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1. (WO2019043421) SYSTEM FOR DETECTING A SIGNAL BODY GESTURE AND METHOD FOR TRAINING THE SYSTEM
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- 63 - CLAIMS

1. A system for detecting a signal body gesture, comprising

- a mobile device (100, 200a, 200b, 200c, 430) and a kinetic sensor (204a, 204b, 204i) adapted for recording a measurement motion parameter pattern corresponding to the time dependence of a motion parameter of the mobile device (100, 200a, 200b, 200c, 430) in a measurement time window (350, 355, 360),

c h a r a c t e r i s e d by further comprising

- a decision unit (210, 375) applying a machine learning classification algorithm subjected to basic training by means of machine training with the application of a training database comprising signal training motion parameter patterns corresponding to the signal body gesture, operated in case the measurement motion parameter pattern having a value being equal to or exceeding a predetermined signal threshold value, being suitable for classifying the measurement motion parameter pattern to a signal body gesture category.

2. The system according to claim 1 , characterised in that the decision unit (210, 375) is adapted for assigning an occurrence probability, characterising a probability of an occurrence of the signal body gesture, based on a measurement motion parameter pattern corresponding to a respective measurement time window (350, 355, 360), to each of the measurement time windows (350, 355, 360), and the classification of a measurement motion parameter pattern corresponding to a given time window (360) to a signal body gesture category is decided by means of the decision unit (210, 375) based on a comparison of occurrence probabilities assigned to the given measurement time window (360) and at least one previous measurement time window (350, 355) with probability threshold values assigned to the measurement time windows (350, 355, 360), wherein the given measurement time window (360) and the at least one previous measurement time window (350, 355) are subsequent to each other and at least the pairs overlap each other.

3. The system according to claim 2, characterised in that the occurrence probabilities assigned to the given measurement time window (360) and to at least one previous measurement time window (355, 360) are arranged in descending series by means of the decision unit (210, 375), and each of at least a part of the occurrence probabilities from the beginning of the series is compared with a probability threshold value corresponding to the position with gradually increasing serial number in the series, respectively.

4. The system according to claim 3, characterised in that the probability threshold values corresponding to positions with gradually increasing serial number are gradually smaller than or equal to the previous value.

5. The system according to any of claims 1-4, characterised in that, for classifying to the signal body gesture category, the values of the measurement motion parameter pattern, as well as short-term summation data and long-term summation data obtained at time instants of the measurement time window (350, 355, 360) by summing up the values of the motion parameter or a power of the absolute values of the motion parameter over a short-term summation period, and a longer long-term summation period, respectively, are applied in the decision unit (210, 375).

6. The system according to claim 5, characterised in that the length of the long-term summation period is 5-15 times the length of the short-term summation period.

7. The system according to any of claims 1-6, characterised in that in the decision unit (210, 375) components of the measurement motion parameter pattern are taken into account weighted according to relevance for classifying to the signal body gesture category.

8. The system according to any of claims 1-7, characterised in that the signal body gesture is a foot stamp or an indirect knock on the mobile device (100, 200a, 200b, 200c, 430).

9. The system according to any of claims 1-8, characterised in that

- start of each signal training motion parameter pattern corresponding to a signal body gesture of the training database applied for machine training is marked by pushing a button of an earphone set or headphone set of the mobile device (100, 200a, 200b, 200c, 430) recording the signal training motion parameter patterns, or by means of a recording sound signal, or

- each signal training motion parameter pattern corresponding to a signal body gesture of the training database are recorded after a respective data entry request of the system.

10. The system according to claim 9, characterised in that the end of each signal training motion parameter pattern corresponding to a signal body gesture is also marked by pushing the button on the earphone set or headphone set of the mobile device (100, 200a, 200b, 200c, 430) or by means of a recording sound signal.

1. The system according to any of claims 1-10, characterised in that the machine learning classification algorithm of the decision unit (210, 375) is subjected to basic training by carrying out the method according to any of claims 12-21.

12. A method for training the system according to any of claims 1-10, c h a r a c t e r i s e d in that the machine learning classification algorithm of the decision unit (210, 375) is subjected to basic training by means of machine training with the application of a training database comprising signal training motion parameter patterns corresponding to the signal body gestures.

13. The method according to claim 12, characterised by comprising the steps of

- recording personalizing data from an end user, and

- personalizing for the end user the machine learning classification algorithm of the decision unit based on the personalizing data.

14. The method according to claim 13, characterised in that the machine learning classification algorithm has respective group-level machine learning models corresponding to at least two user parameter groups formed according to user parameters, and the system further comprises an auxiliary decision unit having an auxiliary decision algorithm adapted for classifying into the at least two user parameter groups, and the method further comprising the steps of

- recording from the end user as personalizing data at least one personalizing motion parameter pattern corresponding to the signal body gesture, and,

- during the personalization of the machine learning classification algorithm of the decision unit for the end user,

- the end user is classified to one of the at least two user parameter groups by means of the auxiliary decision unit based on the at least one personalizing motion parameter pattern, and

- in the machine learning classification algorithm of the decision unit, the group-level machine learning model corresponding to the group according to the classification is applied.

15. The method according to claim 13, characterised by

- recording from the end user as personalizing data at least one personalizing motion parameter pattern corresponding to the signal body gesture, and,

- during the personalization of the machine learning classification algorithm of the decision unit for the end user, subjecting the machine learning classification algorithm having been subjected to the basic training to further training by machine training applying the at least one personalizing motion parameter pattern.

16. The method according to claim 15, characterised by utilizing a neural network-based machine learning classification algorithm in the method, and, during the further training,

- leaving weights of a neural network-based machine learning model corresponding to the machine learning classification algorithm subjected to basic training unchanged,

- inserting complementary layers into the neural network-based machine learning model, and,

- applying the at least one personalizing motion parameter pattern for subjecting the complementary layers to further training by machine training.

17. The method according to claim 13, characterised by

- recording from the end user as personalizing data at least one personalizing motion parameter pattern corresponding to the signal body gesture, and,

- during the personalization of the machine learning classification algorithm of the decision unit for the end user, leaving unchanged a machine learning model corresponding to the machine learning classification algorithm of the decision unit, and subjecting the machine learning classification algorithm to the basic training by machine training utilizing the training database comprising the training motion parameter patterns, as well as utilizing the at least one personalizing motion parameter pattern.

18. The method according to claim 17, characterised by taking into account, during the basic training, the at least one personalizing motion parameter pattern with larger weights compared to the training motion parameter patterns.

19. The method according to claim 13, characterised by

- recording from the end user as personalizing data at least one personalizing motion parameter pattern corresponding to the signal body gesture, and

- during the personalization of the machine learning classification algorithm of the decision unit for the end user, subjecting the machine learning classification algorithm to the basic training by machine training utilizing the training database comprising the training motion parameter patterns, as well as utilizing the at least one personalizing motion parameter pattern, and generating the machine learning model corresponding to the machine learning classification algorithm of the decision unit during the basic training.

20. The method according to any of claims 14-19, characterised by recording from the end user the at least one personalizing motion parameter pattern after a respective data entry request of the system.

21. The method according to claim 13, characterised in that the machine learning classification algorithm has respective group-level machine learning models corresponding to at least two user parameter groups formed according to user parameters, the method further comprising the steps of

- recording from the end user as personalizing data a personal user parameter value of the user parameter being characteristic of the end user,

- classifying the end user, during the personalization of the machine learning classification algorithm of the decision unit for the end user, to one of the at least two user parameter groups based on the personal user parameter value, and

- applying, in the machine learning classification algorithm of the decision unit, the group-level machine learning model corresponding to the group according to the classification.

22. A method for detecting a signal body gesture, comprising the steps of

- recording a measurement motion parameter pattern corresponding to the time dependence of a motion parameter of a mobile device (100,

200a, 200b, 200c, 430) in a measurement time window (350, 355, 360) by means of a kinetic sensor (204a, 204b, 204Ί),

c h a r a c t e r i s e d by

- by means of a decision unit (210, 375) applying a machine learning classification algorithm subjected to basic training by means of machine training with the application of a training database comprising signal training motion parameter patterns corresponding to the signal body

gesture, deciding on classifying the measurement motion parameter pattern to a signal body gesture category, operating the decision unit (210, 375) in case the measurement motion parameter pattern having a value being equal to or exceeding a predetermined detection threshold value.

23. A method for issuing a signal, in particular an alarm signal, ch a racte ri sed by comprising the steps of

- recording a measurement motion parameter pattern by means of the kinetic sensor (204a, 204b, 204i) of the system according to any of claims 1-11,

- deciding, by means of the decision unit of the system according to any of claims 1-11, on classifying the measurement motion parameter pattern into the signal body gesture category, and,

- if the measurement motion parameter pattern has been classified into the signal body gesture category by the decision unit, issuing the signal.

24. A mobile device application, cha racte rised by being controlled by a signal issued by means of the method according to claim 23.

25. A method for controlling a mobile device application, cha racterised by controlling the mobile device (100, 200a, 200b, 200c, 430) by a signal issued by means of the method according to claim

23.

26. A method for recording data, ch a racte rised by comprising the steps of

- marking starts of signal training motion parameter patterns corresponding to signal body gestures of a training database applied for machine training by pushing a button of an earphone set or headphone set of a mobile device (100, 200a, 200b, 200c, 430) recording the training motion parameter patterns or by means of a recording sound signal, or

- recording each signal training motion parameter pattern corresponding to a signal body gesture of the training database after a respective data entry request of the system.

27. The method according to claim 26, characterised by further comprising the step of also marking the end of the signal training motion parameter patterns corresponding to the signal body gestures by pushing the button on the earphone set or headphone set of the mobile device (100, 200a, 200b, 200c, 430) or by means of a recording sound signal.