Data input device

Abstract

A data input device (10) comprises a functional surface (12) designed to receive pressure from a user, such as pressure from a finger (14) of a user; a piezoelectric film (16) having at least one axis of sensitivity (30, 34) and arranged below the functional surface and designed to emit an electrical signal depending on the pressure received by the functional surface; and a computing unit (18) configured to receive the signal generated by the piezoelectric film in response to the pressure on the functional surface; and, based on this signal, to determine an area and / or a shape of the functional surface pressed by the user using a classification algorithm.

Description

Description DATA ENTRY DEVICE technical field

[0001] This disclosure falls within the domain of data input devices, including touch surfaces and keyboards. Previous technique

[0002] Keyboards typically consist of a surface that is sensitive to finger pressure, associated with each key. The keyboard may incorporate mechanical elements (for example, an inductive switch), capacitive elements (for example, electrodes), or even ultrasonic elements.

[0003] Regardless of the solution, the pressure-sensitive areas must be arranged in a matrix or a specific layout unique to a keyboard. Depending on the technology used, various mechanical limitations arise in their integration. For example, mechanical switches can be aesthetically unappealing in addition to requiring a travel distance, while inductive switches suffer from their magnetic sensitivity. As for capacitive matrices, they perform poorly when positioned behind metal.

[0004] It would therefore be beneficial to have a keyboard or, more generally, a data input device that is free from these constraints. Summary

[0005] This disclosure improves the situation.

[0006] A data input device is proposed comprising a functional surface adapted to receive pressure from a user, such as the pressure of a user's finger; a piezoelectric film having at least one axis of sensitivity and being disposed below the functional surface and adapted to emit an electrical signal as a function of the pressure received by the functional surface; and a computing unit configured to: receive the signal generated by the piezoelectric film in response to the pressure on the functional surface; and determine from this signal an area and / or a shape of the functional surface pressed by the user by means of a classification algorithm.

[0007] By "below," we refer to an axis orthogonal to the plane of the two largest dimensions of the functional surface. The underside of the functional surface extends, in use, to the side opposite the face in contact with the user, in particular the user's finger.

[0008] The functional surface can thus form a human-machine interface of the touchscreen type. Preferably, the functional surface forms, in use, an external surface of a motor vehicle. The invention therefore makes it very easy to transform any portion of the bodywork or other external surface of a motor vehicle into a touchscreen surface.

[0009] According to another aspect, a motor vehicle such as a car including the above device is proposed.

[0010] The features described in the following paragraphs may optionally be implemented, independently of each other or in combination with each other:

[0011] - the shape is a point, a line or a two-dimensional figure.

[0012] - the computing unit configured to determine from the signal generated by the piezoelectric film the temporal sequence of pressing the functional surface using a classification algorithm.

[0013] - the input device is a keyboard, the functional surface includes a plurality of zones, each zone corresponding to a key, and the processing unit is configured to determine from this signal the key corresponding to the zone of the functional surface pressed by the user.

[0014] - the keys are arranged along a line and the piezoelectric film is a single piezoelectric film having a sensitivity axis arranged parallel to the line of keys.

[0015] - the keys are arranged in a matrix according to rows and columns, and the piezoelectric film is a single piezoelectric film having at least two non-parallel sensitivity axes.

[0016] - the keys are arranged in a matrix according to rows and columns, and the piezoelectric film consists of a first piezoelectric film having a first axis of sensitivity and a second piezoelectric film having a second axis of sensitivity, the first piezoelectric film being arranged in relation to the second piezoelectric film so as to have the first axis of sensitivity at a non-zero angle, preferably at 90 degrees, from the second axis of sensitivity.

[0017] - the first piezoelectric film is positioned opposite the functional surface so that its first axis of sensitivity is parallel to the lines of the key matrix, and the second piezoelectric film is positioned opposite the functional surface so that the second axis of sensitivity is parallel to the columns of the key matrix.

[0018] - the classification algorithm is of the set-theoretic type.

[0019] - the classification algorithm is of the boosting type, for example AdaBoost (Adaptive Boosting), Gradient Boosting, or XGBoost (Extreme Gradient Boosting).

[0020] - the piezoelectric film is directly bonded to the functional surface. Brief description of the drawings

[0021] Other features, details, and advantages will become apparent upon reading the detailed description below and analyzing the attached drawings, on which: Fig. 1

[0022] [Fig. 1] schematically shows a data input device according to one embodiment, in the form of a keyboard. Fig. 2

[0023] [Fig. 2] shows a partial, transparent top view of the data input device of [Fig. 1], Fig. 3

[0024] [Fig. 3] shows an example of electrical signals observed when a user presses a part of the data input device of [Fig. 1], Fig. 4

[0025] [Fig. 4] shows an example of an electrical signal (left) and its conversion (right) into a time graph of the slope of said signal. Fig. 5

[0026] [Fig. 5] shows examples of time window segmentations of electrical signal slope time graphs observed when a user presses a part of the data input device. Description of the implementation methods

[0027] Reference is now made to Figure 1. A data input device 10 comprises a functional surface 12 adapted to receive finger pressure 14 from a user, a piezoelectric film 16 disposed below the functional surface 12 and adapted to emit an electrical signal according to the user's finger pressure 14 on the functional surface 12, and a processing unit 18 configured to receive the signal generated by the piezoelectric film 16 in response to the pressure of fingers 14 on the functional surface 12, and determine from this signal an area of ​​the functional surface 12 pressed by the user.

[0028] The data input device 10 allows the user to identify the shape and position of pressure applied to the functional surface 12 by one or more fingers. The shape of the pressure can be a point (for example, if the data input device 10 is a keyboard), a line, or a two-dimensional figure (for example, a circle, a zigzag, or a triangle).

[0029] The functional surface 12 includes an outer face 20 that receives the pressure of the user's fingers 14, and an inner face 22 connected to the piezoelectric film 16. The outer surface 20 can receive the pressure of the user's fingers 14 directly, for example as a touch surface, or via the interaction of a mechanical device, such as a keyboard key. The functional surface 12 can be a monolithic surface or consist of a plurality of adjacent surfaces.

[0030] The functional surface 12 can be any surface suitable for transmitting the pressure of the user's fingers to the piezoelectric film 16. Therefore, the functional surface 12 can be relatively rigid as long as the piezoelectric film 16 can detect an atomic displacement in the functional surface 12. The functional surface 12 can be of any material and shape.

[0031] The piezoelectric film 16 can be directly glued to the functional surface 12. In another embodiment, an element is disposed between the piezoelectric film 16 and the functional surface 12. This element allows the user's finger pressure to be transmitted.

[0032] The piezoelectric film 16 includes at least one piezoelectric film having at least one axis of sensitivity. Thus, depending on the implementation of the data input device 10, there may be a single film with a single axis of sensitivity for detecting pressures arranged in a line (for example, keys arranged in a line or a line-type movement, in which case detection is only required along one dimension / axis), or there may be a single film with two or more axes of sensitivity for detecting pressures along two dimensions / axes (for example, keys arranged in rows and columns (matrix) or a two-dimensional figure). Alternatively, the piezoelectric film 16 may include two or more piezoelectric films, each having one axis of sensitivity for detecting pressures along two dimensions.

[0033] The calculation unit 18 allows for the analysis of finger pressures 14 on the functional surface 12 in order to deduce their location and shape. To do this, the calculation unit uses a classification algorithm. The number of classes in the classification algorithm will depend on the number of finger pressure patterns / locations to be identified. For example, for a 9-digit numeric keypad, the number of classes will be 9.

[0034] The calculation unit 18 can be located near or far from the functional surface 12.

[0035] Referring now to Figure 2, an example of the implementation of the data input device 10 is shown in the form of a 9-key numeric keypad 24, each key corresponding to a number from 1 to 9. The functional surface 12 is for this purpose divided into 9 zones 26. This division can be virtual or physical.

[0036] Since the data input device 10 is a numeric keypad 24 with rows and columns (i.e., two-dimensional), the piezoelectric film 16 has at least two axes of sensitivity. In the case of Figure 2, it was chosen that the piezoelectric film 16 be made up of two piezoelectric films, each having one axis of sensitivity. It would also have been possible to have a single piezoelectric film with two or more axes of sensitivity. It would also have been possible to have more than two piezoelectric films.

[0037] In the case of Figure 2, therefore, the piezoelectric film 16 consists of a first piezoelectric film 28 having a first sensitivity axis 30, and a second piezoelectric film 32 having a second sensitivity axis 34. The sensitivity axes 30 and 34 are shown as dashed lines in Figure 2. The first piezoelectric film 28 is positioned relative to the second piezoelectric film 32 such that its first sensitivity axis 30 lies at a non-zero angle to the second sensitivity axis 34. In the example in Figure 2, the angle between the first sensitivity axis 30 and the second sensitivity axis 34 is 90 degrees. However, it is possible that the first sensitivity axis 30 could be at a non-zero angle other than perpendicular to the second sensitivity axis 34.Having the sensitivity axes 30, 34 of the piezoelectric film(s) allows us to detect a shape which would be either at a two-dimensional position on the functional surface 12, such as a point on the numeric keypad 24, or a two-dimensional shape, such as a circle, triangle or zigzag.

[0038] Referring now to Figure 3, an example of the signal generated by the first piezoelectric film 28 is shown when the functional surface 12 is touched at different points by the user. It can be seen that for each point of impact of the finger 14 (for example, the user presses an area of ​​the functional surface 12 corresponding to key 1, or key 3), a different electrical signature E1, E2, ... is generated by the piezoelectric film. Thus, from these signatures, the processing unit 18, thanks to The classification algorithm will be able to determine which area and / or shape of the functional surface 12 was pressed by the user.

[0039] For the classification algorithm to use the electrical signatures, these are first converted into a 1-dimensional N x 1 vector, representing the N characteristics of the signal. Referring to Figures 4 and 5, the signal processing steps for obtaining the vectors corresponding to the electrical signatures will now be explained.

[0040] Figure 4 shows on the left an example of an electrical signal En as it might have been recorded, for example, in Figure 3. The electrical signal En in the left-hand figure shows the evolution of the signal's intensity over time. This signal is first converted into a graph (Figure 4, right) showing the evolution of the slope of the signal En over time. This conversion eliminates noise and highlights the signal's variation. This graph of the slope of the signal En over time is then divided into two or three time windows T1, T2, T3, as illustrated in Figure 5.

[0041] Figure 5 shows four graphs of the slope evolution of the electrical signal emitted by the piezoelectric films according to the area of ​​the functional surface (i.e., key in the case of the keyboard). More specifically, graphs (A) and (B) represent the electrical signals emitted by the first piezoelectric film 28 when keys 1 and 3 are pressed, respectively. Graphs (C) and (D) represent the electrical signals emitted by the second piezoelectric film 30 when keys 1 and 3 are pressed, respectively.

[0042] Thus, for each electrical signature received by each piezoelectric film, transformed into a time-evolution graph of the slope of the electrical signal, the slope graph is divided into two, and possibly three, time windows. These windows correspond to phases of finger 14 pressing on the functional surface 12 (window T1), finger 14 releasing from the functional surface 12 (window T2), and optionally, if this occurs, a rebound once the finger is released (as for example in a keyboard) (window T3).

[0043] Figure 5 shows an example of dividing the slope of the electrical signal into three windows (with the bounce present). During the first time window T1, corresponding to the pressing of finger 14 on the functional surface 12, the slope is positive or negative. During the second time window T2, corresponding to the release of finger 14 from the functional surface 12, the slope has the opposite sign to the sign of the slope in the first time window T1. The boundary between the first and second phases is therefore at the moment of sign change. During the third time window T3, corresponding to the optional bounce, the slope has the opposite sign to the sign of the slope in the second time window T2 (positive or negative). The boundary between the second and third time windows T2, T3 is therefore at the moment of sign change.

[0044] According to one embodiment, a signal processing algorithm allows each time graph of the slope of the electrical signal to be divided into 2 or 3 time windows by looking at the changes in sign of the slope.

[0045] For each time window, features will be extracted. The supervised learning algorithm will use these features to classify the received electrical signature into a corresponding area and / or shape on the functional surface (i.e., for a keyboard, to identify the key pressed by the user, or for a touch surface, to identify the shape traced by the user). There can be a multitude of features, and the algorithm can use one, several, or all of the provided features to determine the corresponding area / shape on the functional surface.

[0046] According to an example, the features extracted from the time graph of the slope of the electrical signal are: (1) Duration of the time window, for each of the 2 or 3 time windows identified above determined on the time evolution graph of the slope of the electrical signal over time, (2) Maximum amplitude of the time graph of the electrical signal in each time window, (3) Maximum amplitude of the slope of the time graph of the slope of the electrical signal in each time window, (4) Ratios of slope amplitudes for each time window and for the different piezoelectric films (if 2 piezos, 3 ratios calculated, if 3 piezos, 9 ratios calculated) calculated on the time evolution graph of the slope of the electrical signal over time.

[0047] The characteristics thus obtained are put into vector form as input data for the classification algorithm.

[0048] For example, if a single piezoelectric film is used [Characteristic 1, Characteristic 2, ..., Characteristic N]. If two piezoelectric films are used [Characteristic 1- Piezo 1, Characteristic 2- Piezo 1, ..., Characteristic N- Piezo 1, Characteristic 1- Piezo 2, Characteristic 2- Piezo 2, ..., Characteristic N- Piezo 2]. If M piezoelectric films are used [Characteristic 1- Piezo 1, Characteristic 2- Piezo 1, ..., Characteristic N- Piezo 1, Characteristic 1- Piezo 2, Characteristic 2- Piezo 2, ..., Characteristic N- Piezo 2, Characteristic 1- Piezo M, Characteristic 2- Piezo M, ..., Characteristic N- Piezo M].

[0049] The classification algorithm will have as many classes as there are zones / shapes to detect. For example, for a 9-digit keypad, there will be 9 classes.

[0050] In one embodiment, the classification algorithm is a set-theoretic algorithm. In another embodiment, the classification algorithm is a boosting-type set-theoretic algorithm.

[0051] A boosting algorithm is a machine learning technique that combines the predictions of several weak or basic models to create a stronger and more accurate predictive model. Boosting belongs to the ensemble learning methods, where multiple models are trained and combined to make better predictions than the individual models.

[0052] In boosting, the basic models are typically simple and weak, such as decision trees with limited depth or shallow neural networks. Each basic model is trained on a subset of the training data or with different weights assigned to the data points. The models are trained iteratively, with each subsequent model focusing on the examples that were misclassified by previous models.

[0053] During the training process, reinforcement assigns higher weights to misclassified examples, effectively giving them more importance. Subsequent models are then trained to rank the misclassified examples, attempting to correct the errors made by previous models. This iterative process continues until a predefined stopping criterion is met, such as a maximum number of models or a desired level of accuracy.

[0054] The final prediction is made by combining the predictions of all the base models, usually through a weighted voting system. The weights are determined based on each base model's performance during training. The idea behind boosting is that by combining several weak models, each focusing on different aspects of the data, the overall model can capture complex relationships and improve predictive accuracy.

[0055] Popular boosting algorithms include AdaBoost (Adaptive Boosting), Gradient Boosting, and XGBoost (Extreme Gradient Boosting).

Claims

Claims 1. A data input device (10) comprising: a. A functional surface (12) adapted to receive pressure from a user, such as pressure from a finger (14) of a user, said functional surface (12) forming, in use, an external surface of a motor vehicle; b. A piezoelectric film (16) having at least one sensitivity axis (30, 34) and being arranged below the functional surface and adapted to emit an electrical signal as a function of the pressure received by the functional surface; and c. A computing unit (18) configured to: i. Receive the signal generated by the piezoelectric film in response to the pressure on the functional surface; and ii. Determine from this signal an area and / or a shape of the functional surface pressed by the user using a classification algorithm, the computing unit (18) being configured to: - determine a temporal evolution of the slope of said signal generated by the piezoelectric film, - define, on this temporal evolution of the slope, time windows (T1, T2, T3) corresponding respectively to phases of pressing, releasing and optionally rebounding on the functional surface (12), - for each time window (T1, T2, T3), extract characteristics of said temporal evolution of the slope, and put these characteristics in vector form as input data for the classification algorithm.

2. Device according to claim 1, in which the shape is a point, a line or a two-dimensional figure.

3. Device according to the preceding claim, in which the calculation unit is configured to determine from the signal generated by the piezoelectric film the time sequence of pressing of the functional surface using a classification algorithm.

4. Device according to one of the preceding claims, in which the input device is a keyboard (24), the functional surface includes a plurality of zones (26), each zone corresponding to a key, and the calculation unit is configured to determine from this signal the key corresponding to the zone of the functional surface pressed by the user.

5. Device according to claim 4, in which the keys are arranged in a line and the piezoelectric film is a single piezoelectric film having a sensitivity axis arranged parallel to the line of keys.

6. Device according to claim 4, in which the keys are arranged in a matrix according to rows and columns, and the piezoelectric film is a single piezoelectric film having at least two non-parallel sensitivity axes.

7. Device according to claim 4, in which the keys are arranged in a matrix according to rows and columns, and the piezoelectric film consists of a first piezoelectric film (28) having a first axis of sensitivity (30) and a second piezoelectric film (32) having a second axis of sensitivity (34), the first piezoelectric film being arranged relative to the second piezoelectric film so as to have the first axis of sensitivity at a non-zero angle, preferably at 90 degrees, from the second axis of sensitivity.

8. Device according to the preceding claim, in which the first piezoelectric film is arranged opposite the functional surface so as to have the first sensitivity axis parallel to the rows of the key matrix, and the second piezoelectric film is arranged opposite the functional surface so as to have the second sensitivity axis parallel to the columns of the key matrix.

9. Device according to one of the preceding claims, in which the classification algorithm is of the set type.

10. Device according to the preceding claim, in which the classification algorithm is of the boosting type, for example Adaptive Boosting, Gradient Boosting, or Extreme Gradient Boosting.

11. Device according to one of the preceding claims, in which the piezoelectric film is directly bonded to the functional surface.

12. Motor vehicle comprising the data input device according to one of claims 1 to 11.

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