The present invention relates to a method for determining a position and a force of a finger press on a touch surface and an electronic device configured to perform the method.

In known electronic devices such as touchpads the force measurement is performed using a mesh-like sensor that provides a map of force applied on the coordinates/positions of the touch surface or using a few single point sensors that measure the force applied directly above the location where it is installed.

In order to a achieve an electronic device configured for determining the position and force of the finger press on the touch surface and having low manufacturing costs, implementing a continuous (touchpad) touch detection and force sensor mesh/array is not favorable.

It is an object of the present invention to provide an improved method for determining a position and a force of a finger press on a touch surface and an improved electronic device configured to perform the method.

This object is achieved by the features of the independent claims. Further preferred embodiments of the invention are subject of the dependent claims.

According to an aspect of the invention, a method for determining a position and a force of a finger press on a touch surface of an electronic device which comprises a touch surface, optionally a touch sensor configured to detect when the touch surface is touched, at least two force sensors configured to detect a respective signal indicative of a respective force applied to the respective force sensor when the touch surface is pressed, and a memory, in which a respective regression model is stored for each of the at least two force sensors which describes a relation between a force applied to the touch surface on a specific position of the touch surface and a respective signal detected by a respective force sensor comprises the following steps: a) generating a plurality of hypotheses points such that each hypothesis point is a state of random force and position on the touch surface, when at least one of the two force sensors detects that the touch surface is pressed and/or the touch sensor detects that the touch surface is touched; b) querying each of the regression models by sequentially inputting each of the plurality of hypotheses points to obtain a respective model based value for a respective one of the plurality of hypotheses points for each of the at least two force sensors; c) respectively calculating a probability that the model based value for a respective one of the at least two force sensors is correct for each of the model based values; d) multiplying the respective probabilities that the model based value for a respective one of the at least two force sensors is correct for each of the model based values to obtain a respective weight for each of the plurality of hypotheses points; e) resampling the plurality of hypotheses points based on the respective weight of each of the hypotheses points to obtain a resampled plurality of hypotheses points; f) changing the resampled plurality of hypotheses points by changing at least one resampled hypothesis point such that a predetermined force and an arbitrary contribution of noise is added to the random force and an arbitrary contribution of noise is added to the position of the at least one resampled hypothesis point to obtain a changed resampled plurality of hypotheses points; g) repeating steps b) to f) a number of times until a predetermined condition is fulfilled, wherein the changed resampled plurality of hypotheses points is set as the plurality of hypotheses points, and h) averaging the positions and the random forces of the hypotheses points and determining the position at which the touch surface is pressed and the force with which the touch surface is pressed based on the average position and the average random force of the hypotheses points.

The hypotheses points generated in step a) are also known as “particles” used in a particle filter algorithm, wherein the steps b) to h) are also part of the particle filter algorithm. By using the (particle filter) algorithm, the position and force of the finger press can be determined using only (the at least) two force sensors.

Accordingly, the method can be performed with an electronic device which can be produced at low manufacturing costs since only relative primitive force sensors and optionally a touch sensor with relatively simple touch capability is needed. In addition, the electronic device is easier to manufacture than an electronic device having mesh force sensors and/or piezoelectric layers which could complicate or limit the mechanical design. The at least two force sensors, which are preferably configured as single point sensors, are additionally convenient for the design of the electronic device, as the single point sensors can be located theoretically at any position and then the regression model is learned accordingly. Moreover, the computational load of the electronic device can be held low, because the particle filter algorithm used in the method has a small computational complexity and is configurable in that the number of generated particles can be chosen/adapted.

Respectively calculating the probability that the model based value for a respective one of the at least two force sensors is correct for each of the model based values performed in step c) can also be described as respectively calculating a probability that the measurement is correct assuming/given that an underlying hypothesis is true.

According to an embodiment step h) comprises averaging the positions and the random forces of hypothesis points having weights higher than a predetermined value and determining the average of the positions and the average of the random forces of the hypotheses points having the weights higher than the predetermined value as the position at which the touch surface is pressed and as the force with which the touch surface is pressed.

According to another embodiment step h) comprises averaging the positions and the random forces of a number of hypotheses points having the highest weights and determining the average of the positions and the average of the random forces of the number of hypotheses points having the highest weights as the position at which the touch surface is pressed and as the force with which the touch surface is pressed.

According to an embodiment the probability that the model based value for the respective one of the at least two force sensors is correct for each of the model based values is calculated in step c) based on a comparison of the respective model based value with the respective signal detected by the respective force sensor.

According to an embodiment the touch surface is supported by dampers and the at least two force sensors are each configured to detect a respective displacement of a respective point of the touch surface. Here, in particular, the respective point of the touch surface corresponds to a point of the touch surface which is arranged directly above the respective force sensor. According to an embodiment the predetermined force is determined based on an average rate of increase in the force of a human finger press and a sampling rate of the at least two force sensors.

According to an embodiment the touch surface is divided into two or more areas, in particular four areas, wherein the touch sensor is configured to detect which of the two or more areas is touched.

According to an embodiment the predetermined condition is fulfilled if a rate of increase of the signal detected by at least one of the at least two force sensors is smaller than a predetermined threshold value.

According to an embodiment the weights of the plurality of hypotheses points are normalized such that the sum of the weights of the plurality of hypotheses points is 1 , and the normalized weights of the respective hypotheses points are set as the weights of the respective hypotheses points after step d).

According to another aspect of the invention an electronic device comprises: a touch surface; optionally a touch sensor configured to detect when the touch surface is pressed; at least two force sensors configured to detect a respective signal indicative of a respective force applied to the respective force sensor when the touch surface is pressed; a memory, in which a respective regression model is stored for each of the at least two force sensors which describes a relation between a force applied to the touch surface on a specific position of the touch surface and a respective signal detected by a respective force sensor; and a control unit which is configured to: a) generate a plurality of hypotheses points such that each hypothesis point is a state of random force and position on the touch surface, when at least one of the two force sensors detects that the touch surface is pressed and/or the touch sensor detects that the touch surface is touched; b) query each of the regression models by sequentially inputting each of the plurality of hypotheses points to obtain a respective model based value for a respective one of the plurality of hypotheses points for each of the at least two force sensors; c) respectively calculate a probability that the model based value for a respective one of the at least two force sensors is correct for each of the model based values; d) multiply the respective probabilities that the model based value for a respective one of the at least two force sensors is correct for each of the model based values to obtain a respective weight for each of the plurality of hypotheses points; e) resample the plurality of hypotheses points based on the respective weight of each of the hypotheses points to obtain a resampled plurality of hypotheses points; f) change the resampled plurality of hypotheses points by changing at least one resampled hypothesis point such that a predetermined force and an arbitrary contribution of noise is added to the random force and an arbitrary contribution of noise is added to the position of the at least one resampled hypothesis point to obtain a changed resampled plurality of hypotheses points; g) repeat steps b) to f) a number of times until a predetermined condition is fulfilled, wherein the changed resampled plurality of hypotheses points is set as the plurality of hypotheses points, and h) average the positions and the random forces of the hypotheses points and determine the position at which the touch surface is pressed and the force with which the touch surface is pressed based on the average position and the average random force of the hypotheses points.

Respectively calculating the probability that the model based value for a respective one of the at least two force sensors is correct for each of the model based values performed in c) can also be described as respectively calculating a probability that the measurement is correct assuming/given that an underlying hypothesis is true.

According to an embodiment the electronic device is configured to average the positions and the random forces of hypothesis points having weights higher than a predetermined value and to determine the average of the positions and the average of the random forces of the hypotheses points having the weights higher than the predetermined value as the position at which the touch surface is pressed and as the force with which the touch surface is pressed in h).

According to an embodiment the electronic device is configured to average the positions and the random forces of a number of hypotheses points having the highest weights and to determine the average of the positions and the average of the random forces of the number of hypotheses points having the highest weights as the position at which the touch surface is pressed and as the force with which the touch surface is pressed in h). According to an embodiment the electronic device is configured to respectively calculate the probability that the model based value for the respective one of the at least two force sensors is correct for each of the model based values based on a comparison of the respective model based value with the respective signal detected by the respective force sensor.

According to an embodiment the touch surface is supported by dampers, wherein the at least two force sensors are each configured to detect a respective displacement of a respective point of the touch surface. Here, in particular, the respective point of the touch surface corresponds to a point of the touch surface which is arranged directly above the respective force sensor.

According to an embodiment the predetermined force is determined based on an average rate of increase in the force of a human finger press and a sampling rate of the at least two force sensors.

According to an embodiment the touch surface is divided into two or more areas, in particular four areas, wherein the touch sensor is configured to detect which of the two or more areas is touched.

According to an embodiment the predetermined condition that the touch surface is pressed is fulfilled if the signal detected by at least one of the at least two force sensors is greater than a predetermined threshold value.

According to an embodiment the predetermined condition is fulfilled if a rate of increase of the signal detected by at least one of the at least two force sensors is smaller than a predetermined threshold value.

According to an embodiment the control unit is further configured to normalize the weights of the plurality of hypotheses points such that the sum of the weights of the plurality of hypotheses points is 1 , and to set the normalized weights of the respective hypotheses points as the weights of the respective hypotheses points, after multiplying the respective probabilities that the model based value for a respective one of the at least two force sensors is correct for each of the model based values to obtain a respective weight for each of the plurality of hypotheses points.

Further advantages and features result from the dependent claims and the embodiment examples. For this purpose shows, partially schematized: Fig. 1 an electronic device according to an embodiment;

Fig. 2 a touch surface of the electronic device shown in Fig. 1 ;

Fig. 3 the touch surface shown in Fig. 2 with positions of generated hypotheses points indicated; and

Fig. 4 the touch surface shown in Fig. 3 with positions of a plurality of resampled hypotheses points indicated.

Fig. 1 shows an electronic device according to an embodiment and Fig. 2 shows a touch surface of the electronic device shown in Fig. 1 .

The electronic device 100, which may be used in a vehicle and may be configured as a touch-based steering wheel switch or a driver infotainment touch screen, for example, comprises a touch surface 10 for receiving a finger press performed by a user in order to control the electronic device 100 or a separate (external electronic) device in communication with the electronic device 100. The touch surface 10 is divided into two or more areas 1 1 , 12, 13, 14, in the present embodiment into four areas 1 1 , 12, 13, 14.

The electronic device 100 further comprises a touch sensor 20 configured to detect when the touch surface 10 is touched and, in particular, which of the four areas 11 , 12, 13, 14 is pressed.

Furthermore, the electronic device 100 comprises a first and a second force sensor FS- a, FS-b, in particular a first and a second single-point force sensor FS-a, FS-b, arranged in an inside of the electronic device 100 below the touch surface 10 at different positions and configured to detect a respective (analog) signal indicative of a respective force applied to the respective force sensor FS-a, FS-b when the touch surface 10 is pressed.

With respect to the configuration of the electronic device 100, it is preferable that there is some asymmetry, either in the touch surface 10 shape, mechanical design or position of the first and second force sensors FS-a, FS-b, so that the algorithm described later to determine the position and the force at which the touch surface 10 is pressed does not confuse a press with another that affects the first and second force sensors FS-a, FS-b in the same way. In particular, the first and second force sensors FS-a, FS-b are configured to translate a (vertical) displacement of a respective point of the touch surface 10 corresponding to the respective position of the respective force sensor FS-a, FS-b to an (analog) signal representing the applied force.

The touch surface 10 is supported by dampers (not shown) such that a push/finger press at any location/position on the touch surface 10 moves the entire surface points down with different (vertical) displacements. Therefore, a push with a force F just above the sensor point of a respective force sensor FS-a, FS-b causes a larger displacement (and corresponding analog signal) than a push with the same force F at a position distant from the sensor point.

The electronic device 100 further comprises a memory 30, in which a respective regression model is stored for each of the first and second force sensors FS-a, FS-b. The respective regression models describe a relation between a force F applied to the touch surface 10 on a specific (two-dimensional) position P (x, y) of the touch surface 10 and a respective signal detected by the respective first or second force sensor FS-a, FS- b.

The fitting of the data in order to obtain the respective regression models can be done by linear or polynomial regression depending on the physical properties of the electronic device 100. Training samples for building the regression models can either be collected using a simulation of the behavior/bending of the touch surface 10 when the touch surface 10 is pressed and the corresponding signals generated by the first and second force sensors FS-a, FS-b or using real measurement data.

The following formula describes a linear regression model in which C ^A represents the respective signal detected by the respective first or second force sensor FS-a, FS-b when the force F is applied to the position P (x, y) on the touch surface 10 indicated by the position x on the X-coordinate and the position y on the Y-coordinate, wherein p ₀ , Pi, p ₂ , and p ₃ are coefficients.

In addition, the electronic device 100 comprises a control unit 40 which is connected to the touch sensor 20, the first and second force sensors FS-a, FS-b, the memory 30 and a communication unit 50, which allows the communication with an external (electronic) device to be controlled by the electronic device 100 based on the position and/or force of received finger press.

The control unit 40, the touch sensor 20, the first and second force sensors FS-a, FS-b, the memory 30 and the communication unit 50 are configured to communicate with each other and to transfer data in-between. In particular, the control unit 40 is configured to receive from the touch sensor 20 one or more signals indicating that the touch surface 10 is touched and which of the areas 1 1 , 12, 13, 14 is touched. Furthermore, the control unit 40 is configured to receive from the first and second force sensors FS-a, FS-b the respective signals indicative of the respective force applied to the respective force sensor FS-a, FS-b.

Furthermore, the control unit 40 is configured to a) generate a plurality of hypotheses points (particles) HPi, ..., HP _n (or simply HPi with i = 1 , ..., n) such that each hypothesis point HPi, ..., HP _n is a state of random force and position on the touch surface 10, when the touch sensor 20 detects that the touch surface 10 is touched. An example for the positions of the generated hypotheses points HPi, ..., HP ₉ is shown in Fig. 3. In this regard, preferably only hypotheses points HPi, ..., HP _n are generated whose positions are located in the (detected) area 1 1 , 12, 13, 14 which has been pressed. In the example illustrated in Fig. 3, it has been detected by the touch sensor 20 that the area 1 1 has been touched. Hence, only hypotheses points HPi are generated which have positions located in area 11 .

The control unit 40 is further configured to b) query each of the regression models by sequentially inputting each of the plurality of hypotheses points HPi to obtain a respective model based value for a respective one of the plurality of hypotheses points HPi, ..., HP _n for each of the first and second force sensors FS-a, FS-b, and to c) respectively calculate a probability that the model based value for a respective one of the first and second force sensors FS-a, FS-b is correct for each of the model based values.

In a preferred embodiment, the calculation of the probability is based on a comparison of the respective model based value with the respective signal detected by the respective force sensor FS-a, FS-b. For example, the probabilities Probi, Fs-a and Probi, _F s-b that a hypothesis point HPi is correct may be calculated using the following formulas: wherein Cps-a ^A i and Cps-b ^A i are the model based values for the hypothesis point HPi, CFS- _a and Cps-b are the signals detected by the force sensors FS-a and FS-b, e.g. capacitance values when corresponding force sensors are used, and o is the standard deviation of the distribution around a measurement, for example a normal distribution. The calculated probabilities can be also be denoted as a weights w[(FS-a)]i and w[(FS-b)]i.

The control unit 40 is further configured to d) multiply the respective probabilities that the model based value for a respective one of the first and second force sensors FS-a, FS- b is correct for each of the model based values to obtain a respective weight Wi for each of the plurality of hypotheses points HPi, in particular by using the following formula:

Wj = w[(FS — a)] i x w[(FS — h)]j

The control unit 40 is further configured to e) resample the plurality of hypotheses points HPi based on the respective weight of each of the hypotheses points HPi to obtain a resampled plurality of hypotheses points HPi as shown in Fig. 4. In this resampling process, hypotheses points HPi having a relative low weight Wi are cancelled or less likely chosen while other hypotheses points HPi having a relative high weight Wi are chosen more than one time to obtain the resampled plurality of hypotheses points HPi

The control unit 40 is further configured to f) change the resampled plurality of hypotheses points HPi by changing at least one resampled hypothesis point HPi such that a predetermined force and an arbitrary contribution of noise is added to the random force and an arbitrary contribution of noise is added to the position of the at least one resampled hypothesis point HPi to obtain a changed resampled plurality of hypotheses points HPi. In a preferred embodiment, all of the resampled hypothesis points HPi are changed to obtain the changed resampled plurality of hypotheses points HPi.

An example for the adding of the respective arbitrary contribution of noise, in particular Gaussian noise, to the random force and to the position of the at least one resampled hypothesis point HPi is illustrated in the following formulas: yt + /v(o, a) F _t <- F _L + N(0, a), wherein

N (0, a) is the normal distribution with respect to a center point 0 and o is a standard deviation which is chosen with respect to Xi and yi depending on the size of the sensor area and with respect to Fi depending on a press force range with which the press is performed, wherein the o’s for Xj, yi and Fi can be chosen different from each other.

An example for the adding of the predetermined force to the random force of the at least one resampled hypothesis point HPi is illustrated in the following formula:

Ft F _t + F _av x T _sam pi _e , wherein

F _av is an average rate of increase in the force of a human finger press and T _S am _P ie is a sampling rate of the force sensors FS-a, FS-b and/or the touch sensor 20.

The electronic device 40 is further configured to g) repeat steps b) to f) a number of times until a predetermined condition is fulfilled, wherein the changed resampled plurality of hypotheses points HPi is set as the plurality of hypotheses points HPi, to h) average the positions and the random forces of the hypotheses points HPi and to determine the position P (x, y) at which the touch surface 10 is pressed and the force F with which the touch surface 10 is pressed based on the average position and the average random force of the hypotheses points HPi, ..., HP _n .

According to an embodiment the electronic device 40 is configured to average the positions and the random forces of hypothesis points having weights higher than a respective predetermined value and to determine the average of the positions and the average of the random forces of the hypotheses points HPi, ..., HP _n having the weights higher than the respective predetermined values as the position P (x, y) at which the touch surface 10 is pressed and as the force F with which the touch surface 10 is pressed in h).

According to another embodiment the electronic device 40 is configured to average the positions and the random forces of a number of hypotheses points HPi, ..., HP _n having the highest weights and to determine the average of the positions and the average of the random forces of the number of hypotheses points having the highest weights as the position P (x, y) at which the touch surface 10 is pressed and as the force F with which the touch surface 10 is pressed in h). The predetermined condition may be fulfilled if a rate of increase of the signal detected by at least one of the first and second force sensors FS-a, FS-b is smaller than a predetermined threshold value which indicates that the finger press is stopped.

LIST OF REFERENCE SIGNS

10 touch surface

11 -14 area of touch surface 20 touch sensor

30 memory

40 control unit

50 communication unit

100 electronic device FS-a first force sensor

FS-b second force sensor

HPi hypothesis point