Game processing method and terminal based on multi-modal physiological data

Abstract

The invention discloses a game processing method and terminal based on multi-modal physiological data, and the method comprises the steps: dynamically collecting physiological baseline data, and collecting the physiological data of a first player in a target game scene; according to the physiological baseline data and the physiological data, using a toughness calculation engine to calculate the psychological toughness score of the first player, and according to the psychological toughness score, adjusting the game difficulty level according to a preset proportion, so that the objectivity of game evaluation can be realized, the psychological toughness level of the player can be adapted, and powerful support is provided for game decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of game design and testing, and particularly relates to a game processing method and a terminal based on multimodal physiological data. Background Art

[0002] Currently, the main approach to game difficulty assessment and adaptation technology relies on player feedback questionnaires and in-game behavior data analysis. Specifically, subjective evaluations of game difficulty are collected through questionnaires, and at the same time, behavior data such as the number of player deaths and clearance time are analyzed to indirectly infer the rationality of game difficulty. However, this existing technology has certain limitations. On the one hand, the subjective deviation is serious, and the questionnaire results are easily affected by players' emotions and expression abilities, making it difficult to truly reflect players' psychological states. For example, players may underestimate their own sense of frustration due to "face" issues. On the other hand, the behavior data has limitations. Indicators such as the number of deaths cannot distinguish whether the player's failure is due to excessive difficulty or operational errors, and cannot quantify the changes in players' psychological resilience during high-pressure events. In addition, the existing technology also lacks personalized adaptation and cannot provide customized difficulty suggestions for players with different psychological resilience levels. Summary of the Invention

[0003] The technical problem to be solved by the present invention is: to provide a game processing method and a terminal based on multimodal physiological data, which can not only achieve the objectivity of game evaluation, but also adapt to the player's psychological resilience level and provide strong support for game decision-making.

[0004] To solve the above technical problem, the technical solution adopted by the present invention is: A game processing method based on multimodal physiological data, including the steps of: S1. Dynamically collect physiological baseline data, and collect the physiological data of the first player in the target game scenario; S2. Use a resilience calculation engine to calculate the psychological resilience score of the first player according to the physiological baseline data and the physiological data, and adjust the game difficulty level according to the psychological resilience score according to a preset ratio.

[0005] To solve the above technical problem, another technical solution adopted by the present invention is: A game processing terminal based on multimodal physiological data, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above-mentioned game processing method based on multimodal physiological data.

[0006] The beneficial effects of the present invention are as follows: A game processing method and terminal based on multi-modal physiological data provided by the present invention collect physiological baseline data dynamically to provide reference data for subsequent game processing, and collect the physiological data of the first player in the target game scenario. According to the physiological baseline data and the physiological data of the first player, a resilience calculation engine is used to calculate the psychological resilience score of the first player. According to the psychological resilience score, the game difficulty level is adjusted according to a preset ratio, making it more in line with the actual psychological state and tolerance of the player, improving the player's game experience, avoiding the player's sense of frustration caused by inappropriate difficulty, and helping developers better understand the psychological characteristics of players and the impact of the game on them, providing strong support for game decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a flowchart of a game processing method based on multi-modal physiological data according to an embodiment of the present invention; Figure 2 It is a system architecture diagram of a game processing method based on multi-modal physiological data according to an embodiment of the present invention; Figure 3 It is another flowchart of a game processing method based on multi-modal physiological data according to an embodiment of the present invention; Figure 4 It is a schematic diagram of a game processing terminal based on multi-modal physiological data according to an embodiment of the present invention; Reference Signs Description: 1. A game processing terminal based on multi-modal physiological data; 2. Memory; 3. Processor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0008] To describe in detail the technical content, the achieved objectives and the effects of the present invention, the following is described in conjunction with the embodiments and accompanied by the drawings.

[0009] Please refer to Figure 1 , an embodiment of the present invention provides a game processing method based on multi-modal physiological data, including the steps: S1. Dynamically collect physiological baseline data, and collect the physiological data of the first player in the target game scenario; S2. According to the physiological baseline data and the physiological data, use a resilience calculation engine to calculate the psychological resilience score of the first player, and adjust the game difficulty level according to a preset ratio according to the psychological resilience score.

[0010] As can be seen from the above description, the beneficial effects of the present invention are as follows: By dynamically collecting physiological baseline data, reference data is provided for subsequent game processing, and the physiological data of the first player in the target game scenario is collected. According to the physiological baseline data and the physiological data of the first player, a resilience calculation engine is used to calculate the psychological resilience score of the first player. According to the psychological resilience score, the game difficulty level is adjusted according to a preset ratio, making it more in line with the actual psychological state and tolerance of the player, improving the player's gaming experience, avoiding the player's sense of frustration caused by inappropriate difficulty, helping developers better understand the psychological characteristics of players and the impact of the game on them, and providing strong support for game decision-making.

[0011] Further, the dynamic collection of physiological baseline data includes: Real-time collect the physiological data of the second player in each game scenario. If there is a situation where the physiological data of the second player does not match the game scenario, mark the abnormal physiological data as invalid and retest the game scenario; Otherwise, use the physiological data as the corresponding physiological baseline data and classify and store the physiological baseline data in the physiological baseline database according to the game scenario.

[0012] As can be seen from the above description, by excluding abnormal physiological data and retesting the game scenario where abnormal data appears, the accuracy and reliability of the physiological baseline data are ensured, accurately reflecting the typical physiological state of the player in each game scenario, and storing the physiological baseline data in the physiological baseline database, providing a reference basis for subsequent analysis and application.

[0013] Further, the physiological data includes heart rate variability, micro-expression data, and skin conductance level, and the physiological baseline data includes heart rate baseline, anxiety index baseline, and skin conductance baseline; Calculating the psychological resilience score of the first player according to the physiological baseline data and the physiological data using a resilience calculation engine includes: Calculate the heart rate stability of the first player according to the ratio between the heart rate variability of the first player and the corresponding heart rate baseline in the physiological baseline data; Quantify the micro-expression data of the first player through a convolutional neural network model to obtain an anxiety index, count the duration during which the anxiety index is higher than the anxiety index baseline, and calculate the expression control degree of the first player according to the ratio between the duration and the total time of the first player's data collection; Calculate the skin conductance change degree of the first player according to the difference between the skin conductance level of the first player and the corresponding skin conductance baseline in the physiological baseline data.

[0014] As described above, through multi-dimensional comprehensive evaluation by combining indicators such as heart rate variability, micro-expression data, and skin conductance level, the transformation of psychological resilience evaluation from subjective scales to objective quantification has been realized, comprehensively and objectively reflecting the psychological resilience level of the first player, which helps to adjust the game difficulty level according to the player's psychological resilience level in the follow-up.

[0015] Furthermore, calculating the psychological resilience score of the first player using a resilience calculation engine based on the physiological baseline data and the physiological data further includes: Assigning corresponding weights to the heart rate stability, expression control degree, and skin conductance change degree of the first player, and calculating the psychological resilience score of the first player according to the heart rate stability, expression control degree, and skin conductance change degree of the first player and their weights: Psychological resilience score = α · heart rate stability + β · expression control degree + γ · skin conductance change degree; Among them, the psychological resilience score is an indicator used to evaluate the psychological resilience and adaptability of the first player when facing stress or challenges; α, β, and γ are all weight coefficients, respectively used to measure the contribution degrees of heart rate stability, expression control degree, and skin conductance change degree in the psychological resilience score.

[0016] As described above, by assigning different weight coefficients to each factor, the influence degree of each factor on calculating the psychological resilience score can be adjusted according to specific situations, improving the evaluation flexibility, and objectively quantifying the psychological resilience for easy analysis and comparison, providing a data basis for game design optimization.

[0017] Furthermore, assigning corresponding weights to the heart rate stability, expression control degree, and skin conductance change degree of the first player includes: Using the random forest algorithm to perform feature importance analysis on the physiological data of the first player to determine the weights of each data type in the physiological data, and optimizing the weights through the gradient descent method, and assigning the optimized weights to the heart rate stability, expression control degree, and skin conductance change degree of the first player correspondingly.

[0018] As described above, by using the random forest algorithm to evaluate the influence degree of different physiological data types on psychological resilience, subjective biases are avoided, the evaluation accuracy is improved, and the weights are further optimized through the gradient descent method, thereby improving the accuracy of psychological resilience evaluation.

[0019] Please refer to Figure 4, Another embodiment of the present invention provides a game processing terminal based on multimodal physiological data, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above-mentioned game processing method based on multimodal physiological data.

[0020] The above-mentioned game processing method and terminal based on multimodal physiological data of the present invention are applicable to the game design scenario, which can not only achieve the objectivity of game evaluation, but also adapt to the player's psychological resilience level, providing strong support for game decision-making. The following is illustrated by specific embodiments: Please refer to Figures 1 to 3 , Embodiment 1 of the present invention is: A game processing method based on multimodal physiological data, including the steps: S1. Dynamically collect physiological baseline data, and collect the physiological data of the first player in the target game scenario.

[0021] In this embodiment, the dynamic collection of physiological baseline data includes: real-time collecting the physiological data of the second player in each game scenario. If there is a situation where the physiological data of the second player does not match the game scenario, the abnormal physiological data is marked as invalid, and the game scenario is retested; otherwise, the physiological data is used as the corresponding physiological baseline data, and the physiological baseline data is classified and stored in the physiological baseline database according to the game scenario. The game scenarios include soothing game scenarios, intense game scenarios, and gentle game scenarios. The baseline data of each game scenario is collected through a unified data collector (such as a smart bracelet, a camera, a biosensor) to ensure data consistency, and the abnormal physiological data is excluded, and the game scenario with abnormal data is retested. For example, if a player shows an abnormal high-pressure signal (such as a heart rate variability lower than the threshold) in the "soothing game", the data of this scenario is marked as invalid and retested to ensure the accuracy and reliability of the classification of physiological baseline data. The physiological baseline data is classified and stored in the physiological baseline database according to the game scenario. For example, "soothing game - heart rate baseline" is independent of "intense game - heart rate baseline", accurately reflecting the typical physiological state of the player in each game scenario, providing a reference basis for subsequent analysis and application.

[0022] Further, in this embodiment, the game scenarios are divided according to prior knowledge. For example, soothing games: verify the game stress level through player research or psychological experiments, and select games with known low stress and high relaxation levels (such as "Animal Crossing"), so as to collect the physiological data baseline of players in a relaxed state; intense games: based on community player feedback or game mechanism complexity (such as the difficulty of boss battles), select games with recognized high difficulty and high stress (such as "Dark Souls"), so as to collect the peak physiological data of players under high pressure; moderate games (such as "Stardew Valley"): collect the physiological data of players under moderate stress.

[0023] S2. Calculate the psychological resilience score of the first player using the resilience calculation engine based on the physiological baseline data and the physiological data, and adjust the game difficulty level according to a preset ratio based on the psychological resilience score, specifically including S2.1 - S2.3.

[0024] In this embodiment, the physiological data includes heart rate variability, micro-expression data, and skin conductance level, and the baseline data includes heart rate baseline, anxiety index baseline, and skin conductance baseline. The physiological data collection devices and evaluation indicators are shown in Table 1. Among them, heart rate variability (HRV) reflects the regulatory ability of the autonomic nervous system; micro-expression data can reflect the instantaneous emotional changes of players, especially anxiety and stress responses; skin conductance level (EDA) reflects the intensity of players' stress responses.

[0025] Table 1 Physiological Data Table

[0026] S2.1. Calculate the evaluation indicators corresponding to each data type, including: Calculate the heart rate stability of the first player according to the ratio between the heart rate variability of the first player and the corresponding heart rate baseline in the physiological baseline data; Quantify the micro-expression data of the first player through a convolutional neural network model to obtain an anxiety index, count the duration during which the anxiety index is higher than the anxiety index baseline, and calculate the expression control degree of the first player according to the ratio between the duration and the total data collection time of the first player; Calculate the skin conductance change degree of the first player according to the difference between the skin conductance level of the first player and the corresponding skin conductance baseline in the physiological baseline data.

[0027] In this embodiment, through multi-dimensional comprehensive evaluation by combining indicators such as heart rate variability, micro-expression data, and skin conductance level, the transformation of psychological resilience evaluation from subjective scales to objective quantification is realized, comprehensively and objectively reflecting the psychological resilience level of the first player, which helps to adjust the game difficulty level according to the psychological resilience level of the player in the follow-up. Specifically, the anxiety index quantifies the instantaneous intensity of micro-expression data (such as the degree of mouth corner sagging, eyebrow tension) through a CNN model, outputting a continuous value from 0 to 1 (0 = no anxiety, 1 = maximum anxiety), and the anxiety duration can be the proportion of the time segment when the anxiety index > 0.5 in the total time of data collection of the first player. The cumulative anxiety effect of the player during the game is reflected through the anxiety expression duration. The expression control degree calculation formula is:

[0028] Among them, I(·) is the indicator function, T is the total time of data collection of the first player. When the player frequently shows high-intensity anxiety expressions (long duration), the expression control degree approaches 0, indicating low psychological resilience; if the player can quickly return to calm (short duration), the expression control degree approaches 1, reflecting high psychological resilience.

[0029] In addition, in this embodiment, a control experimental group is also set up. Players are invited to synchronously record the anxiety index and subjective anxiety score during a standard stress test (such as an arithmetic task), and the accuracy of the model is verified through the Pearson correlation coefficient (such as a correlation between the anxiety index and subjective score > 0.7 is considered valid).

[0030] S2.2. Calculate the psychological resilience score of the first player, specifically: Assign corresponding weights to the heart rate stability, expression control degree, and skin conductance change degree of the first player, and calculate the psychological resilience score of the first player according to the heart rate stability, expression control degree, and skin conductance change degree of the first player and their weights: Psychological resilience score = α · heart rate stability + β · expression control degree + γ · skin conductance change degree; Among them, the psychological resilience score is an indicator for evaluating the psychological resilience and adaptability of the first player when facing stress or challenges; α, β, and γ are all weight coefficients, respectively used to measure the contribution degrees of heart rate stability, expression control degree, and skin conductance change degree in the psychological resilience score.

[0031] In this embodiment, by assigning different weight coefficients to each factor, the influence degree of each factor on calculating the psychological resilience score can be adjusted according to specific situations, improving the evaluation flexibility, and objectively quantifying the psychological resilience for easy analysis and comparison, providing a data basis for adjusting the game difficulty level.

[0032] S2.3. Assign corresponding weights to the heart rate stability, expression control, and skin conductance change of the first player, including: Use the random forest algorithm to analyze the feature importance of the physiological data of the first player to determine the weight of each data type in the physiological data, and optimize the weight by the gradient descent method. Assign the optimized weight to the heart rate stability, expression control, and skin conductance change of the first player.

[0033] In this embodiment, by assigning different weight coefficients to each factor, the influence degree of each factor on calculating the mental toughness score can be adjusted according to the specific situation, improving the evaluation flexibility. And the weight optimization process can effectively reduce the prediction error of the model through iterative adjustment, ensuring the evaluation accuracy. In addition, the weight assignment can also be initially assigned according to psychological research. The contribution degree of heart rate stability to mental toughness evaluation is relatively high. Therefore, the weight of heart rate stability α is assigned as 0.5, and the weights of expression control β and skin conductance change γ are assigned as 0.3 and 0.2 respectively.

[0034] The specific application scenarios of this embodiment include the optimization test of the Boss battle difficulty and the evaluation and decision-making of the DEMO scenario. Specifically: 1. Optimization test of the Boss battle difficulty To verify whether the Boss battle difficulty is adapted to the mental toughness level of the target player group, collect the physiological data of the test players in the Boss battle, calculate the toughness score of the test players according to the physiological data and the physiological baseline data, and adjust the Boss behavior according to the toughness score. The difficulty adjustment rule can be: if the toughness score < 0.3, reduce the attack frequency and damage of the Boss; if the toughness score > 0.7, increase the skill complexity and blood volume of the Boss, so that the game can be adapted to the mental toughness level of the players in real time, avoiding the frustration or boredom caused by the fixed difficulty, and improving the accuracy and response speed of dynamic adjustment through multi-modal data fusion. And monitor the change of the toughness score of the test players after adjustment to verify the optimization effect. The difficulty adaptation rate is expected to increase from 60% to 85%, and the player satisfaction is expected to increase from 3.5 points to 4.2 points.

[0035] 2. Evaluation and decision-making of multiple scenarios in the DEMO To select the optimal scenario from multiple DEMO versions (select the scenario with the mental toughness score closest to the target toughness range (such as 0.4 - 0.6)), multiple tests were conducted on three different difficulty versions (DEMO A with high difficulty, DEMO B with medium difficulty, and DEMO C with low difficulty) by the same player, and the mean value of the mental toughness scores of the player in each version was calculated. The results were: DEMO A: 0.25 (beyond the tolerance range), DEMO B: 0.52 (matched to the target range), DEMO C: 0.75 (insufficient challenge). Therefore, DEMO B was selected as the final solution. By quantifying the mental toughness level, an objective decision-making basis was provided for game design, and the satisfaction of DEMO selection was increased by 40%.

[0036] Please refer to Figure 4 , Embodiment 2 of the present invention is: A game processing terminal 1 based on multi-modal physiological data, including a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements each step of a game processing method based on multi-modal physiological data in Embodiment 1.

[0037] In summary, the game processing method and terminal based on multi-modal physiological data provided by the present invention dynamically collect physiological baseline data, including heart rate baseline, anxiety index baseline, and skin conductance baseline, exclude abnormal physiological data that does not match the test scenario during the collection process, and ensure the accuracy and reliability of the physiological baseline data; collect the physiological data of the first player, including heart rate variability, micro-expression data, and skin conductance level, calculate the heart rate stability, expression control degree, and skin conductance change degree of the first player according to the physiological baseline data and physiological data, and assign corresponding weight coefficients to each evaluation index, including determining the initial weight and weight optimization, calculate the mental toughness score of the first player, improve the flexibility and accuracy of the evaluation, achieve objective quantification of mental toughness, facilitate analysis and comparison, and provide a data basis for game design optimization; adjust the game difficulty level according to the mental toughness score of the first player according to a preset ratio, and adapt to the mental toughness level of the player in real time, avoiding the sense of frustration caused by too high difficulty or the boredom caused by too low difficulty.

[0038] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A game processing method based on multimodal physiological data, characterized in that: Includes steps: S1. Dynamically collect physiological baseline data and collect physiological data of the first player in the target game scene; S2. Calculate the psychological toughness score of the first player using a toughness calculation engine according to the physiological baseline data and the physiological data, and adjust the game difficulty level according to a preset ratio based on the psychological toughness score.

2. A game processing method based on multimodal physiological data according to claim 1, characterized in that: The dynamic collection of physiological baseline data includes: The physiological data of the second player in each game scene is collected in real time. If there is an abnormal match between the physiological data of the second player and the game scene, the abnormal physiological data is marked as invalid, and the game scene is retested; Otherwise, the physiological data is used as corresponding physiological baseline data, and the physiological baseline data is classified and stored in a physiological baseline database according to the game scene.

3. The game processing method based on multimodal physiological data according to claim 1, characterized in that: The physiological data include heart rate variability, micro-expression data and skin conductance level, and the physiological baseline data include heart rate baseline, anxiety index baseline and skin conductance baseline; Calculating the psychological toughness score of the first player using a toughness calculation engine according to the physiological baseline data and the physiological data, including: calculating the heart rate stability of the first player according to the ratio between the heart rate variability of the first player and the corresponding heart rate baseline in the physiological baseline data; quantifying the micro-expression data of the first player through a convolutional neural network model to obtain an anxiety index, counting the duration of the anxiety index being higher than the anxiety index baseline, and calculating the expression control degree of the first player according to the ratio between the duration and the total data collection time of the first player; The skin conductance variation of the first player is calculated according to the difference between the skin conductance level of the first player and the corresponding skin conductance baseline in the physiological baseline data.

4. A game processing method based on multimodal physiological data according to claim 3, characterized in that: Calculating the psychological toughness score of the first player using a toughness calculation engine based on the physiological baseline data and the physiological data, further comprising: Corresponding weights are assigned to the first player's heart rate stability, expression control, and skin conductance variability, and the first player's psychological toughness score is calculated based on the first player's heart rate stability, expression control, and skin conductance variability and their weights: Psychological resilience score = α·heart rate stability + β·expression control + γ·skin conductance change; In the formula, α, β, and γ are weight coefficients, which are used to measure the contribution of heart rate stability, expression control, and skin conductance variability to the psychological resilience score.

5. A game processing method based on multimodal physiological data according to claim 4, characterized in that: Corresponding weights are assigned to the first player's heart rate stability, expression control, and skin conductance change, including: A random forest algorithm is used to perform feature importance analysis on the physiological data of the first player to determine the weight of each data type in the physiological data, and the weight is optimized by a gradient descent method, and the optimized weight is correspondingly assigned to the heart rate stability, expression control, and skin conductance change of the first player.

6. A game processing terminal based on multimodal physiological data, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Dynamically collect physiological baseline data and collect physiological data of the first player in the target game scene; S2. Calculate the psychological toughness score of the first player using a toughness calculation engine according to the physiological baseline data and the physiological data, and adjust the game difficulty level according to a preset ratio based on the psychological toughness score.

7. A game processing terminal based on multimodal physiological data according to claim 6, characterized in that: The dynamic collection of physiological baseline data includes: The physiological data of the second player in each game scene is collected in real time. If there is an abnormal match between the physiological data of the second player and the game scene, the abnormal physiological data is deemed invalid and the game scene is retested; Otherwise, the physiological data is used as corresponding physiological baseline data, and the physiological baseline data is classified and stored in a physiological baseline database according to the game scene.

8. The game processing terminal based on multimodal physiological data according to claim 6, characterized in that: The physiological data include heart rate variability, micro-expression data and skin conductance level, and the physiological baseline data include heart rate baseline, anxiety index baseline and skin conductance baseline; Calculating the psychological toughness score of the first player using a toughness calculation engine according to the physiological baseline data and the physiological data, including: calculating the heart rate stability of the first player according to the ratio between the heart rate variability of the first player and the corresponding heart rate baseline in the physiological baseline data; quantifying the micro-expression data of the first player through a convolutional neural network model to obtain an anxiety index, counting the duration of the anxiety index being higher than the anxiety index baseline, and calculating the expression control degree of the first player according to the ratio between the duration and the total data collection time of the first player; The skin conductance variation of the first player is calculated according to the difference between the skin conductance level of the first player and the corresponding skin conductance baseline in the physiological baseline data.

9. A game processing terminal based on multimodal physiological data according to claim 8, characterized in that: Calculating the psychological toughness score of the first player using a toughness calculation engine based on the physiological baseline data and the physiological data, further comprising: Corresponding weights are assigned to the first player's heart rate stability, expression control, and skin conductance variability, and the first player's psychological toughness score is calculated based on the first player's heart rate stability, expression control, and skin conductance variability and their weights: Psychological resilience score = α·heart rate stability + β·expression control + γ·skin conductance change; Among them, the psychological resilience score is an indicator used to evaluate the psychological resilience and adaptability of the first player when facing pressure or challenges; α, β, and γ are all weight coefficients, which are used to measure the contribution of heart rate stability, expression control, and skin conductance change to the psychological resilience score.

10. A game processing terminal based on multimodal physiological data according to claim 9, characterized in that: Corresponding weights are assigned to the first player's heart rate stability, expression control, and skin conductance change, including: A random forest algorithm is used to perform feature importance analysis on the physiological data of the first player to determine the weight of each data type in the physiological data, and the weight is optimized by a gradient descent method, and the optimized weight is correspondingly assigned to the heart rate stability, expression control, and skin conductance change of the first player.

Images