Method for recognizing a predetermined gesture

Abstract

A method for recognizing a predetermined gesture, such as a kick (16) or hand movement, intended to control the opening or closing, and / or the locking or unlocking of a motor vehicle opening, the method being implemented by a computing unit (22). The method comprises obtaining an electrical signal from a non-contact motion sensor (18), the electrical signal corresponding to a gesture; determining at least three windows in the electrical signal corresponding to the approach, stop, and withdrawal phases of the gesture; determining the value of at least one characteristic associated with the electrical signal for each of the time windows; and using a boosting-type classification algorithm to determine whether the gesture belongs to one of the following two classes: a positive event indicating that the gesture corresponds to the predetermined gesture, and a negative event indicating that the gesture does not correspond to the predetermined gesture.Figure from the summary: Figure 1.

Description

Title of the invention: Method for recognizing a predetermined gesture Technical field

[0001] The present disclosure relates to the field of methods for recognizing a predetermined gesture, intended to control the opening or closing, and / or the locking or unlocking of an opening of a motor vehicle, and using movement sensors based on contactless detection, in particular sensors of body movements such as a kick. Prior art

[0002] A predetermined gesture sensor, in particular a sensor capable of recognizing a kick-type gesture, may allow a user to activate or control certain vehicle functions, such as opening the trunk of a car by performing a movement or gesture with their foot. This is a form of hands-free interaction designed to provide convenience and ease of use.

[0003] Instead of using a physical button on the car or on its key fob, the user can activate the mechanism by simply kicking under a specific area of ​​the rear bumper at the trunk, where the sensor is located. The sensor detects the movement and triggers the unlocking or opening of the trunk.

[0004] The purpose of a kick sensor is to allow the user to access the cargo area of ​​the vehicle without requiring them to put their belongings down or physically interact with the car, such as when their hands are full with grocery bags.

[0005] While kick sensors can be convenient and provide hands-free access, they also have some drawbacks, particularly regarding sensitivity. Kick sensors can sometimes be too sensitive or not sensitive enough, and can then misinterpret other movements or objects and lead to unintended activations. For example, if the user or a passerby accidentally brushes against the sensor area while walking past the vehicle, this can trigger the trunk to open or close.

[0006] It would therefore be beneficial to have a kick sensor that is more reliable in its movement detection. Summary

[0007] The present disclosure improves the situation.

[0008] A method is proposed for recognizing a predetermined gesture such as a kick or a hand strike, intended to control the opening or closing, and / or the locking or unlocking an opening of a motor vehicle. The method is implemented by a computing unit and comprises obtaining an electrical signal from a contactless motion sensor, the electrical signal corresponding to a gesture; determining at least three time windows in the electrical signal, said at least three time windows corresponding to phases of approach, stop and withdrawal of the gesture; determining the value of at least one characteristic associated with the electrical signal for each of said at least three time windows; forming a vector comprising the values ​​of said at least one characteristic of the electrical signal for each of said at least three time windows;and using a boosting-type classification algorithm to determine from the vector whether the gesture belongs to one of the following two classes: positive event indicating that the gesture matches the predetermined gesture, and negative event indicating that the gesture does not match the predetermined gesture.;

[0009] Gesture sensors, including kick sensors, are traditionally developed with capacitive technology, meaning it uses capacitive touch technology to detect and interpret kick gestures or foot movements. Capacitive sensors work on the principle of capacitance, which is the ability of an object to store an electrical charge. In the context of a kick sensor, capacitive technology is used to create an invisible capacitive field in a specific area, typically near the rear bumper of a vehicle. When a person's foot enters or interacts with this field, it disrupts the electrical charge and the sensor can detect the change in capacitance.

[0010] More recently, a new sensor technology based on radar technology (24 GHz or UWB) has been coupled with a naive algorithm. A radar-based kick sensor uses the principles of radar (radio waves) to detect and interpret kicking gestures or foot movements. The sensor emits radio waves and analyzes their reflections to detect the presence and movement of a person's foot. The naive algorithm is designed to extract the signal characteristics associated with the reflected radio waves and compares them to a threshold (min / max) that is representative of a positive gesture of movement of a person's foot.

[0011] The main weakness of the naive algorithm is the lack of robustness with respect to abuses and in particular to the involuntary passer-by (in the case of a foot sensor). Indeed, the diversions and the events to be detected often have the same characteristic values ​​if we look at them independently.

[0012] The proposed method makes it possible to go further in the analysis of the signal by dividing the signal into time windows, and by using characteristics of each time window by a supervised learning algorithm, which thus classifies the gesture as positive or negative of a movement of a person's foot according to these features.

[0013] The method presented proposes to use a combination of feature values ​​to solve the classification problem. Instead of having all the features tested in parallel as in the case of using a naive algorithm, supervised learning makes it possible to design a sequence of feature tests coupled with a weighting of the results of each test to split the classes to be sorted as much as possible.

[0014] The features set out in the following paragraphs may, optionally, be implemented, independently of one another or in combination with one another:

[0015] - said at least one characteristic of the electrical signal for each of ... at least three time windows include at least two of the following characteristics: - the duration of the approach time window; - the duration of the shutdown time window; - the duration of the withdrawal time window; - the sum of the durations of the approach, stop and withdrawal time windows; - the number of points in the approach time window that have a positive phase shift; - the number of points in the withdrawal time window that have a negative phase shift; - the ratio of the number of points in the approach time window that have a positive phase shift to the duration of the approach time window; - the ratio of the number of points in the withdrawal time window that have a negative phase shift to the duration of the withdrawal time window; - number of absolute variations of the signal determined by incrementing a counter by the absolute value of the difference in amplitude between two consecutive points, each time the absolute value of the difference in amplitude between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - number of absolute variations in the signal period determined by incrementing a counter by the absolute value of the period difference between two consecutive points, each time the absolute value of the period difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - number of absolute variations of the phase of the signal determined by incrementing a counter by the absolute value of the phase difference between two consecutive points, each time the absolute value of the phase difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - ratio of the average signal amplitude in the approach time window to the average signal amplitude in the withdrawal time window.

[0016] - the motion sensor is a foot motion sensor, arranged in use lisation under a car trunk, the sensor including two antennas, a first antenna directed in use towards a rear of the car, and a second antenna directed in use towards an underside of the car, and wherein said at least one characteristic of the electrical signal for each of said at least three time windows comprises: - the ratio of the average of the signal amplitude in the approach time window for the first antenna to the average of the signal amplitude in the approach time window for the second antenna; and - the ratio of the average signal amplitude in the withdrawal time window for the first antenna to the average signal amplitude in the withdrawal time window for the second antenna.

[0017] - determining from the vector whether the gesture belongs to one of the two classes includes the determination of a weight associated with the gesture from the characteristic vector and the comparison of the weight with a threshold value delimiting whether or not it belongs to a positive stroke event.

[0018] - the classification algorithm is of the boosting type, for example AdaBoost (Adaptive Boosting), Gradient Boosting, or XGBoost (Extreme Gradient Boosting).

[0019] - the method further comprises a step of issuing an opening instruction or of closing, and / or locking or unlocking an opening of a motor vehicle, in response to the determination of the positive event indicating that the gesture corresponds to the predetermined gesture.

[0020] According to another aspect, a computer program is provided comprising instructions for implementing all or part of a method according to the invention when this program is executed by a processor.

[0021] According to another aspect, there is provided a non-transitory, computer-readable recording medium on which such a program is recorded.

[0022] According to another aspect, a method for opening a car trunk comprises using the above method for determining a gesture, and issuing an instruction for opening or closing, and / or locking or unlocking the trunk in response to determining the positive event indicating that the gesture corresponds to the predetermined control gesture. According to one example, issuing the instruction is performed by a computing unit separate from the computing unit implementing the gesture determination method.

[0023] According to another aspect, there is provided a car comprising the above non-transitory recording medium. Brief description of the drawings

[0024] Other characteristics, details and advantages will appear on reading the detailed description below, and on analyzing the attached drawings, in which: Fig.l

[0025] [Fig.l] shows an illustration of a user opening a car trunk with a foot gesture; Fig. 2

[0026] [Fig.2] shows an example of an electrical signal from the contactless gesture sensor following the detection of a gesture by an antenna, the signal being decomposed into a real part (Q) and an imaginary part (I); Fig. 3

[0027] [Fig.3] shows the phase, amplitude and period graphs of the signal of [Fig.2]; Fig. 4

[0028] [Fig.4] shows a division into time windows of a phase shift between the imaginary part (I) and the real part (Q) of the signal of [Fig.2] according to one embodiment; and Fig. 5

[0029] [Fig.5] shows an example of distribution into positive and negative events following the use of a signal boosting type classification algorithm of [Fig.2]. Description of the embodiments

[0030] Reference is now made to [Fig.l] which shows a user 10 opening the trunk 12 of a vehicle 14, in this case a car, by means of the action of his foot 16. For this, a contactless sensor 18 is arranged at the rear of the car below the trunk 12 at the bumper. It is by passing his foot 16 in front of the sensor 18 following a kicking gesture, that a trunk opening system 20 detects the kick and controls the opening of the trunk 12. The system 20 includes the sensor 18 and a calculation unit 22 which comprises a boosting-type classification algorithm in order to determine whether the gesture detected by the sensor 18 is actually a kicking gesture.

[0031] The contactless sensor 18 may be, according to one example, of two types. It may have one detection antenna (such as a UWB sensor) or two detection antennas (for example, a 24 GHz sensor). In the case where the sensor 18 has two detection antennas, the sensor 18 may have a first antenna 18a directed towards the rear of the car 14 (axis A1), and a second antenna 18b directed towards the underside of the car 14 (axis A2). These two antennas combined make it possible to detect the movement of the foot 16. The sensor 18 receives from each antenna 18a, 18b an electrical signal corresponding to the detected movement. This signal comprises a real part I and an imaginary part Q which have a time shift P between them.

[0032] The method 30 for determining a gesture event will now be described. The method makes it possible to determine whether the gesture recorded by the sensor 18 corresponds to a predetermined control gesture. The predetermined gesture is preferably a "kick", i.e. a swing of the foot forward then back to its initial position. In variants, the gesture could be more complex, but there will advantageously be an approach phase (of the vehicle 14), a stop phase and a withdrawal phase (backing away from the vehicle 14). The gesture could also be a hand gesture, having similar characteristics, possibly associated with functions of the vehicle 14 other than opening the trunk.

[0033] The method 30 uses the calculation unit 22 to analyze the signals received by the contactless sensor 18 and determine whether there has indeed been a predetermined gesture, i.e. a kick. The classification carried out by the method 30 makes it possible to avoid false positives or false negatives.

[0034] In a first step, an electrical signal corresponding to a gesture detected by the sensor 18 is obtained. [Fig.2] illustrates an example of a signal obtained for an antenna 18a or 18b. This signal comprises a real part (Q) and an imaginary part (I). [Fig.3] shows the phase, amplitude and period variations of the signal.

[0035] In a second step, processing of the received signal is carried out. From the signal, at least three time windows are determined T1, T2, T3. These three time windows correspond to phases of approach, stop, and withdrawal of the gesture, that is to say phases of approach, stop, and withdrawal of the foot in front of the sensor 18. In order to determine these windows, the phase shift between the imaginary part (I) and the real part (Q) of the signal is analyzed. During a gesture, the phase is stable positive, then undergoes a loss of value, an inflection point appears and the phase becomes strongly negative, then drops again and stabilizes. The approach time window T1 will therefore correspond to a time window where the phase is positive and stable.The stop time window T2 will be the time window adjacent to the approach time window T1, and will therefore correspond to a time window where the phase undergoes a loss of value, an inflection point appears and the phase becomes strongly negative, then decreases again. The withdrawal time window T3 will be the time window adjacent to the stop time window T2, and will therefore correspond to a time window where the phase is negative and stable. A signal processing algorithm can be used to determine these three time windows.

[0036] There could be more than three time windows. For example, the clipping could include an additional window before the approach window T4 shown in [Fig.4], and an additional window after the T5 withdrawal window shown in [Fig.4],

[0037] In a third step, the values ​​of at least one characteristic of the electrical signal are determined for each of said at least three time windows. The characteristics may be the following: - the duration of the approach time window; - the duration of the shutdown time window; - the duration of the withdrawal time window; - the sum of the durations of the approach, stop and withdrawal time windows; - the number of points in the approach time window that have a positive phase shift; - the number of points in the withdrawal time window that have a negative phase shift; - the ratio of the number of points in the approach time window that have a positive phase shift to the duration of the approach time window; - the ratio of the number of points in the withdrawal time window that have a negative phase shift to the duration of the withdrawal time window; - number of absolute variations of the signal (in full, i.e. including real part and imaginary part) determined by incrementing a counter by the absolute value of the difference in amplitude between two consecutive points, each time the absolute value of the difference in amplitude between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points. According to one example, this threshold value is 30; - number of absolute variations of the signal period determined by incrementing a counter by the absolute value of the period difference between two consecutive points, each time the absolute value of the period difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points. In one example, this threshold value is 2; - number of absolute variations in the phase of the signal determined by incrementing a counter by the absolute value of the phase difference between two consecutive points, each time the absolute value of the phase difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points. In one example, this threshold value is 2.

[0038] In the case where the sensor 18 has two antennas, the following characteristics can also be considered: - ratio of the average of the signal amplitude (in full, i.e. including real part and imaginary part) in the approach time window by the average of the amplitude of the signal in the removal time window, the signal having in one example been modified by a high-pass filter to center the signal at 0; - the ratio of the average of the signal amplitude in the approach time window for the first antenna to the average of the signal amplitude in the approach time window for the second antenna; and - the ratio of the average of the amplitude of the entire signal, i.e. including real part and imaginary part) in the withdrawal time window for the first antenna by the average of the amplitude of the signal in the withdrawal time window for the second antenna, the signal having according to one example been modified by a high-pass filter to center the signal at 0.

[0039] In a fourth step, a vector is formed from the values ​​of the characteristics of the electrical signal determined for each of the time windows. The vector will have the form of an N x 1 vector of dimension 1, with the N characteristics of this signal.

[0040] In a fifth step, a boosting-type classification algorithm determines from the vector whether the predetermined gesture belongs to one of the following two classes: positive event of predetermined gesture (the gesture is indeed a kick), and negative event not corresponding to the predetermined gesture (i.e. in this case, kick).

[0041] The classification algorithm is a two-class boosting type ensemble algorithm.

[0042] A boosting algorithm is a machine learning technique that combines the predictions of multiple weak or base models to create a stronger and more accurate prediction model. Boosting belongs to ensemble learning methods, where multiple models are trained and combined to make better predictions than individual models.

[0043] In boosting, base models are typically simple and weak, such as decision trees with limited depth or shallow neural networks. Each base model is trained on a subset of the training data or with different weights assigned to the data points. Models are trained iteratively, with each subsequent model focusing on examples that were misclassified by previous models.

[0044] During the training process, reinforcement assigns higher weights to misclassified examples, effectively giving them more importance. Subsequent models are then trained to prioritize 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 number maximum number of models or a desired level of precision.

[0045] 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 the performance of each base model during training. The idea behind boosting is that by combining multiple weak models, each focusing on different aspects of the data, the overall model can capture complex relationships and improve predictive accuracy.

[0046] Popular boosting algorithms include AdaBoost (Adaptive Boosting), Gradient Boosting, and XGBoost (Extreme Gradient Boosting).

[0047] With reference to [Fig.5], the classification algorithm will output a weight. If this weight is greater than a threshold value (shown in dotted lines), it will be concluded that a positive kick event has occurred. If this weight is less than the threshold value, it will be concluded that a negative non-kick event has occurred.

[0048] The threshold value is determined upstream of the method 30 during the training and calibration of the classification algorithm.

[0049] The classification algorithm may or may not use the N features provided to it to determine the weight of the event.

[0050] The use of the boosting type classification algorithm makes it possible to reduce the dimension of the gesture determination problem. If the characteristics had been taken in isolation, the problem could not have been solved. Thanks to boosting, sequential tests are carried out, which makes it possible to apply a weight, to have only one dimension, and finally to classify.

[0051] At the end of the method, if it is determined that the gesture corresponds to the predetermined control gesture (positive event), an instruction to open or close, and / or lock or unlock the trunk can be issued. If, on the contrary, it is determined that the gesture does not correspond to the predetermined control gesture (negative event), no instruction is issued.

[0052] The sending of the instruction can be controlled by the calculation unit 22 or another calculation unit connected to the calculation unit 22.

Claims

Claims

1. Method for recognizing a predetermined gesture, such as a kick (16) or hand strike, intended to control the opening or closing, and / or the locking or unlocking of an opening of a motor vehicle, the method being implemented by a calculation unit (22) and comprising: a. Obtaining an electrical signal from a contactless motion sensor (18), the electrical signal corresponding to a gesture; b. The determination of at least three time windows (T1, T2, T3) in the electrical signal, said at least three time windows corresponding to phases of approach, stop and withdrawal of the gesture; c. Determining the value of at least one characteristic associated with the electrical signal for each of said at least three time windows; d. Forming a vector comprising the values of said at least one characteristic of the electrical signal for each of said at least three time windows; and e. Using a boosting-type classification algorithm to determine from the vector whether the gesture belongs to one of the following two classes: positive event indicating that the gesture matches the predetermined gesture, and negative event indicating that the gesture does not match the predetermined gesture.

2. The method of claim 1, wherein said at least one characteristic of the electrical signal for each of said at least three time windows comprises at least two of the following characteristics: - the duration of the approach time window; - the duration of the shutdown time window; - the duration of the withdrawal time window; - the sum of the durations of the approach, stop and withdrawal time windows; - the number of points in the approach time window that have a

3. positive phase shift; - the number of points in the withdrawal time window that have a negative phase shift; - the ratio of the number of points in the approach time window that have a positive phase shift to the duration of the approach time window; - the ratio of the number of points in the withdrawal time window that have a negative phase shift to the duration of the withdrawal time window; - number of absolute variations of the signal determined by incrementing a counter by the absolute value of the difference in amplitude between two consecutive points, each time the absolute value of the difference in amplitude between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - number of absolute variations in the signal period determined by incrementing a counter by the absolute value of the period difference between two consecutive points, each time the absolute value of the period difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - number of absolute variations in the phase of the signal determined by incrementing a counter by the absolute value of the phase difference between two consecutive points, each time the absolute value of the phase difference between two consecutive points is greater than a statistically defined threshold corresponding to an abnormal increase in amplitude between two consecutive points; - ratio of the average signal amplitude in the approach time window to the average signal amplitude in the withdrawal time window. A method according to claim 1 or 2, wherein the motion sensor is a foot motion sensor, disposed in use below a car trunk, the sensor including two antennas, a first antenna directed in use towards a rear of the car, and a second antenna directed in use towards an underside of the car, and wherein said at least one characteristic of the electrical signal for each of said at least three time windows comprises: - the ratio of the average signal amplitude in the approach time window for the first antenna to the average signal amplitude in the approach time window for the second antenna; and - the ratio of the average signal amplitude in the withdrawal time window for the first antenna to the average signal amplitude in the withdrawal time window for the second antenna.

4. Method according to one of the preceding claims, in which the determination from the vector whether the gesture belongs to one of the two classes comprises the determination of a weight associated with the gesture from the vector of characteristics and the comparison of the weight with a threshold value delimiting the belonging or not to a positive stroke event.

5. Method according to one of the preceding claims, in which the classification algorithm is of the boosting type, for example Adaptive Boosting, Gradient Boosting, or Extreme Gradient Boosting.

6. Computer program comprising instructions for implementing the method according to one of claims 1 to 5 when this program is executed by a processor.

7. Non-transitory recording medium readable by a computer on which is recorded a program for implementing the method according to one of claims 1 to 5 when this program is executed by a processor.

8. Method for opening a trunk (12) of a car (14) comprising: using the method for determining a gesture according to one of claims 1 to 5, and issuing an instruction for opening or closing, and / or locking or unlocking the trunk in response to determining the positive event indicating that the gesture corresponds to the predetermined control gesture.

9. Opening method according to the preceding claim, in which the transmission of the instruction is carried out by a calculation unit separate from the calculation unit implementing the gesture determination method.

10. A car (14) comprising the non-transitory recording medium of claim 7.

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