Device, method and application for establishing a current load level

Abstract

The invention relates to a device, a method, a computer program product and an application for establishing a current load level ( 36, 36 A, 36 B, 36 C, 36 D) of a user. The device and the method comprise a mobile terminal ( 1 ), which comprises at least one sensor ( 2 ) generating signal data ( 31 ) and a plurality of available applications ( 5 ) for use by the user and also an evaluation unit ( 13, 6 ). According to the invention, provision is made for the mobile terminal ( 1 ) to comprise an application, which is configured as further application ( 6 ) and establishes a plurality of biometric data ( 33 ) in respect of the user, at least from the signal data ( 31 ) or from available applications ( 5 ) used by the user, and to make said data available to the evaluation unit ( 13, 6 ), and for the evaluation unit ( 13, 6 ) to determine the current load level ( 36, 36 A, 36 B, 36 C, 36 D) of the user from the biometric data ( 33 ).

1 .- 38 . (canceled) 
     
     
         39 . An apparatus for ascertaining a current stress level for a user, comprising:
 a mobile terminal having:
 at least one sensor that produces signal data and is integrated in the mobile terminal; 
 a plurality of available applications for use by the user; and 
 an evaluation unit, 
   wherein the mobile terminal has an application that is provided and designed to ascertain a plurality of biometric data pertaining to the user at least from the signal data or from available applications used by the user and to make said biometric data available to the evaluation unit, and in that the evaluation unit is provided and designed to determine the current stress level of the user from the biometric data; and   wherein the evaluation unit is designed and provided to determine the current stress level of the user from the biometric data by applying a method that is performed on the at least one artificial neural network.   
     
     
         40 . The apparatus as claimed in  claim 39 , wherein the application is storable as a SIM application in a memory area on a SIM card that can be operated in the mobile terminal, and is executable by a separate execution unit integrated on the SIM card. 
     
     
         41 . The apparatus as claimed in  claim 39 , wherein the evaluation unit is designed and provided to determine the current stress level of the user from the biometric data by applying a method that is performed on a plurality of artificial neural networks that are designed as a convolutional deep belief network. 
     
     
         42 . The apparatus as claimed in  claim 39 , wherein the plurality of biometric data can be divided into a plurality of categories, wherein the categories comprise a plurality of the following categories selected from the group that consists of:
 sleep;   speech;   motor functions;   social interaction;   economic data;   personal data; and   questionnaire data.   
     
     
         43 . The apparatus as claimed in  claim 42 , wherein a plurality of artificial neural networks is provided, and wherein the plurality of artificial neural networks comprises two neuron layers, an input layer and a hidden layer, wherein the input layer comprises a plurality of input neurons and the hidden layer comprises a plurality of hidden neurons, and the evaluation unit is designed and provided to stipulate the input layer of at least one of the artificial neural networks by means of the hidden layers of a plurality of other artificial neural networks. 
     
     
         44 . The apparatus as claimed in  claim 42 , wherein the at least one artificial neural network comprises two neuron layers, an input layer and a hidden layer, wherein the input layer comprises a plurality of input neurons and the hidden layer comprises a plurality of hidden neurons, and wherein the values of the input neurons can be stipulated by means of the plurality of biometric data. 
     
     
         45 . The apparatus as claimed in  claim 44 , wherein a plurality of artificial neural networks is provided, and wherein the evaluation unit interacts with a plurality of processors, and the respective processor is designed and provided to compute neurons for at least one of the plurality of artificial neural networks, wherein the apparatus is designed and provided to allow the plurality of artificial neural networks to be computed by the plurality of processors in parallel. 
     
     
         46 . The apparatus as claimed in  claim 44 , wherein at least one graphics card with at least one graphics card processor is arranged in the mobile terminal or in the central server and the at least one graphics card processor can support computation of a plurality of artificial neural networks. 
     
     
         47 . The apparatus as claimed in  claim 44 , wherein the at least one sensor integrated in the mobile terminal comprises a gyroscope, an acceleration sensor, a light sensor or a combination of said sensors. 
     
     
         48 . The apparatus as claimed in  claim 39 , wherein the at least one artificial neural network comprises two neuron layers, an input layer and a hidden layer, wherein the input layer comprises a plurality of input neurons and the hidden layer comprises a plurality of hidden neurons, and wherein the values of the input neurons can be stipulated by means of the plurality of biometric data. 
     
     
         49 . The apparatus as claimed in  claim 48 , wherein the evaluation unit is designed and provided to stipulate the current stress level from at least one output neuron that can be identified using at least one hidden neuron of at least one artificial neural network. 
     
     
         50 . The apparatus as claimed in  claim 39 , wherein a plurality of artificial neural networks is provided, and wherein the plurality of artificial neural networks comprises two neuron layers, an input layer and a hidden layer, wherein the input layer comprises a plurality of input neurons and the hidden layer comprises a plurality of hidden neurons, and the evaluation unit is designed and provided to stipulate the input layer of at least one of the artificial neural networks by means of the hidden layers of a plurality of other artificial neural networks. 
     
     
         51 . The apparatus as claimed in  claim 39 , wherein a plurality of artificial neural networks is provided, and wherein the evaluation unit interacts with a plurality of processors, and the respective processor is designed and provided to compute neurons for at least one of the plurality of artificial neural networks, wherein the apparatus is designed and provided to allow the plurality of artificial neural networks to be computed by the plurality of processors in parallel. 
     
     
         52 . The apparatus as claimed in  claim 39 , wherein at least one graphics card with at least one graphics card processor is arranged in the mobile terminal or in the central server and the at least one graphics card processor can support computation of a plurality of artificial neural networks. 
     
     
         53 . The apparatus as claimed in  claim 39 , wherein the at least one sensor integrated in the mobile terminal comprises a gyroscope, an acceleration sensor, a light sensor or a combination of said sensors. 
     
     
         54 . A method for ascertaining a current stress level for a user of a mobile terminal, comprising:
 starting an application installed on a mobile terminal, so that said application is executed on the mobile terminal;   ascertaining a plurality of biometric data pertaining to the user by means of the application, wherein the biometric data are ascertained at least from use data, which are captured from the use of at least one existing application available on the mobile terminal by the user, or from signal data from at least one sensor integrated in the mobile terminal; and   evaluating the biometric data by means of an evaluation unit in order to determine the current stress level, wherein the evaluation unit determines a current stress level using at least one artificial neural network.   
     
     
         55 . The method as claimed in  claim 54 , which method can be carried out on an apparatus that includes a mobile terminal having:
 at least one sensor that produces signal data and is integrated in the mobile terminal;   a plurality of available applications for use by the user, including said application that is started on the mobile terminal; and   said evaluation unit.   
     
     
         56 . The method as claimed in  claim 54 , wherein the application, while executed on the mobile terminal, ascertains that biometric data by verifying that the at least one sensor integrated in the mobile terminal provides signal data or the at least one application available on the mobile terminal provides use data, and in the event of successful verification the application ascertains the biometric data at least from the provided signal data or the provided use data and provides said biometric data with a timestamp and wherein the evaluation unit determines the current stress level from the biometric data whose timestamp is currently valid. 
     
     
         57 . The method as claimed in  claim 54 , wherein the evaluation unit trains the at least one artificial neural network on the basis of biometric data and stress levels determined for the biometric data, as a result of which the quality of the artificial neural network is improved. 
     
     
         58 . An application for a mobile terminal, which is designed to be used as an application of a mobile terminal and has the following functions:
 ascertainment of a plurality of biometric data pertaining to a user, wherein the biometric data are ascertained at least from signal data from at least one sensor integrated in the mobile terminal or from use data from the use of at least one application available on the mobile terminal by the user; and   evaluation of the biometric data in order to determine a current stress level.