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

• US 9,750,454 B2
• Filed: 09/08/2015
• Issued: 09/05/2017
• Est. Priority Date: 12/15/2011
• Status: Active Grant

• Current Assignee: Hero Workout GmbH
• Original Assignee: Fabian Walke
• Inventors: Walke, Fabian, Radtki, Hauke
• Primary Examiner(s): Huynh, Phuong
• Application Number:
  US14/847,179
• Publication Number:
  US 20150374283A1
• Time in Patent Office: 728 Days
• CPC Class Codes: A61B 2560/0223 of calibration, e.g. protocols for calibrating sensors
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0004 characterised by the type of physiological signal transmitted
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/1125 Grasping motions of hands
• CPC Class Codes: A61B 5/1126 using a particular sensing technique
• CPC Class Codes: A61B 5/224 Measuring muscular strength
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7242 using integration
• CPC Class Codes: A61B 5/725 using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 : characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A61B 90/98 : using electromagnetic means, e.g. transponders
• CPC Class Codes: A63B 1/00 : Horizontal bars
• CPC Class Codes: A63B 2071/0655 : Tactile feedback
• CPC Class Codes: A63B 2071/068 : Input by voice recognition
• CPC Class Codes: A63B 21/0724 : Bar-bells; Hand bars
• CPC Class Codes: A63B 21/0726 : Dumb bells, i.e. with a central bar to be held by a single hand, and with weights at the ends
• CPC Class Codes: A63B 21/4035 : for operation by hand
• CPC Class Codes: A63B 2220/10 : Positions
• CPC Class Codes: A63B 2220/16 : Angular positions
• CPC Class Codes: A63B 2220/17 : Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/34 : Angular speed
• CPC Class Codes: A63B 2220/40 : Acceleration
• CPC Class Codes: A63B 2220/51 : Force
• CPC Class Codes: A63B 2220/802 : Ultra-sound sensors
• CPC Class Codes: A63B 2220/808 : Microphones
• CPC Class Codes: A63B 2225/15 : with identification means that can be read by electronic means
• CPC Class Codes: A63B 2225/20 : with means for remote communication, e.g. internet or the like
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 23/1218 : Chinning, pull-up, i.e. concentric movement
• CPC Class Codes: A63B 23/1236 : Push-ups in horizontal position, i.e. eccentric movement
• CPC Class Codes: A63B 71/0622 : Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

The invention relates to the field of mobile training data acquisition in sport, particularly in strength training, body building, fitness sports and rehabilitation, as well as the analysis of said training data. The invention involves a method and a mobile device (1) for precise acquisition of multiple training data. The multiple training data includes, for example, the time-path curve of the force application point of the training load, the mechanical work and the tension duration of eccentric and concentric muscle length changes and isometric muscle contractions. An analysis of the training data is based on a training model (25).

Claims

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

determining sensor values in movement patterns via said mobile device that is fixed to a body segment;

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

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

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

recalling predetermined movement data, comprising the characteristic variables of set movement patterns of one said sport exercise X using one 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 sport exercise X using said training utensil Y, comprising acceleration and angular speed values;

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

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:

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 either of 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, and 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 sport 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 sport 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'"'"'s sport on the basis of said first submodel;

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

9. The method of claim 1, wherein the 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 by means of said mobile device for the 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.

18. 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.

19. 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.

20. The method of claim 1, wherein the sport is body building, fitness sports or rehabilitative sport.

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

a processor for calculating training data;

a first storage unit for storing said training data;

a second storage unit on which predetermined movement data are stored, wherein the predetermined movement data comprises the characteristic variables of set movement patterns of sport 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 for determining at least one of acceleration and angular speed values, which are transmitted to the processor.

13. The device of claim 12, wherein the device can be embodied in the form of a wristwatch.

14. The device of claim 12, 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.

15. The device of claim 12, 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.

16. The device of claim 12, wherein the device contains a magnetometer for measuring magnetic flux density vector.

17. The device of claim 12, wherein the device contains at least one of a user interface, a display unit, a vibration motor, and a loudspeaker.