil Y, from strength-training exercises and training utensils via the mobile device;

recalling predetermined movement data, consisting of characteristic variables of set movement patterns of said strength-training exercise X using said training utensil Y from a second storage unit in said mobile device;

determining raw sensor values using said mobile device in said set movement patterns of said strength-training exercise X using said training utensil Y, consisting of acceleration and angular speed values;

calculating reworked-measurement values via said mobile device based on said predetermined movement data and said raw sensor;

calculating multiple training data using said mobile device, on the basis of said reworked measurement values.

2. The method as claimed in claim 1 , wherein said calculation of reworked measurement values using said mobile device comprises at least one of the following steps:

initially calibrating said mobile device in order to at least one of: improve said calculation and extend said multiple training data;

including a magnetic flux density vector in said raw sensor values;

fusing said raw sensor values with said predetermined movement data;

integrating said acceleration values twice;

filtering sensor offsets.

3. The method as claimed in claim 1 , wherein said multiple training data, which are based on said raw sensor values, contain at least one of the following items of information:

displacement/time profile of a force contact point of a training load along at least one of: the X-axis, Y-axis and Z-axis;

at least one of: time under tension of eccentric muscle length changes, concentric muscle length changes, and isometric muscle contractions;

number of movement repetitions;

mechanical work;

rotational work;

muscle load;

torque;

force;

impulse;

physical effect;

grip width;

grip variant;

foot position;

initial angle of a superior joint;

muscle length state;

level of exertion;

type of movement in a joint;

intensity technique applied in a training set;

training method.

4. The method of claim 1 , wherein a user selects at least one of a training load, said training utensil, and said strength-training exercise in at least one of: (i) an automated manner by means of an RFID unit and RFID tag, and (ii) a manual manner by means of at least one of a user interface and display unit.

5. The method of claim 1 , wherein said strength-training exercise is acquired automatically, proceeding from said raw sensor values.

6. The method of claim 1 , wherein additional raw sensor values are transmitted from at least one sensor module to said mobile device via a wireless interface.

7. The method as claimed in claim 1 , wherein at least one of said multiple training data, said reworked measurement values, said raw sensor values, and further training data are transmitted to at least one of: (i) the first storage unit in said mobile device, (ii) the computer, and (iii) a wireless station via at least one of an interface and the wireless interface; and transmitted to at least one of (a) a training data server via the Internet, and (b) a computer of an external user via at least one of the Internet and direct connection.

8. The method as claimed in claim 7 , wherein there are continuous analyses of at least one of: said multiple training data, said reworked measurement values, and raw sensor values, personal user data, and said further training data on said training data server;

said continuous analyses are based on a training model;

said training model is stored on said training data server and combines a first submodel and second submodel;

said training model contains at least one of: said multiple training at, said reworked measurement values, and raw sensor values, said personal user data, and said further training data as input data;

said training model predicts the performance of said user in strength training on the basis of said first submodel;

said training model controls the strength training of said user on the basis of said second submodel.

9. The method of claim 1 , wherein a level of exertion in a training set is acquired.

10. The method of claim 1 , wherein at least one of optical, acoustic, and haptic signals are provided to said user via said mobile device for purposes of support when carrying out the predetermined movement patterns.

11. The method of claim 10 , wherein said signals contain information about at least one of a rhythm, an amplitude, and the direction of the predetermined movement patterns.

12. The method as claimed in claim 1 , wherein said calculation of reworked measurement values using said mobile device comprises fusing said raw sensor values with said predetermined movement data.

13. The method of claim 1 , wherein the reworked measurement values are generated in an algorithm based on said raw sensor values and said predetermined movement data from the second storage unit.

14. A device for mobile training data acquisition, the device comprising:

a housing;

a sensor for determine sensor values:

a processor for calculating training data;

a first storage unit for storing said training data;

a data interface for transmitting said training data to a computer;

a second storage unit on which predetermined movement data are stored, wherein the predetermined movement data consist of characteristic variables of set movement patterns of strength-training exercises using a training utensil Y, and wherein the predetermined movement data is configured to be recalled from the processor;

an accelerometer and rate sensor configured to determine at least one of acceleration and angular speed values, which are configured to be transmitted to the processor.

15. The device of claim 14 , wherein the device is a wristwatch.

16. The device of claim 14 , wherein the device contains a wireless interface for wireless data interchange with at least one of: (i) at least one sensor module, (ii) at least one wireless station, and (iii) any other devices.

17. The device of claim 14 , wherein the device contains an RFID unit, which is embodied as RFID unit and as RFID transmission unit;

said RFID unit is configured to communicate with RFID tags, which are (i) attached to said training utensil, (ii) integrated in said training utensil, and/or (iii) situated in the vicinity of at least one of said training utensil and external devices.

18. The device of claim 14 , wherein the device contains a magnetometer for measuring magnetic flux density vector.

19. The device of claim 14 , wherein the device contains at least one of a user interface, a display unit, a vibration motor, and a loudspeaker.