A mobile phone screen touch sensitivity evaluation system

Abstract

The application relates to the field of mobile device touch technology, and discloses a mobile phone screen touch sensitivity evaluation system. The system records the touch events of the user in real time through a data acquisition module, calculates the touch duration and moving distance according to the touch events through a data processing module, realizes quantitative analysis of the touch events, comprehensively evaluates the accuracy and response time of the touch events through a sensitivity evaluation module, generates a touch sensitivity score, and identifies the touch mode of the user. This systematic evaluation method not only improves the accuracy and reliability of the evaluation results, but also provides personalized feedback and optimization suggestions according to the touch behaviors of different users, so as to help the user better understand and adapt to the touch technology. The final result display module clearly presents the evaluation report in the form of a chart, so that the user can intuitively understand the touch sensitivity situation, avoids errors caused by subjective evaluation of the user on the touch screen sensitivity, and improves the overall user experience and satisfaction.

Description

Technical Field

[0001] This invention relates to the field of mobile device touch technology, specifically to a mobile phone screen touch sensitivity evaluation system. Background Technology

[0002] With the widespread use of smartphones, touchscreens have become one of the primary methods of human-computer interaction. Users operate via touchscreens, such as swiping, clicking, and zooming, making it an essential part of daily life and work. The user experience of touchscreens directly impacts user efficiency, satisfaction, and loyalty. For example, when operating applications, playing games, or browsing the web, the sensitivity and response speed of the touchscreen significantly affect the user's intuitive experience and operational smoothness. Therefore, evaluating the sensitivity and performance of touchscreens has become a crucial task for improving user experience, ensuring a smooth and natural interactive experience.

[0003] Currently, various touchscreen technologies exist on the market, including capacitive, resistive, and optical touchscreens. Each technology has its own characteristics. Capacitive touchscreens are widely used in modern smartphones due to their high sensitivity and multi-touch capabilities, while resistive touchscreens still have unique advantages in specific application scenarios. These different technologies vary significantly in touch sensitivity, response speed, and anti-interference capabilities. However, existing evaluation methods often lack systematicity and scientific rigor, making it difficult to comprehensively reflect the actual performance of touchscreens. This leaves users without effective, quantifiable information on touch sensitivity when choosing devices, thus affecting their purchasing decisions.

[0004] Traditional touch sensitivity assessments rely primarily on user subjectivity, such as subjective evaluations of touchscreen sensitivity, accuracy, and response speed. This approach lacks objective data support, leading to insufficient accuracy and reliability of the assessment results. Because user subjective experience is influenced by various factors, including personal habits, usage environment, and psychological expectations, different users may have significantly different assessments of the same touchscreen. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a mobile phone screen touch sensitivity evaluation system. A data acquisition module records user touch events in real time, including touch location, pressure value, and timestamp, forming a detailed data foundation. A data processing module calculates touch duration and movement distance to achieve quantitative analysis of touch events. A sensitivity evaluation module comprehensively assesses the accuracy and response time of touch events, generating a touch sensitivity score and identifying the user's touch patterns. This systematic evaluation method not only improves the accuracy and reliability of the evaluation results but also provides personalized feedback and optimization suggestions based on different users' touch behaviors, helping users better understand and adapt to touch technology. Finally, a results display module clearly presents the evaluation report in chart form, allowing users to intuitively understand the touch sensitivity situation, avoiding errors caused by subjective evaluations of touch screen sensitivity, and improving the overall user experience and satisfaction.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a mobile phone screen touch sensitivity evaluation system, comprising a data acquisition module, a data processing module, a sensitivity evaluation module, and a result display module; The data acquisition module is used to receive and record the user's touch events on the mobile phone screen in real time, including touch start events and touch end events, and generate timestamps, while also recording the touch position coordinates and touch pressure values. The data processing module receives user touch events on the mobile phone screen acquired by the data acquisition module, calculates the touch duration and touch movement distance of the touch events, and normalizes the touch coordinates before transmitting them to the sensitivity evaluation module. The sensitivity evaluation module calculates the accuracy of all touch events and the average time from the start of the touch to the system response based on the data transmitted by the data processing module, and calculates the touch sensitivity score of the mobile phone screen to evaluate the touch sensitivity of the mobile phone screen. At the same time, based on the statistical analysis of touch events, it identifies the user's touch patterns and behavioral characteristics. The results display module integrates the evaluation results into a mobile phone screen touch sensitivity evaluation report, which is then presented to the user in the form of charts.

[0007] Preferably, the touch duration of the touch event is calculated using the following formula:

[0008] In the formula, Indicates the duration of the touch. Indicates the timestamp of the touch event ending. Indicates the timestamp of the touch start event.

[0009] Preferably, the formula for calculating the touch movement distance is as follows:

[0010] In the formula, Indicates the distance the touch gesture has traveled. Indicates the coordinates of the touch start point. Indicates the coordinates of the touch endpoint.

[0011] Preferably, the normalization formula for the touch coordinates is as follows:

[0012]

[0013] In the formula, , Indicates the original touch coordinates. , Indicates the screen width range. , Indicates the screen height range.

[0014] Preferably, the formula for calculating the average time of the system response is as follows:

[0015] In the formula, This represents the average time of the system response. This represents the total number of all detected touch events. Indicates the first System response time per touch. Indicates the index subscript.

[0016] Preferably, the accuracy calculation formula for the touch event is as follows:

[0017] In the formula, Indicates the accuracy of touch events. This indicates the number of touches that successfully completed the intended operation. This indicates the total number of touches.

[0018] Preferably, the formula for calculating the touch sensitivity score of the mobile phone screen is as follows:

[0019] In the formula, This indicates the score for the phone screen's touch sensitivity. Indicates the total number of touches. Indicates the first The distance a touch can move. Indicates the first System response time per touch. To represent a minimum value, avoid division by zero. Indicates the index subscript.

[0020] Preferably, the user's touch pattern is identified by applying the following formula:

[0021] In the formula, This indicates the touch mode recognition score. Indicators representing frequency distribution Indicates the distribution value of the movement path. and This represents the weighting coefficients between the frequency distribution index and the movement path distribution value obtained through training with the deep learning module.

[0022] Preferably, the identification of the behavioral features is achieved through the following formula:

[0023] In the formula, This indicates the complexity index of touch behavior. Indicates the first The duration of each touch. Indicates the first The distance a touch can move. This represents the adjustment coefficient. Indicates the total number of touches. Indicates the index subscript.

[0024] Preferably, the sensitivity assessment module further includes an environmental factor influence analysis unit, which assesses the impact of these environmental factors on touch sensitivity by combining touch event data of users under different lighting, temperature and humidity conditions, and automatically generates environmental adaptability suggestions in the final assessment results.

[0025] Compared with the prior art, the present invention provides a mobile phone screen touch sensitivity evaluation system, which has the following beneficial effects: This invention records user touch events in real time through a data acquisition module, including touch location, pressure value, and timestamp, forming a detailed data foundation. The data processing module calculates touch duration and movement distance to achieve quantitative analysis of touch events. The sensitivity evaluation module comprehensively evaluates the accuracy and response time of touch events, generates a touch sensitivity score, and identifies the user's touch patterns. This systematic evaluation method not only improves the accuracy and reliability of the evaluation results but also provides personalized feedback and optimization suggestions based on different users' touch behaviors, thereby helping users better understand and adapt to touch technology. Finally, the results display module clearly presents the evaluation report in chart form, allowing users to intuitively understand the touch sensitivity situation, avoiding errors caused by subjective evaluations of touch screen sensitivity, and improving the overall user experience and satisfaction. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the system flow of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] To address the issue that traditional touch sensitivity assessments rely heavily on user subjectivity and lack quantitative data support, resulting in insufficient accuracy and reliability of the assessment results, a mobile phone screen touch sensitivity assessment system is proposed. Please refer to [link / reference]. Figure 1 The system includes a data acquisition module, a data processing module, a sensitivity assessment module, and a results display module; The data acquisition module primarily employs a high-precision multi-channel sensor array, including a touch sensor array and a pressure sensor, to achieve real-time and comprehensive monitoring of user touch behavior. Specifically, it integrates high-frequency sampling hardware (e.g., a microcontroller or FPGA chip) capable of continuously capturing touch event information at the microsecond level. At the start of a touch event, the sensor detects changes in touch pressure and instantaneous changes in touch position, and the edge processing unit immediately generates a corresponding timestamp, recording the event's start time, coordinate position, and pressure value. Simultaneously, the pressure sensor digitizes the analog pressure signal via an analog-to-digital converter (ADC) to ensure data accuracy. At the end of a touch event, the sensor detects a pressure drop below a threshold, triggering an end event and generating a timestamp of the end time, along with the corresponding touch position and pressure parameters. Furthermore, the system utilizes a high-efficiency real-time operating system (RTOS) to ensure rapid data transmission and storage, synchronously storing touch event data (timestamp, coordinates, pressure) in high-speed storage (e.g., DDR memory). Interrupt-driven and multi-threaded processing mechanisms ensure the continuity and integrity of data acquisition. This multi-sensor fusion and high-precision time synchronization technology effectively improves the real-time performance, accuracy, and completeness of touch events, providing a solid foundation of data for subsequent sensitivity assessment. In the data processing module, advanced data processing technologies and algorithms are employed to efficiently and accurately analyze the touch event data transmitted from the data acquisition module. First, using a time synchronization mechanism and timing processing algorithm, the duration of each touch event is precisely calculated, i.e., by subtracting the touch start and end timestamps using a formula. This can reflect the length of time a user spends interacting with the screen, helping to assess touch response and operating habits. Secondly, the system combines spatial coordinate data and utilizes the Euclidean distance formula. The system calculates the distance traveled with each touch, revealing not only the range of finger movement and operational complexity but also providing quantitative indicators for subsequent behavioral analysis. To ensure consistency across different devices, screen sizes, and resolutions, the system employs normalization technology, linearly transforming the original touch coordinates. , Converting to the standard range [0,1], this normalization process not only facilitates cross-device comparison but also optimizes the subsequent machine learning and behavioral model analysis effects. It also ensures the consistency and reliability of data in different application scenarios. The application of this series of technical means enables data processing to not only have high-speed and efficient real-time performance but also accurately extract key features of touch behavior. The core of the sensitivity evaluation module design lies in employing advanced statistical analysis and machine learning techniques to deeply mine and comprehensively evaluate touch data. First, the system compares the success rates of target operations and user operations in actual touch events, combining success and failure statistics with a formula... The success rate (accuracy) of all touch events is calculated to reflect the reliability and precision of the touch system. To measure the system's responsiveness, a time difference calculation method is used to calculate the response time of all touch events. Then, using the average value formula The system obtains the average response time, a metric that significantly reflects the screen's sensitivity level. A shorter response time results in a smoother user experience. Next, the system uses these results to calculate a touch sensitivity score, defined as... This score reflects the distance the user's finger moves within the response time range. The larger the value, the more sensitive the touch and the faster the response. In addition, by statistically analyzing multi-dimensional features such as touch frequency, trajectory complexity, and pressure changes, and combining cluster analysis, entropy calculation and behavior pattern recognition algorithms, the system can automatically identify the user's touch habits, operation preferences and dynamic behavior characteristics, realize personalized behavior analysis, and optimize interface design and interaction logic. The sensitivity assessment module also includes an environmental factor impact analysis unit. Employing multi-sensor fusion and intelligent analysis technology, combined with high-precision environmental sensing devices including light, temperature, and humidity sensors, it monitors the user's environment in real time for lighting intensity, temperature range, and humidity levels. After standardized preprocessing, these environmental parameters undergo multivariate statistical analysis, regression models, and machine learning algorithms (such as support vector machines, random forests, or deep neural networks) for multi-faceted and multi-level correlation analysis. This assesses the impact of different environmental factors on touch sensitivity. The system then combines the collected environmental data with the user's touch event data in the corresponding environment. By combining complex causal relationship models, the system quantifies the specific impact of environmental changes on touch operation success rate, response time, and sensitivity score, reflecting the importance and influence of different environmental factors. The system also uses machine learning algorithms to train and predict historical data on environmental changes and touch performance, identifying potential risks of operation delays or errors under specific environmental conditions. Based on these analyses, the system will automatically generate personalized environmental adaptation suggestions in the final evaluation report, such as adjusting screen brightness, optimizing touch sensitivity threshold, suggesting users use specific operating habits under specific environmental conditions, or prompting users to take environmental adjustment measures to ensure the stability and consistency of the touch experience. The results display module employs various advanced visualization technologies and data interaction methods to transform complex touch evaluation data into intuitive and easy-to-understand charts and images. This ensures users can clearly grasp the overall performance of smartphone screen touch sensitivity. Specifically, the system utilizes data analysis and graphics libraries (such as D3.js, Chart.js, or Matplotlib) to construct a dynamic and interactive report interface, including line charts, bar charts, radar charts, and heatmaps. This comprehensively displays the average touch response time, success rate, sensitivity score, and statistical characteristics of user touch behavior. To enhance user experience, the report interface also features interactive filtering, zooming, and hover tooltips for convenient... Users gain in-depth understanding of data changes across different metrics or time periods. Furthermore, through an efficient data export mechanism, users can save or share evaluation reports in various formats (such as PDF, images, or web pages), facilitating decision analysis and performance optimization in practical applications. Throughout the process, responsive design and professional front-end frameworks (such as React, Vue, or Angular) are employed to ensure excellent display across different terminal devices. Through these technologies, the results display module not only achieves scientific integration of data results but also greatly enhances the aesthetics and interactivity of the presentation, allowing users to intuitively and comprehensively understand and analyze the performance metrics of the touchscreen, providing strong decision-making support for subsequent improvements and optimizations.

[0029] This mobile phone screen touch sensitivity evaluation system uses high-precision sensors and real-time sampling technology to record the time, location, and pressure of every touch event in real time, ensuring the integrity and accuracy of the data. Subsequently, it uses efficient algorithms to calculate the touch duration, movement distance, and normalized coordinates, providing a standardized basis for subsequent analysis. In the evaluation module, it combines success rate, response time, and sensitivity score to quantify the screen's touch performance in detail. At the same time, it identifies user touch habits through statistical behavioral characteristics. Finally, rich charts and interactive interfaces make the evaluation results intuitive, helping users easily understand touch sensitivity and system performance. Overall, this system not only has high technological advancement and practicality, but also provides a scientific and comprehensive solution for optimizing and improving the smartphone touch experience.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A mobile phone screen touch sensitivity evaluation system, characterized in that: It includes a data acquisition module, a data processing module, a sensitivity assessment module, and a results display module; The data acquisition module is used to receive and record the user's touch events on the mobile phone screen in real time, including touch start events and touch end events, and generate timestamps, while also recording the touch position coordinates and touch pressure values. The data processing module receives user touch events on the mobile phone screen acquired by the data acquisition module, calculates the touch duration and touch movement distance of the touch events, and normalizes the touch coordinates before transmitting them to the sensitivity evaluation module. The sensitivity evaluation module calculates the accuracy of all touch events and the average time from the start of the touch to the system response based on the data transmitted by the data processing module, and calculates the touch sensitivity score of the mobile phone screen to evaluate the touch sensitivity of the mobile phone screen. At the same time, based on the statistical analysis of touch events, it identifies the user's touch patterns and behavioral characteristics. The results display module integrates the evaluation results into a mobile phone screen touch sensitivity evaluation report, which is then presented to the user in the form of charts.

2. The mobile phone screen touch sensitivity evaluation system according to claim 1, characterized in that: The duration of the touch event is calculated using the following formula: , in the formula, Indicates the duration of the touch. Indicates the timestamp of the touch event ending. Indicates the timestamp of the touch start event.

3. The mobile phone screen touch sensitivity evaluation system according to claim 2, characterized in that: The formula for calculating the touch movement distance is as follows: , in the formula, Indicates the distance the touch gesture has traveled. Indicates the coordinates of the touch start point. Indicates the coordinates of the touch endpoint.

4. The mobile phone screen touch sensitivity evaluation system according to claim 3, characterized in that: The normalization formula for the touch coordinates is as follows: ， , in the formula, , Indicates the original touch coordinates. , Indicates the screen width range. , Indicates the screen height range.

5. The mobile phone screen touch sensitivity evaluation system according to claim 4, characterized in that: The formula for calculating the average time of the system response is as follows: , in the formula, This represents the average time of the system response. This represents the total number of all detected touch events. Indicates the first System response time per touch. Indicates the index subscript.

6. The mobile phone screen touch sensitivity evaluation system according to claim 5, characterized in that: The accuracy calculation formula for the touch event is as follows: , in the formula, Indicates the accuracy of touch events. This indicates the number of touches that successfully completed the intended operation. This indicates the total number of touches.

7. The mobile phone screen touch sensitivity evaluation system according to claim 6, characterized in that: The formula for calculating the touch sensitivity score of the mobile phone screen is as follows: , in the formula, This indicates the score for the phone screen's touch sensitivity. Indicates the total number of touches. Indicates the first The distance a touch can move. Indicates the first System response time per touch. To represent a minimum value, avoid division by zero. Indicates the index subscript.

8. The mobile phone screen touch sensitivity evaluation system according to claim 7, characterized in that: The user's touch pattern is identified by applying the following formula: , in the formula, This indicates the touch mode recognition score. Indicators representing frequency distribution Indicates the distribution value of the movement path. and This represents the weighting coefficients between the frequency distribution index and the movement path distribution value obtained through training with the deep learning module.

9. A mobile phone screen touch sensitivity evaluation system according to claim 8, characterized in that: The identification of the behavioral features is achieved through the following formula: , in the formula, This indicates the complexity index of touch behavior. Indicates the first The duration of each touch. Indicates the first The distance a touch can move. This represents the adjustment coefficient. Indicates the total number of touches. Indicates the index subscript.

10. A mobile phone screen touch sensitivity evaluation system according to claim 9, characterized in that: The sensitivity assessment module also includes an environmental factor influence analysis unit, which combines touch event data of users under different lighting, temperature and humidity conditions to assess the impact of these environmental factors on touch sensitivity and automatically generates environmental adaptability suggestions in the final assessment results.

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