Wrist-worn device identity authentication method based on vibration feedback

Abstract

The invention provides a wrist-worn device identity authentication method based on vibration feedback. A vibration signal is generated through equipment such as a smart watch, and vibration response data of a user is collected in real time by using a built-in accelerometer. The method comprises the steps of signal noise filtering, direction correction and normalization preprocessing, then vibration characteristics are extracted through multi-order difference, and a multi-branch convolutional neural network (MCNN) is used for characteristic extraction and fusion so as to capture vibration characteristics of different time scales. And finally, identity matching is carried out through a Softmax classifier. The method shows good stability and recognition precision in various wearing postures, hand action changes and complex use scenes, and has high anti-interference capability.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent wrist-worn devices and relates to a wrist-worn device identity authentication method based on vibration feedback. Background Art

[0002] With the rapid development of information technology and the increasing popularity of the Internet of Things (IoT), devices have become an indispensable part of modern life, especially wrist-worn devices such as smart watches and fitness trackers, which have gradually become the core carriers of personal digital life with their versatility and portability, such as real-time health monitoring, message notifications, mobile payments, navigation, and sports tracking. These devices can monitor users' health data such as heart rate, blood pressure, and sleep quality in real time, provide users with accurate health management services, and greatly improve users' quality of life and daily convenience by integrating multiple application functions.

[0003] However, as the functions of wrist-worn devices continue to expand and deepen, their security issues have become increasingly prominent. Smart watches and other wrist-worn devices store and transmit a large amount of sensitive information, including but not limited to users' health data, real-time location information, payment records, etc. Once this information is maliciously attacked or illegally obtained, it will pose a serious threat to the user's personal privacy and property security. Especially in the field of identity authentication, traditional authentication methods, such as passwords and PIN codes, are difficult to meet the dual needs of modern smart devices for high security and convenience due to their shortcomings such as easy to crack and easy to forget.

[0004] Currently, the closest existing technologies focus on two authentication methods: biometrics and vibration feedback. Although biometric technologies, such as fingerprint recognition, facial recognition, and iris scanning, have been widely used in various smart devices and provide high security due to their characteristics based on the unique physiological characteristics of users, they face significant environmental limitations in practical applications. For example, the accuracy of fingerprint recognition will drop significantly when the fingers are wet, dirty, or injured; the recognition effect of facial recognition will be significantly weakened in low light, when the user wears a mask, or when the facial expression changes. In addition, once biometric data is stolen or abused, it will bring irreversible security risks, and these technologies often rely on specific hardware support, which increases the complexity and manufacturing cost of the equipment.

[0005] Another type of existing technology, namely the identity authentication method based on vibration feedback, attempts to achieve identity authentication by detecting the user's response to vibration stimulation and has achieved certain research results, but it is still in the exploratory stage, and its recognition accuracy and stability need to be further improved. For example, the existing vibration feedback identity authentication system may require users to perform specific actions or patterns, which not only increases the user's operating burden, but may also affect the convenience of authentication and user experience. Secondly, existing methods usually rely on a combination of multi-axis vibration data and complex two-factor models. Although they can achieve identity authentication, they are easily affected by changes in wearing position and noise interference in actual applications, and have poor adaptability to natural gestures and daily activities. Summary of the invention

[0006] In order to solve the above technical problems, the present application provides a wrist-worn device identity authentication method based on vibration feedback. Compared with the existing methods, the present invention shows obvious advantages in feature extraction, anti-interference and resource consumption.

[0007] In order to achieve the above objectives, this application adopts the following technical solutions:

[0008] A wrist-worn device identity authentication method based on vibration feedback comprises the following steps:

[0009] Generation and collection of vibration signals: generating vibration feedback signals using a built-in vibration motor of the wrist-worn device, and collecting the vibration feedback signals in real time using a built-in accelerometer of the wrist-worn device;

[0010] Signal preprocessing: Noise filtering: Use a Butterworth bandpass filter to filter the vibration feedback signal, use a cutoff frequency of 10Hz to 49.8Hz and filter the vibration feedback signal outside this range; Direction correction: By correcting the signal direction on the three axes of the accelerometer, ensure that the collected vibration feedback signal is consistent regardless of the user's wearing posture; Normalization: Normalize the amplitude range of the vibration signal that has been noise filtered and direction corrected, in order to standardize the vibration data of different time periods and different devices so that they have the same scale and distribution;

[0011] Differential feature extraction: Perform first-order, second-order, fourth-order, and eighth-order differential processing on the preprocessed vibration signal to extract the changing characteristics of the vibration signal in different time periods;

[0012] Multi-branch convolutional neural networks (MCNN) processing: The extracted differential features are input into MCNN; convolution kernels of different sizes are used to capture short-term and long-term vibration features; each branch extracts features of different time scales through the convolution layer and uses 64 filters; the ReLU activation function is used for nonlinear processing; the convolution output is converted into a one-dimensional vector through the flattening layer; in the feature fusion stage, the feature vectors from different branches are spliced ​​together using the Concatenate layer to form a comprehensive feature vector containing multi-scale features;

[0013] Classification and recognition: The comprehensive feature vector generated by the multi-branch convolutional neural network is input into the classifier through the fully connected layer, and the Softmax function is used to classify the results to determine whether the vibration response matches the registered user features; if it matches, the identity authentication is successful; if it does not match, the identity authentication fails.

[0014] In some embodiments, the vibration feedback signal is a signal generated after the original vibration signal of the vibration motor acts on the wrist.

[0015] In some embodiments, the accelerometer collects response data of the vibration feedback signal at a frequency of 100 Hz in real time, and each sampling lasts for 3 seconds.

[0016] In some embodiments, the formula for correcting the direction of the accelerometer signal on the three axes is as follows:

[0017] α′ i =(1-β)(α i -g i ),i=x,y,z#

[0018]

[0019] Among them, α i is the raw acceleration value, g i is the gravitational acceleration captured by the accelerometer along the i-th axis, and β is the filter factor determined by the filter's time constant t and the event transfer rate dT.

[0020] In some embodiments, the formula for normalizing the amplitude range of the vibration signal is:

[0021]

[0022] Among them, α' i is a single reading of the ith axis after orientation alignment, u i is the mean of the data along the same axis, δ iis the mean standard deviation of the data along the same axis; α″ i is the accelerometer data.

[0023] A readable storage medium stores a computing program, which, when executed by a processor, can implement any of the above-mentioned wrist-worn device identity authentication methods based on vibration feedback.

[0024] An electronic device comprises the above-mentioned readable storage medium.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The wrist-worn device identity authentication method based on vibration feedback in this invention collects the user's vibration signal in real time through a high-precision vibration sensor, and extracts discernible differential features through preprocessing steps such as noise filtering, normalization and direction correction. Subsequently, the designed MCNN model is used to train and identify the features, achieving high-precision identity authentication.

[0027] The core advantage of the method of the present invention lies in its multi-scale dynamic feature extraction capability, which can effectively cope with signal changes in complex usage scenarios, such as changes in user activity status and adjustments to the tightness of the wearable device. This not only greatly improves the accuracy and stability of identity recognition, but also enhances the security and reliability of the system, while reducing the reliance on additional hardware, bringing significant progress to the identity authentication technology of wrist-worn devices.

[0028] The method of the present invention can access a pre-collected challenge-response library, where the challenge refers to the vibration stimulus of the watch to the user, and the response is the collected user vibration. Due to the characteristics of the user's arm vibration, each response is unique to each challenge and user.

[0029] The present invention uses multi-order differential feature extraction and MCNN to capture subtle changes in vibration signals, effectively improving the authentication stability and anti-interference ability under different wrist activities and wearing tightness changes. Especially in the case of slight position changes, the present invention can still maintain an authentication accuracy rate of more than 97%, which is suitable for natural activities in daily life and does not require strict wearing conditions.

[0030] The present invention only needs to use three-axis acceleration data, which greatly reduces hardware dependence and computing resource consumption, and is easy to implement on common smart watch configurations. This design significantly optimizes resource utilization efficiency, making the method have obvious advantages in convenience, stability and feasibility of popularization and application.

[0031] The method of the present invention achieves accurate identity matching by comparing the vibration characteristics of the user with the vibration characteristics of multiple known users pre-registered in the system one by one. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 is a flowchart of the identity authentication method;

[0034] Figure 2 This is the MCNN structure and parameter diagram;

[0035] Figure 3 The accuracy of the authentication model at different sampling times provided in Example 1 of the present invention;

[0036] Figure 4 The authentication model loss value (Loss) at different sampling times provided in Example 1 of the present invention;

[0037] Figure 5 The Accuracy, Loss, Recall and F1-score of the authentication model after the device provided in Example 2 of the present invention is randomly moved on the original position baseline;

[0038] Figure 6 The authentication model Accuracy, Loss, Recall, and F1-score of the user's hand performing different habitual movements provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution, design method and advantages of the present invention clearer, the present invention is further described in detail by specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0041] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered as part of the specification.

[0042] refer to Figure 1-Figure 2The present invention provides a wrist-worn device identity authentication method based on vibration feedback, the method comprising the following steps:

[0043] S1. User-worn device: The user wears a smartwatch on his wrist, which is equipped with a built-in vibration motor and accelerometer. The main function of the accelerometer is to collect the vibration feedback signal from the vibration motor. When the vibration motor sends out the original vibration signal through the user's wrist, the signal will be transmitted according to the physical characteristics of the wrist, such as bone structure, muscle tissue and skin. Given that each person's wrist structure, including bone density and muscle distribution, is unique, the original vibration signal will experience different degrees of absorption, reflection and attenuation during the transmission process, thereby forming a vibration feedback signal with individual specificity.

[0044] The present invention can capture these differences in vibration response caused by differences in wrist structure and convert them into data that characterizes the unique physical and physiological characteristics of the user's wrist. By analyzing this data, the system can accurately verify the user's identity. To ensure the accuracy of identity verification, the user needs to keep his position stable when wearing the device so that the accelerometer can accurately and effectively capture the vibration feedback signal.

[0045] S2. Generation and collection of vibration signals: The device generates vibration feedback signals through the built-in vibration motor, and sets the data sampling frequency to about 100Hz. The accelerometer then records the response data of the vibration feedback signal in real time. The accelerometer records in detail the timestamp and the acceleration data of the three axes of X, Y, and Z. After analyzing these data, the system can identify the key features of each user. Each sampling process lasts about 1 to 5 seconds. This duration ensures that the system can collect enough data for effective identity authentication.

[0046] S3. Signal preprocessing:

[0047] Noise filtering: Use a Butterworth bandpass filter to filter the vibration feedback signal and remove the frequency bands below 10Hz and above 49.8Hz to eliminate interference from the external environment.

[0048] Direction correction: In order to solve the signal difference caused by the change of wrist wearing posture, the system corrects the signal direction on the three axes of the accelerometer to ensure the consistency of the collected vibration signal. The formula is as follows:

[0049] α′ i =(1-β)(α i -g i ),i=x,y,z#

[0050]

[0051] Among them, α iis the raw acceleration value, g i is the gravitational acceleration captured by the accelerometer along the i-th axis, and β is the filter factor determined by the filter's time constant t and the event transfer rate dT.

[0052] Normalization: Since the vibration signal collected by the accelerometer of the wrist-worn device is a three-axis acceleration along the x, y, and z axes, the value ranges between the three axes vary greatly, and the value ranges between different device models vary even more. Therefore, the amplitude range of the vibration signal is normalized to ensure data consistency for different users, different devices, and different usage scenarios. The formula is as follows:

[0053]

[0054] Among them, α' i is a single reading of the ith axis after orientation alignment, u i and δ i are the mean and standard deviation of the data along the same axis respectively. After normalization, the accelerometer data (α″ i ) is centered at 0 and scaled to have a standard deviation of 1. Thus, data from different datasets will be comparable.

[0055] S4. Differential feature extraction: Perform first-order, second-order, fourth-order, and eighth-order differential processing on the preprocessed vibration signal to extract the changing characteristics of the signal in different time periods. Each order of differential captures the dynamic changes of the signal and enhances the system's perception of the wrist vibration pattern. The formula is as follows:

[0056] Diff n (t) = x(t) - x(tn)#

[0057] Among them, Diff n (t) represents the nth order difference, and x(t) is the signal value at time point t.

[0058] S5. Multi-branch convolutional neural networks (MCNN) processing: The differential features are input into a multi-branch convolutional neural network, and different convolution kernels (sizes of 1, 2, 4, 8, 16, 32) are used to capture short-term and long-term vibration features. Each branch extracts features of different time scales through the convolution layer to ensure that the model can accurately understand the complex changes of the vibration signal. Each branch uses 64 filters and uses the ReLU activation function for nonlinear processing, and then the convolution output is converted into a one-dimensional vector through the flattening layer. In the feature fusion stage, the Concatenate layer is used to splice the feature vectors from different branches together to form a comprehensive vector containing multi-scale features.

[0059] S6: Classification and recognition: The comprehensive feature vector generated by the convolutional network is input into the classifier through the fully connected layer, and the Softmax function is used to classify the results to determine whether the vibration response matches the registered user features. If they match, the authentication is successful and the device allows the user to access or operate; if they do not match, the system prompts that the authentication has failed.

[0060] Example 1: Verification of the impact of training dataset size on model performance

[0061] In order to verify the impact of different training data set sizes on the performance of the identity recognition model, this embodiment tests the balance between the authentication accuracy and computing resource consumption of the system at different sampling times under the condition that the device sampling frequency is 100Hz. It should be noted that the identity authentication method of the present invention can be performed on a variety of smart wrist-worn devices, such as smart watches, smart glasses, and smart phones, which further expands the applicability of the method.

[0062] Experimental setup:

[0063] Device selection and wearing position: This embodiment was tested on the first generation of Xiaomi Watch smartwatch. The experiment required the user to wear the smartwatch on the left wrist with the back of the hand facing up and keeping the wrist stable to ensure the consistency and comparability of data collection.

[0064] Data collection tools: The experiment uses a self-developed Android application to extract data from the accelerometer of the smart watch to ensure the flexibility and accuracy of the data collection process. This application can capture the acceleration response values ​​of the three axes of x, y, and z to generate vibration feature data for model training.

[0065] Dataset setting: Four users were randomly selected and their vibration feedback data at different sampling times were collected to provide support for data sets of different sizes. The sampling time was set to 1 second, 2 seconds, 3 seconds, 4 seconds, and 5 seconds. The purpose was to observe the impact of the change in data set size on the model training effect and find the optimal sampling time.

[0066] Vibration signal acquisition and model training:

[0067] Signal acquisition: Vibration stimulation is applied to the user through the built-in vibration motor of the smart watch as a challenge signal; after vibration stimulation, the accelerometer data acquisition application obtains vibration response data with a sampling frequency of 100Hz. The data records the acceleration response values ​​of the acceleration sensor on the x, y, and z axes for each sampling.

[0068] Model processing: MCNN is used for training, Softmax classifier is used to classify feature vectors, the extracted vibration features are compared with the features of multiple pre-registered known users, and a 1:N matching method is used to achieve accurate identity matching.

[0069] Experimental results analysis:

[0070] Certification accuracy: Based on Figure 3 The results show that as the sampling time increases from 1 second to 3 seconds, the authentication accuracy gradually increases, reaching 97.5% at 3 seconds, indicating that the model has sufficient feature information for efficient authentication; after extending the sampling time to 4 seconds and 5 seconds, the accuracy no longer improves, but increases the computational burden. Therefore, 3 seconds is the optimal sampling time, indicating that a 3-second time segment contains enough feature information to achieve efficient and accurate identity authentication with less data.

[0071] Model loss value: According to Figure 4 From the results, we can see that as the sampling time increases from 1 second to 3 seconds, the loss value gradually decreases, reaching a minimum of 0.12 at 3 seconds, indicating that this sampling time can provide sufficient and effective feature information for the model, thereby significantly reducing the error. When the sampling time is extended to 4 seconds and 5 seconds, the loss value increases slightly, probably due to the increased computational burden caused by the introduction of redundant information. This shows that a sampling time of 3 seconds achieves an ideal balance between feature extraction efficiency and model performance.

[0072] This experiment shows that a sampling time of 3 seconds achieves a balance between the authentication performance and computing resource consumption of the model, with a high authentication accuracy and a low loss value. This conclusion highlights the unique advantages of the wrist-worn device identity authentication method based on vibration feedback in the data collection time setting, and provides an efficient and low-resource solution for practical applications, which helps to improve the overall performance of the model and optimize resource usage.

[0073] Example 2: Anti-interference capability test in complex environment

[0074] This example aims to verify the anti-interference ability of the wrist-worn device identity authentication method based on vibration feedback of the present invention in complex environments that users may encounter in their daily lives, and compares the authentication accuracy and robustness under different conditions by introducing a control group. The specific test steps are as follows:

[0075] Test environment setup:

[0076] Experimental group (with interference): simulates the wrist movements of users in daily life, such as walking, slight swinging or adjusting the wearing position, which causes a small displacement of the smartwatch on the wrist. The specific operation is to randomly move the watch up and down along the wrist by about one centimeter to test the robustness of the authentication algorithm under unstable wearing position.

[0077] Control group (no interference): The user keeps the wrist still and wears the smartwatch in a fixed position on the wrist to avoid position changes. This set of test conditions is designed to observe the authentication accuracy of the algorithm in the no-interference state as a baseline comparison.

[0078] Data collection and authentication process:

[0079] Data collection: Vibration feedback data from the user's wrist was collected under two conditions. In the experimental group, the watch was kept in each moving position for a few seconds to ensure that the data covered the vibration response of different wearing positions. In the control group, the watch position was kept unchanged.

[0080] Authentication process: The collected data is preprocessed, including noise filtering, direction correction and normalization. The processed data is added with differential features and input into the MCNN model for training. The user is authenticated under two sets of conditions.

[0081] Results and analysis: By comparison Figure 5 The authentication accuracy of the experimental group and the control group can be concluded that even if the watch moves randomly on the wrist, the authentication algorithm can still maintain an authentication accuracy of more than 97%, indicating that the algorithm still has a high degree of recognition stability and anti-interference ability under slight position changes. This result verifies the reliability of the present invention in practical applications, especially when the user moves or adjusts the position of the device, it can still effectively perform identity recognition.

[0082] Example 3: Adaptability test under different hand movements

[0083] This embodiment aims to verify the adaptability of the wrist-worn device identity authentication method based on vibration feedback of the present invention under changes in user hand movements, and to evaluate its stability and accuracy under different gestures.

[0084] Test environment settings: Simulate the natural gesture changes in users' daily lives and design several typical hand movements, including:

[0085] Relax your palm naturally: The user's hands are completely relaxed without any specific movements.

[0086] Half-clenched fist: The palms are slightly closed, in a half-clenched fist state.

[0087] Thumbs up: Relax your palm naturally with your thumb pointing upwards.

[0088] In each gesture, the smartwatch was worn on the user’s left wrist to ensure consistency in the data collection location.

[0089] Data collection and authentication process: Under the above gestures, the vibration motor of the smartwatch is used to generate vibration signals, and the accelerometer is used to collect the user's vibration feedback data. The collected signals are preprocessed (including noise filtering, direction correction and normalization) to eliminate the data differences that may be caused by different gestures and ensure data consistency.

[0090] Results and analysis: According to Figure 6 The result analysis found that the authentication accuracy under different gestures remained above 97%, with a specific accuracy of 97.49% and a loss value of 0.11. There were no significant differences in the evaluation indicators (such as accuracy and loss value) under different gestures. This result shows that the identity authentication method of the present invention can still maintain a high recognition accuracy rate when the natural movements of the user's hands change, fully demonstrating good adaptability.

[0091] It should be noted that although the above describes the various steps in a specific order, it does not mean that the various steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order as long as the required functions can be achieved.

[0092] The present invention may be a system, a method and / or an electronic product. The electronic product may include a readable storage medium carrying readable program instructions for causing a processor to implement various aspects of the present invention.

[0093] The readable storage medium can be a tangible device that holds and stores instructions used by the instruction execution device. The readable storage medium can include, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a protruding structure in a groove on which instructions are stored, and any suitable combination thereof.

[0094] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A wrist-worn device identity authentication method based on vibration feedback, characterized in that: The following steps are involved: Generation and collection of vibration signals: generating vibration feedback signals using a built-in vibration motor of the wrist-worn device, and collecting the vibration feedback signals in real time using a built-in accelerometer of the wrist-worn device; Signal preprocessing: Noise filtering: Use a Butterworth bandpass filter to filter the vibration feedback signal, use a cutoff frequency of 10Hz to 49.8Hz and filter the vibration feedback signal outside this range; Direction correction: Correct the signal direction on the three axes of the accelerometer; Normalization processing: Normalize the amplitude range of the vibration signal that has been noise filtered and direction corrected; Differential feature extraction: Perform first-order, second-order, fourth-order, and eighth-order differential processing on the preprocessed vibration signal to extract the changing characteristics of the vibration signal in different time periods; Multi-branch convolutional neural network processing: The extracted differential features are input into MCNN; convolution kernels of different sizes are used to capture short-term and long-term vibration features; each branch extracts features of different time scales through the convolution layer and uses 64 filters; the ReLU activation function is used for nonlinear processing; the convolution output is converted into a one-dimensional vector through the flattening layer; in the feature fusion stage, the feature vectors from different branches are spliced ​​together using the Concatenate layer to form a comprehensive feature vector containing multi-scale features; Classification and recognition: The comprehensive feature vector generated by the multi-branch convolutional neural network is input into the classifier through the fully connected layer, and the Softmax function is used to classify the results to determine whether the vibration response matches the registered user features; if it matches, the identity authentication is successful; if it does not match, the identity authentication fails.

2. The wrist-worn device identity authentication method based on vibration feedback according to claim 1, characterized in that: The vibration feedback signal is a signal generated after the original vibration signal of the vibration motor acts on the wrist.

3. The wrist-worn device identity authentication method based on vibration feedback according to claim 1, characterized in that: The accelerometer collects response data of the vibration feedback signal at a frequency of 100 Hz in real time, and each sampling lasts for 3 seconds.

4. The wrist-worn device identity authentication method based on vibration feedback according to claim 1, characterized in that: The formula for correcting the signal direction on the three axes of the accelerometer is as follows: a' i =(1-b)(a) i -g i ),i=x,y,z# Among them, α i is the raw acceleration value, g i is the gravitational acceleration captured by the accelerometer along the i-th axis, and β is the filter factor determined by the filter's time constant t and the event transfer rate dT.

5. The wrist-worn device identity authentication method based on vibration feedback according to claim 1, characterized in that: The formula for normalizing the amplitude range of the vibration signal is: Among them, α' i is a single reading of the ith axis after orientation alignment, u i is the mean of the data along the same axis, δ i is the mean standard deviation of the data along the same axis; α″ i is the accelerometer data.

6. A readable storage medium having a computing program stored thereon, characterized in that: When the program is executed by a processor, it can implement the wrist-worn device identity authentication method based on vibration feedback as described in any one of claims 1 to 5.

7. An electronic device, characterized in that: Includes the readable storage medium as described in claim 5.

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