Biofeedback-based alertness enhancement method, system, storage medium, and device

Abstract

The embodiment of the present application provides a method, system, storage medium and device for improving alertness based on biofeedback, the method comprising: determining an alertness improvement feedback training task; collecting physiological characteristic data of a user to be trained, determining a personalized physiological characteristic data baseline threshold of the user to be trained, and setting an initial training difficulty of the feedback training task according to the determined baseline threshold; starting the feedback training task in a current training stage, collecting physiological characteristic data of the user to be trained in the current training stage of the feedback training task, determining a personalized physiological characteristic data stage threshold of the user to be trained in the current training stage, and setting a stage training difficulty of a next stage of the feedback training task according to the stage threshold determined in the current stage; and continuing the training task until the task is completed. The embodiment of the present application can support special personnel such as long-haul personnel to complete a training target, realize improvement of alertness adjustment ability of the special personnel, and thus achieve the effect of improving alertness level and slowing down alertness decline.

Description

Technical Field

[0001] This invention relates to the field of biofeedback training technology, and in particular to methods, systems, storage media, and devices for improving alertness based on biofeedback. Background Technology

[0002] Special personnel performing long-haul missions may experience a decline in alertness due to prolonged isolation, irregular shift work, and continuous operation, which can affect the smooth execution of these missions. Therefore, it is necessary to provide alertness training to long-haul personnel to equip them with mature alertness regulation capabilities, thereby improving the duration and level of alertness maintained under typical mission conditions. Currently, commonly used methods for alertness training include behavioral methods, computer-aided training, and biofeedback. With the development of biofeedback technology, biofeedback training, due to its advantages such as low cost, no side effects, and stable results, is gradually being applied to improve alertness in various fields. Currently, applications of biofeedback-based alertness training are mostly conducted in laboratory environments, and there is a lack of applications for specific personnel in real-world work scenarios. When selecting different feedback signal sources (EEG, fNIRS, MRI), in addition to considering the temporal and spatial resolution of the signal source, the portability and cost-effectiveness of the neurophysiological signal acquisition device in real-world use and training, as well as the ease of deployment and implementation and manpower costs, should also be considered to make a comprehensive selection.

[0003] Biofeedback training is more suitable for long-haul personnel than traditional non-feedback training methods. Since its inception, the basic principles and system composition of this training method have remained relatively fixed and clear, with its process remaining largely unchanged, still consisting of three parts: signal extraction, signal processing and calculation, and signal feedback. However, there are still some gaps in directly applying existing biofeedback alertness technology to the alertness enhancement training of long-haul personnel, particularly in terms of the target population and the requirements for transferability. First, in the selection of feedback signal sources, the actual training environment of specific personnel should be fully considered, selecting feedback signal sources and corresponding portable acquisition equipment that can both ensure signal accuracy to guarantee training effectiveness and facilitate implementation. Second, in terms of biofeedback training strategies, current domestic and international applications only cover implicit strategies related to alertness training, and have not yet considered how to design corresponding explicit training strategies for alertness enhancement specifically for long-haul personnel. Third, in terms of training program design, current domestic and international applications have not fully considered the individual differences in biofeedback training and have not established flexible and applicable training programs for different individuals. Summary of the Invention

[0004] In view of the above-mentioned problems existing in the prior art, the embodiments of the present invention provide a method, system, storage medium and device for enhancing alertness based on biofeedback, so as to solve the technical problem that existing biofeedback training technology cannot be directly used for enhancing alertness training of long-haul personnel.

[0005] This invention provides a method for enhancing alertness based on biofeedback, comprising the following steps: Step S1: Based on the reasons for the decline in vigilance among long-haul personnel, determine the vigilance enhancement feedback training task; Step S2: Collect physiological characteristic data of the trainees, determine the baseline threshold of the personalized physiological characteristic data of the trainees, and set the initial training difficulty of the feedback training task based on the determined baseline threshold. Step S3: Start the feedback training task for the current training phase, collect physiological characteristic data of the trainee in the current training phase of the feedback training task, determine the personalized physiological characteristic data phase threshold of the trainee in the current training phase, and set the phase training difficulty of the next phase of the feedback training task based on the phase threshold determined in the current phase. Step S4: Determine whether the feedback training task meets the completion requirements. If yes, proceed to step S5; otherwise, proceed to step S3. Step S5: End the feedback training task.

[0006] In one embodiment, the physiological characteristic data is the level of oxyhemoglobin in the prefrontal cortex of the trained user.

[0007] In one embodiment, the physiological characteristic data is acquired using a portable near-infrared brain functional imaging device.

[0008] In one embodiment, the decline in alertness among long-haul workers is caused by prolonged isolation, irregular shift work, and continuous operation.

[0009] In one embodiment, the alertness enhancement feedback training task includes meta-attention monitoring training, arousal training, and cognitive resource training, respectively corresponding to factors such as long-term isolation and confinement, irregular shift work, and continuous work.

[0010] In one embodiment, the training scenarios for the arousal training and meta-attention monitoring training are matched with the work scenarios of the trainees in long-haul operation tasks.

[0011] In one embodiment, the method further includes: Step S6, transfer training: After the feedback training task is completed, transfer training is performed without real-time feedback.

[0012] In addition, embodiments of the present invention also provide an alertness enhancement system based on biofeedback, comprising: Near-infrared data acquisition module is used to acquire optical signal data from the prefrontal cortex of the training user; The biofeedback training module is used to provide feedback training tasks and receive raw data collected by the near-infrared data acquisition module during the feedback training task stage. It processes the data, extracts HBO indicators, adjusts the difficulty of the feedback training task based on the extracted HBO data, and adjusts the visual feedback parameters during the visualization biofeedback stage. The database uses a relational data format to store basic information about trainees and training data, and provides data editing and download functions.

[0013] In addition, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the biofeedback-based alertness enhancement method as described in any embodiment of the present invention.

[0014] In addition, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the biofeedback-based alertness enhancement method as described in any embodiment of the present invention.

[0015] Compared with existing technologies, the beneficial effects of the biofeedback-based alertness enhancement method, system, storage medium, and device provided in this invention are as follows: Based on the existing feedback training framework using neurophysiological acquisition devices, this invention establishes an adaptive attention feedback intervention framework, adds a transfer training component, and adaptively adjusts the training difficulty according to the subject's current level of self-regulation. This allows long-haul personnel to consolidate their alertness enhancement strategies in training scenarios that match their work environment, ensuring that even without real-time physiological feedback in actual maritime operations, long-haul personnel can enhance and maintain alertness through implicit or explicit strategies. This effectively ensures the long-haul personnel's ability to improve alertness and effectively transfers training results. Attached Figure Description

[0016] Figure 1 A schematic diagram of the adaptive training feedback intervention framework involved in the biofeedback-based alertness enhancement method provided in the embodiments of the present invention; Figure 2 A flowchart illustrating the biofeedback-based alertness enhancement method provided in this embodiment of the invention; Figure 3 A schematic diagram of the process of a single training session of the feedback training task involved in the alertness enhancement method based on biofeedback provided in the embodiments of the present invention; Figure 4 A schematic diagram of the structure of the biofeedback-based alertness enhancement system provided in an embodiment of the present invention; Figure 5 A schematic diagram illustrating the hardware components and training methods of the biofeedback-based alertness enhancement system provided in this embodiment of the invention. Figure 6 A schematic diagram of a transfer training task scenario involving arousal enhancement based on biofeedback provided in an embodiment of the present invention. Figure 7 A schematic diagram of a cognitive resource enhancement transfer training task scenario involved in the biofeedback-based alertness enhancement method provided in this embodiment of the invention; Figure 8 A schematic diagram of a meta-attention monitoring enhancement transfer training task scenario involved in the biofeedback-based alertness enhancement method provided in this embodiment of the invention; Figure 9 This is a schematic diagram illustrating the usage process of the biofeedback-based alertness enhancement system provided in an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0019] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0020] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0021] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0022] Specific embodiments of this application are described below with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to ascertain the true intent based on the user's historical operations, and to avoid unnecessary or redundant details that would obscure this application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in various ways with substantially any suitable detailed structure.

[0023] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0024] The principles and features of the present invention are described below with reference to the accompanying drawings. The embodiments described are for illustrative purposes only and are not intended to limit the scope of the invention. The following description, in conjunction with... Figure 1-9 The preferred embodiments of the present invention will be described in further detail below: like Figure 1-2 , Figure 9 As shown, this embodiment of the invention provides a method for enhancing alertness based on biofeedback, comprising the following steps: Step S1: Based on the reasons for the decline in vigilance among long-haul personnel, determine the vigilance enhancement feedback training task; Step S2: Collect physiological characteristic data of the trainees, determine the baseline threshold of the personalized physiological characteristic data of the trainees, and set the initial training difficulty of the feedback training task based on the determined baseline threshold. Step S3: Start the feedback training task for the current training phase, collect physiological characteristic data of the trainee in the current training phase of the feedback training task, determine the personalized physiological characteristic data phase threshold of the trainee in the current training phase, and set the phase training difficulty of the next phase of the feedback training task based on the phase threshold determined in the current phase. Step S4: Determine whether the feedback training task meets the completion requirements. If yes, proceed to step S5; otherwise, proceed to step S3. Step S5: End the feedback training task.

[0025] In one embodiment, the physiological characteristic data is the level of oxyhemoglobin in the prefrontal cortex of the trained user.

[0026] In one embodiment, the physiological characteristic data is acquired using a portable near-infrared brain functional imaging device.

[0027] In one embodiment, the decline in alertness among long-haul workers is caused by prolonged isolation, irregular shift work, and continuous operation.

[0028] In one embodiment, the alertness enhancement feedback training task includes meta-attention monitoring training, arousal training, and cognitive resource training, respectively corresponding to factors such as long-term isolation and confinement, irregular shift work, and continuous work.

[0029] In one embodiment, the training scenarios for the arousal training and meta-attention monitoring training are matched with the work scenarios of the trainees in long-haul operation tasks.

[0030] In one embodiment, the method further includes: Step S6, transfer training: After the feedback training task is completed, transfer training is performed without real-time feedback.

[0031] In addition, embodiments of the present invention also provide an alertness enhancement system based on biofeedback, comprising: Near-infrared data acquisition module is used to acquire optical signal data from the prefrontal cortex of the training user; The biofeedback training module is used to provide feedback training tasks and receive raw data collected by the near-infrared data acquisition module during the feedback training task stage. It processes the data, extracts HBO indicators, adjusts the difficulty of the feedback training task based on the extracted HBO data, and adjusts the visual feedback parameters during the visualization biofeedback stage. The database uses a relational data format to store basic information about trainees and training data, and provides data editing and download functions.

[0032] In addition, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the biofeedback-based alertness enhancement method as described in any embodiment of the present invention.

[0033] In addition, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the biofeedback-based alertness enhancement method as described in any embodiment of the present invention.

[0034] Example 1 Existing methods for enhancing alertness focus on behavioral training, which requires therapists with psychological expertise and is difficult to extend to specific populations. Furthermore, existing computer-assisted training and biofeedback training are mostly conducted in laboratory settings, making it difficult to guarantee the transferability of training effects when applied to specific work scenarios.

[0035] This invention constructs a typical scenario of alertness decline, specifies a matching alertness training strategy, and utilizes adaptive difficulty-adjusted biofeedback technology to guide personnel in reinforcement training, enabling them to autonomously combat alertness decline. During the training process, three biofeedback training strategies suitable for specific work groups are constructed to improve alertness maintenance capabilities in different scenarios. This achieves the effect of improving alertness levels and mitigating alertness decline.

[0036] Compared to traditional electroencephalography (EEG) techniques, hemodynamic imaging signals, especially real-time biofeedback based on functional near-infrared spectroscopy (FIR), are novel. Computers can perform real-time denoising preprocessing on the images to calculate various data indicators used in conventional functional magnetic resonance imaging (fMRI), such as brain activation, functional connectivity strength, and even more complex independent component and multivariate analyses. The calculated indicators are presented to the subjects via a visual system (visualization) as a feedback signal. Subjects can then learn to adjust various brain indicators autonomously, according to experimental requirements, to achieve corresponding changes in brain activity.

[0037] This invention comprises a portable near-infrared brain functional imaging device and a computer-based alertness enhancement system. The prototype, based on physiological characteristics such as real-time cerebral blood oxygenation levels, employs a biofeedback training method combining implicit and explicit approaches. Through training of arousal, cognitive resources, and meta-attention monitoring, it enhances alertness. The prototype possesses functions for long-duration personnel alertness training, assessment, and management, and can support specialized personnel in achieving training objectives.

[0038] This invention establishes a biofeedback training technique based on near-infrared brain functional imaging and identifies the target brain region for feedback, namely the prefrontal cortex, based on previous experimental results.

[0039] This invention addresses three mechanisms of vigilance decline, combining cognitive training and mindfulness meditation, employing implicit and explicit training methods, and designing three special forms of feedback for individuals.

[0040] This invention, based on an existing feedback training framework using neurophysiological data acquisition devices, adds a transfer training component and designs corresponding alertness level induction tasks to simulate the alertness decline of long-haul personnel during long-haul missions. This allows personnel to consolidate their alertness enhancement strategies in this training scenario, ensuring that even without real-time physiological feedback in actual maritime operations, personnel can enhance and maintain alertness through implicit or explicit strategies. Furthermore, the adaptive alertness regulation and transfer training techniques developed for the transfer of alertness effects among long-haul personnel are highly comprehensive, ensuring effective long-distance transfer of training results and providing theoretical support for personnel training and effect verification in subsequent special scenarios.

[0041] like Figure 5 As shown, this study analyzes the characteristics of vigilance decline among long-haul personnel, which are caused by three factors: prolonged isolation and confinement, irregular shift work, and continuous operation. It also explores corresponding vigilance enhancement strategies by combining cognitive training, mindfulness training, and other training methods. Specifically, the strategies are: arousal training for irregular shift work, cognitive resource training for continuous operation, and meta-attention monitoring training for prolonged isolation and confinement. For navigation, steering and other job tasks, relevant biofeedback methods were designed to break through the training technology for vigilance of long-haul personnel. It has been verified that it can effectively improve the vigilance maintenance time and vigilance level of long-haul personnel in typical mission environments.

[0042] like Figure 1 As shown, this embodiment of the invention enhances the alertness of subjects through data acquisition and processing, personalized adjustment, adaptive task difficulty adjustment, and execution feedback training tasks. The data acquisition and processing stage uses near-infrared sensors to collect brain region activity data from subjects, and performs noise removal, feature extraction, and data analysis on the subjects' HBO data.

[0043] like Figure 2 As shown, in the personalized adjustment phase, individualized calibration is performed for each subject, and HBO data of each individual in the initial training state is collected to obtain feedback index thresholds adapted to each subject. Simultaneously, a personalized training program is selected to provide more motivation and stimulation for individuals, and feedback features and methods are set according to individual training effects. After data collection is completed, this embodiment of the invention updates each person's personalized parameters to the feedback training task phase.

[0044] The adaptive task difficulty adjustment mechanism operates throughout the feedback training period, continuously assessing each individual's attentional state and determining task adjustment strategies to adjust the training task difficulty. Before adjusting the training task difficulty, the participants' HBO data is analyzed to calculate key parameters for task difficulty adjustment. The training task difficulty is adjusted based on the participants' current level of self-regulation. These key parameters are transmitted to the feedback training phase, thereby altering the feedback training scenario. Participants adjust their state according to the changes in the feedback training scenario. These stimuli cause participants to perceive changes in their internal bodily signals, and their internal negative feedback regulation loops activate to maintain the current HBO within a normal range.

[0045] The feedback training task phase primarily provides various feedback training tasks (including personnel arousal training, cognitive resource training, and meta-attention monitoring training). Changes in feedback indicators are mapped to changes in the task scenario during this phase, enabling participants to accurately understand changes in their own attentional state. Furthermore, personalized feedback training tasks can better attract participants' attention, thereby increasing their initiative and enthusiasm in participating in feedback training and enhancing its effectiveness.

[0046] Adaptive threshold selection The purpose of biofeedback training is to teach individuals how to adjust their brain activity, thereby influencing certain specific behavioral patterns. It typically works by mapping a specific pattern of brain region activity signals to audio or visual feedback signals for the subject. For example, high and low alertness correspond to specific forms of feedback stimuli. Through repeated training, individuals can recall and reconstruct the one-to-one correspondence between feedback characteristics and specific stimuli, thus learning to associate specific changes in mental state with changes in brain activity. Generally, biofeedback rewards are implemented using a sliding window approach. This involves calculating the individual's brain region activity characteristics, for example, moving the window forward in 2-second increments. Feedback characteristics are calculated using HBO data within the window each time, and then compared to a threshold. When the feedback characteristics exceed the threshold, visual feedback or other forms of reward are provided to the subject. Conversely, when the feedback characteristics are below the threshold, no reward is provided. Therefore, the method of biofeedback rewards can be described by the following formula:

[0047] in This indicates the current feedback metrics. The threshold representing the feedback metric, It indicates both the method of feedback and reward, and the current state of alertness.

[0048] This feedback reward method has two problems. First, the feedback reward method is not precise enough. As long as the feedback characteristic exceeds the threshold, the same stimulus reward will be provided. This will reduce the individual's mental effort and will not be able to correspond one-to-one with different attentional states and different stimulus reward intensities, thus weakening the effect of feedback training. Second, another key issue is how to determine an appropriate threshold. Traditional biofeedback usually involves a professional doctor determining the threshold, or using a standard threshold for all subjects. However, because physiological signals often vary greatly from person to person, depending on both the subject's physiological differences and their emotional and psychological factors at the time, a fixed feedback index threshold will not be suitable for different individuals. Therefore, it is necessary to determine different feedback thresholds for different individuals.

[0049] To achieve a more precise feedback and reward system, it is necessary to describe alertness states more accurately.

[0050] 1. In a single training task, adjustments are made based on the baseline HBO upper and lower limits for each individual.

[0051] In this embodiment of the invention, the feedback features are normalized to a range of [0,1]. The closer to 1, the lower the alertness; the closer to 0, the higher the alertness, as shown in formula (2). Furthermore, the alertness state between [0,1] will be mapped to different feedback reward stimulus intensities. The closer to 1, the lower the stimulus intensity; the closer to 0, the higher the stimulus intensity. In this way, the alertness state and the reward stimulus can be accurately mapped.

[0052]

[0053] in This represents the lower limit threshold of the feedback indicator; This indicates the upper limit threshold of the feedback indicator. It indicates both the intensity of the feedback reward stimulus and the current state of alertness.

[0054] Based on existing biofeedback experimental results, baseline data was collected in the first 30 seconds of training, and the average HBO was calculated within 2 seconds by sliding the time window.

[0055] The average HBO over 30 seconds was used as the baseline for biofeedback. ; The maximum HBO value within a 2-second window of 30 seconds was used as the upper limit threshold for biofeedback. ; This allows for personalized input of feedback indicator thresholds.

[0056] Adaptive training difficulty adjustment 2. In multiple consecutive training tasks, adjust the difficulty of the next task based on the results of the previous task.

[0057] Biofeedback training cycles are typically long. If trainees cannot maintain interest and motivation during these extended periods, it hinders the improvement of individual alertness levels. To attract trainees' alertness and enhance the effectiveness of biofeedback training, this patent combines biofeedback with tasks, designing multiple alertness feedback training strategies to fully meet the diverse interests of trainees and provide personalized feedback training task scenarios. Trainees can choose training methods and task scenarios that interest them. Visually, different feedback scenarios are provided, each with multiple levels of scene brightness, clarity, and field of view. In order to provide more accurate feedback training stimuli to individuals, this embodiment of the invention maps near-infrared feedback indicators to task adjustment parameters. When individuals have different levels of alertness, different levels of training objectives need to be provided.

[0058] In feedback training, alertness indicators are transmitted to the training process in real time, thereby modifying the parameters of the feedback training phase. Individuals can intuitively understand their own alertness level through changes in the parameters of the feedback training task, and thus adjust their alertness accordingly. Individuals must effectively improve their alertness level to complete the training.

[0059] Adaptive biofeedback relies on an individual's self-regulation ability, and due to variations in self-regulation, individual responses to tasks differ significantly. Furthermore, an inverted U-shaped relationship exists between task performance and individual engagement. As workload decreases, engagement gradually declines, and the training effect on the subject decreases accordingly; similarly, as workload increases, limited regulatory capacity leads to performance degradation. Only by finding the optimal performance level can a good training effect be achieved. During feedback training, tasks that are too easy or too difficult can distract individuals from alertness. If the reward stimulus is easily obtained within the threshold range, the target is easily lost, which is detrimental to improving the subject's alertness. Therefore, in adaptive alertness regulation systems, appropriate feedback training difficulty is a key factor in enabling subjects to achieve optimal performance.

[0060] This invention incorporates adaptive feedback training tasks into a biofeedback system, precisely linking changes in biofeedback metrics with the difficulty of the feedback training task, and proposes an adaptive training difficulty adjustment strategy.

[0061] Target metric threshold: In adaptive training, a complete training session is divided into several repeated single training tasks. In each training task, a personalized feedback indicator threshold is determined based on the trainee's current brain region activity level, and the current feedback difficulty is determined based on past data or initial settings. Based on both, the target indicator threshold Y for each training task is obtained. i .

[0062] Training objectives: The training objective in a single training task is to maintain brain region activity within the target threshold Y. i Reaching duration T L If this objective is achieved during a single training task, it is considered that the training objective has been achieved; otherwise, it is considered that it has not been achieved. Feedback difficulty adjustment: The feedback difficulty is adjusted based on the achievement of the training objectives. If the training objective is achieved in this session, the training difficulty is increased in the next training session, with the target value being the current feedback difficulty V and the difficulty increase step size V. m The sum; if the training objective is not achieved this time, the feedback difficulty will be reduced in the next training session, with the target value being the current feedback difficulty V and the difficulty reduction step size V. n The difference (when the target value is lower than the minimum difficulty value V)min At that time, it was still V. min ); The specific content of each alertness enhancement training is as follows.

[0063] 1. Arousal Enhancement Training The wakefulness enhancement training uses the feedback signal strength obtained through a 2-second sliding window timer to periodically update the blur level of the image. The stronger the signal, the less blurry the image. Instructions and audio are displayed at the top to guide the user, thereby achieving the effect of biofeedback by reflecting blood oxygen levels onto the interface.

[0064] 2. Cognitive Resource Enhancement Training An n-backQTimer is defined to periodically refresh numbers to implement the n-back cognitive task. The radius of the feedback color block in the image is periodically updated using the feedback signal strength obtained from a 2-second sliding window timer; the stronger the signal, the larger the radius. Instructions and audio guide the user to complete the n-back task, thus achieving a biofeedback effect by displaying blood oxygen levels on the interface.

[0065] 3. Training to enhance meta-attention monitoring The meta-attention monitoring enhances training by using a 2-second sliding window timer to obtain feedback signal strength, periodically updating the position of the template in the image. The stronger the signal, the farther and faster the target changes. Instructions and audio are displayed above to guide the user, thereby achieving a biofeedback effect by reflecting blood oxygen levels onto the interface.

[0066] Different training scenarios for improving meta-attention monitoring are developed for different job positions: 1) Nautical Scene The task is to walk from one end of the nautical chart to the other. The management system can configure the start and end points, with the end point represented by a red triangle and the route shown by a red dashed line. As the strength of the biofeedback signal increases, the ship will gradually travel towards the end point along the route.

[0067] 2) Steering scenario The pointer will rotate around the center as the intensity of the biofeedback signal changes; the higher the signal intensity, the closer it is to the target angle.

[0068] 3) Variable Depth Scenes Once training begins, the current depth is indicated by the scale corresponding to the depth of the blue bars, while the target depth is indicated by the red triangles. The depth of the blue bars will change with the intensity of the biofeedback signal; the higher the signal intensity, the closer it is to the target depth.

[0069] Training effect transfer training strategy Traditional biofeedback training aims to improve an individual's alertness level, representing short-term transfer of training effects. Its effectiveness is primarily assessed through standardized cognitive psychology tests related to alertness, such as the AX-CPT task, the Schulte Grid, and the go / no-go task. These tests essentially measure baseline attention levels and explore the retention effect of training on task performance after biofeedback training ceases. However, alertness maintenance and enhancement training for specific personnel is primarily implemented before the mission. The ultimate goal is to ensure that the enhanced alertness from the training supports their operational effectiveness during the mission phase, representing long-term transfer of training effects. Existing mature biofeedback technologies exhibit poor transferability in the context of long-distance maritime transport personnel. Therefore, maintaining and transferring the training effects to the mission phase after training ceases for these personnel presents a significant challenge.

[0070] To ensure that the training effect of biofeedback technology can be effectively transferred to the typical work scenarios of special personnel, the embodiments of the present invention break through the transfer training technology of alertness from the perspectives of different levels of alertness induction and adaptive adjustment of training difficulty.

[0071] This invention, in addition to the traditional real-time feedback phase of biofeedback training, adds delayed transfer training. That is, transfer training is conducted after the initial feedback training, without real-time feedback (delayed feedback is provided after training). During the feedback training phase, trainees autonomously learn corresponding implicit and explicit neural regulation strategies, mastering self-regulation methods of alertness suitable for themselves. From an ergonomic perspective, this enables individuals with special needs to maintain the same level of alertness as during the task phase. During the transfer training phase, no feedback information is provided to the trainers. They are required to effectively apply the alertness regulation strategies learned in the feedback training phase to complete the transfer task under simulated different levels of alertness. This ensures that even in the absence of real-time physiological feedback in a real-world work environment, individuals with special needs can enhance and maintain alertness through implicit or explicit strategies, thereby guaranteeing effective long-term transfer of training results.

[0072] The specific transfer training content is as follows: 1. Enhanced Arousal Through Transfer Training like Figure 6 As shown, the background material presented is consistent with the feedback training module for enhancing arousal, and the clarity remains unchanged. Trainees use implicit strategies learned in feedback training to improve the activity of their own brain regions.

[0073] 2. Cognitive resource enhancement transfer training like Figure 7As shown, the same 2-back interface as the cognitive resource enhancement feedback training module is presented. The parameters of the 2-back training remain unchanged, and the background color is fixed to the initial color. Trainees use the implicit strategies learned in the feedback training to improve the activity of their own brain regions.

[0074] 3. Meta-attention monitoring enhances transfer training like Figure 8 As shown, the task scenario interface is presented in the same way as the cognitive resource enhancement feedback training module, and mindfulness audio is played. Trainees use the mindfulness methods learned in the feedback training to follow the audio for mindfulness training and improve their brain activity. During the training process, the rudder angle pointer / ship position / depth indicator on the task interface does not change with the brain activity.

[0075] Training process and methods The training process consists of two parts: a single training session and a training cycle and an interval. Default training parameters have been preliminarily determined, and the specific design is as follows: 1. Single training process The process of a single training session is designed as follows: Figure 3 As shown.

[0076] 1) When formal training begins, a PVT test is conducted first, with a default test duration of 5 minutes, and the current alertness level of the personnel is recorded; 2) Conduct feedback training. The default duration of feedback training is a minimum of 2 minutes and a maximum of 5 minutes. If the training goal is achieved ahead of schedule, the feedback training session will end early. A total of 5 feedback training sessions must be completed consecutively. When visual stimulation is used as a form of feedback, the frequency of stimulation change should be moderate. If the stimulation changes too quickly, the body will not be able to react in time; if the stimulation changes too slowly, it will not effectively reflect the body's current state changes. The rate of change should be determined based on the biofeedback method used in this embodiment of the invention and the subject's subjective experience, with a stimulation change rate of times per 2 seconds. That is, during the feedback training process, HBO is collected and calculated in 2-second time windows. Within the initial 30 seconds of training, the baseline value and threshold of the feedback are determined, and the training target is determined in conjunction with the current training difficulty. Two minutes after training is completed, the training target is judged to determine whether the HBO concentration increase value has reached the training target within 30 consecutive seconds. If so, the training target is determined to have been reached, one feedback training session ends, the corresponding difficulty parameters are adjusted, and the next stage begins. If the current training target is not reached within 5 minutes, one feedback training session ends, the corresponding difficulty parameters are adjusted, and the next stage begins. 3) Conduct transfer training, and determine the training difficulty of transfer training based on the highest difficulty of feedback training; 4) Conduct a PVT test, with a default test duration of 5 minutes, and record the alertness level of the personnel after training; 2. Training presentation and instructions During the training process, in order to better guide trainees to train according to the goals designed by the training techniques and to enable trainees to carry out relevant training work independently, instructions were designed for each training task and corresponding scenario, as detailed in the table below.

[0077] Training presentation and instructions

[0078] 3. Training cycle and training interval Research on the number of biofeedback training sessions has yielded varying results across different studies. Based on existing research, the training cycle typically ranges from 4 to 12 weeks, with 4 to 8 training sessions per cycle. The interval between two training sessions is usually as short as 0.5 days and as long as no more than 2 weeks.

[0079] Based on preliminary experiments, a follow-up study of arousal training was conducted on two subjects. Within one training cycle, the subjects showed significant training effects in the first two training sessions, with both sessions increasing HBO concentration in the target brain region. No significant improvement was observed at the start of the third training session. Therefore, it can be concluded that training for special individuals should be maintained at least three times within one cycle.

[0080] like Figure 4 As shown, the alertness enhancement system based on biofeedback mainly consists of three parts: a near-infrared data acquisition module, a biofeedback training module, and a database.

[0081] Near-infrared data acquisition module: The core hardware module of the entire system. Its main function is to collect brain activation data from trainees. This data will be used to provide biofeedback to assist users in training. The collected data is transmitted to the biofeedback training module.

[0082] Biofeedback Training Module: Receives data signals from the near-infrared data acquisition module. The signal processing module processes the received raw signals and extracts HBO data. Based on changes in the trainee's brain activation level, it provides visualized biofeedback, allowing users to intuitively understand their brain activity state. This module includes training functions and offline data evaluation functions. Three different feedback forms are designed based on different vigilance decay mechanisms, including arousal training, cognitive resource training, and meta-attention monitoring training. An adaptive threshold algorithm is incorporated into the biofeedback training module, which can adjust the parameters of visual feedback according to individual differences among trainees and their current training effect. The offline data evaluation function can import the trainee's physiological (near-infrared, ECG) and behavioral (PVT performance) data and output the corresponding vigilance level to help trainees understand their current vigilance level.

[0083] Database: A relational database is used to store trainer data, including basic user information, HBO data from user training, and PVT performance data. The database allows for the download and editing of this data.

[0084] In a preferred embodiment, a PC-based biofeedback enhancement training system is built based on Huichuang Company's portable near-infrared brain functional imaging device. This system can connect in real-time with an alertness central nervous system signal monitoring device (portable near-infrared brain functional imaging device) to collect the user's brain blood oxygenation activity in real time. Through the biofeedback training module, the brain blood oxygenation level is presented to the user in a visual way, helping the user understand their own blood oxygenation activity level. The user can set training goals and gradually master strategies for autonomously regulating the blood oxygenation activity level of alertness-related brain regions based on the results of visual feedback, thus achieving biofeedback training.

[0085] The system functions mainly consist of five parts: data transmission and reception, a PyQt-based user interface, a database, near-infrared data processing and analysis, a user control terminal, and a management control terminal.

[0086] Data Transmission / Reception: This function handles communication, control, and data acquisition between the computer and the portable near-infrared device. It uses TCP / IP-based communication software to ensure stable data transmission. Simultaneously, it employs security protocols such as HTTPS to encrypt data and protect user privacy.

[0087] The PyQt-based user interface is developed to run on various operating systems, including Windows, Mac, and Linux. It provides visual biofeedback, allowing users to intuitively understand their brain's neural activity. Furthermore, the interface offers various functionalities, such as starting and pausing training, and viewing historical training data.

[0088] Database: A relational database is used to store the user's training data. The database is stored on the user's computer and is backed up regularly.

[0089] Near-infrared data processing and analysis: Primarily responsible for processing and analyzing data collected from portable near-infrared devices to generate biofeedback and provide it to users.

[0090] The user control terminal is the core of the entire system, responsible for coordinating the operation of various functional modules. It controls the PyQt interface, near-infrared data processing, near-infrared device communication, and database communication by sending and receiving information.

[0091] The management control panel supports setting training parameters for various functions and processes of the aforementioned user control panel, such as the number of tasks, duration, and difficulty. It also supports downloading and managing each user's training data, including behavioral performance results and near-infrared data. Developed using Python and PyQt, the management control panel also has database access capabilities, enabling the download and management of user data.

[0092] Furthermore, embodiments of the present invention can also achieve alertness assessment based on historical data. In this case, the system further includes... The alertness assessment module can import historical data and output the corresponding alertness level.

[0093] Hardware interfaces: ECG data transmission, wristband data transmission, near-infrared data transmission, and behavioral data transmission.

[0094] Data Import: Displays import boxes for each dimension, allowing you to import the corresponding data. A file with at least one dimension must be entered; Offline file data import table

[0095] Once completed, click "OK" to perform an alertness assessment: output the corresponding alertness level; Data collection complete: Exit this evaluation interface.

[0096] like Figure 9 As shown, the system usage process is as follows: User Login: The interface displays a login box, including input boxes for basic information such as name and gender. After filling in the information, click submit to enter the training process. Select a training strategy; there are three training strategies: arousal training, cognitive resource training, and meta-attention monitoring training.

[0097] Equipment wearing: Open the acquisition software, the user wears the electrode cap, the right side of the interface displays the arrangement of the photoelectric plates and the channel quality. After the user has put on the cap and the channel signal quality is good, click "Confirm" to proceed to the next step.

[0098] PVT Pre-Test: The PVT test instructions will be presented. After confirming that you understand them, you will practice for 5 sessions. Then you will enter the formal test, which will last for 5 minutes. After the test, you will be reminded to proceed to the next step.

[0099] A red square will always be displayed in the center of the screen. Once a number (yellow) appears in the square, the participant is required to press the space bar as soon as possible. The number is the current number of milliseconds counted from when the number appeared. If the participant presses the space bar or does not press it within 5 seconds, the next trial will automatically begin. If the participant presses the space bar before a number appears, an exclamation mark (!) will be displayed in the square, and the next trial will begin.

[0100] Feedback training: A total of 4 feedback training sessions will be conducted, with a 30-second rest period between each session. Each training session will last 5 minutes (the duration and number of sessions for the practice and formal phases will be configured by the experiment administrator on the management control terminal).

[0101] Feedback metrics: The level of oxyhemoglobin in the brain region corresponding to the strategy after preprocessing. A 2-second moving average window is used to smooth the original oxyhemoglobin signal. The baseline is calculated by extracting the average signal value 2 seconds prior to each time step, and then the baseline is subtracted from the smoothed signal.

[0102] Feedback signal = Oxyhemoglobin level in the brain region corresponding to the strategy after pretreatment / Difficulty level The difficulty level is configured by the experiment administrator on the management side. The hemodynamic response is normalized to the range of 0-1 by the difficulty level and corresponds to the visual elements presented on the interface (different feedback training visual elements correspond to different training strategies selected in the login section).

[0103] Transfer training: Perform one transfer training session, lasting 5 minutes, without providing real-time feedback. Only after the 5-minute task is completed will the user be shown the highest and average level they could achieve during this training.

[0104] PVT post-test: Duration 5 minutes. After the test, complete all training. Click to submit experimental data and end the training process.

[0105] Hardware architecture Huichuang's portable near-infrared device is the core hardware module of the entire system. Its main function is to collect the user's brain neural activity data. This data will be used to provide biofeedback to help users with training. To ensure the accuracy and reliability of the data and its adaptability to long-duration scenarios, Huichuang's high-quality near-infrared acquisition equipment was selected.

[0106] Data transceiver unit: Responsible for receiving data transmitted from Huichuang's portable near-infrared device and transmitting it to the computer for processing. It performs functions including data reception and data transmission. The data reception function receives data signals sent from the portable near-infrared device. The data transmission function transmits the processed data to the computer, and data transmission can be performed wirelessly or via wired connection.

[0107] Computer: The computer will act as the central node, its functions including receiving data from near-infrared imaging devices, storing, processing, and analyzing data, and undertaking training tasks. The computer receives real-time brain oxygenation activity data through a connection to a portable near-infrared imaging device and stores it in a database. It uses processing and analysis software to process and analyze the data; signal processing functions can process and optimize the received signals to improve data quality and accuracy, extract useful information, and generate real-time feedback for training tasks. The management and control terminal supports training parameter configuration and data management, including downloading user training data and near-infrared data. The entire system aims to help users improve their cognitive abilities and attention levels through biofeedback training.

[0108] Parameter configuration.

[0109] Arousal training parameters: PVT pretest time, task time, PVT posttest time, and displayed image.

[0110] Cognitive resource training parameters: PVT pretest time, task time, PVT posttest time, background color, feedback sphere color, and font color.

[0111] Meta-attention monitoring training parameters: PVT pretest time, task time, PVT posttest time, task module type (choose 1 out of 3), feedback graph initial position, feedback graph target position.

[0112] Software architecture In this embodiment of the invention, the software architecture encompasses various programming languages, frameworks, and libraries. To provide a local desktop application running in a Windows environment, this application involves interaction with a portable near-infrared imaging device and database connectivity. The following is the technology stack and detailed software architecture: Operating System and Environment: Development and deployment are carried out under the Windows operating system. Python virtual environments are used to isolate and manage project dependencies, keeping the code's runtime environment clean and consistent.

[0113] Programming Language: Python is the primary development language, used for backend development, database operations, and user interface development. However, communication with near-infrared imaging equipment requires other languages. Python's ctypes library is used to call C / C++ libraries, and IPC mechanisms and socket communication are used to achieve communication between different programming languages. Additionally, JavaScript is used to handle some dynamic interactive functions of the user interface.

[0114] Development framework and libraries: PyQt5 is primarily used as the development framework for desktop applications. Additionally, SQLAlchemyORM is used for database connections and operations, PySerial is used for device communication, and NumPy and Pandas are used for data processing.

[0115] Database technology: MySQL is used as the database system. The PyMySQL and SQLAlchemy libraries in Python are used to interact with the MySQL database. Database management tools such as Navicat Premium are used for database design and maintenance.

[0116] Version control and code editor: Git is used as the version control system, GitHub is used for code hosting, and GitFlow workflow is used for branch management and version release. Visual Studio Code is used as the primary code editor, and its rich plugin system is utilized to improve development efficiency.

[0117] Development and testing tools: Postman is used for testing and debugging interfaces, PyTest is used for writing and running unit tests, and Locusty is used for performance testing and stress testing.

[0118] Code quality and continuous integration: To ensure code quality, Flake8 is used for code style checks, Pylint for code quality checks, and Mypy for type checking. Simultaneously, Jenkins is used for continuous integration and continuous deployment to accelerate the development process and promptly identify and fix issues.

[0119] The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the present invention. The scope of protection of the present invention is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to the present invention within its spirit and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method for enhancing alertness based on biofeedback, characterized in that, Includes the following steps: Step S1: Based on the reasons for the decline in vigilance among long-haul personnel, determine the vigilance enhancement feedback training task; Step S2: Collect physiological characteristic data of the trainees, determine the baseline threshold of the personalized physiological characteristic data of the trainees, and set the initial training difficulty of the feedback training task based on the determined baseline threshold. Step S3: Start the feedback training task for the current training phase, collect physiological characteristic data of the trainee in the current training phase of the feedback training task, determine the personalized physiological characteristic data phase threshold of the trainee in the current training phase, and set the phase training difficulty of the next phase of the feedback training task based on the phase threshold determined in the current phase. Step S4: Determine whether the feedback training task meets the completion requirements. If yes, proceed to step S5; otherwise, proceed to step S3. Step S5: End the feedback training task.

2. The method for enhancing alertness based on biofeedback according to claim 1, characterized in that: The physiological characteristic data refers to the oxygenated hemoglobin level in the prefrontal cortex of the trained user.

3. The method for enhancing alertness based on biofeedback according to claim 2, characterized in that: The physiological characteristic data were collected using a portable near-infrared brain functional imaging device.

4. The method for enhancing alertness based on biofeedback according to claim 1, characterized in that: The reasons for the decline in alertness among long-haul workers include prolonged isolation in enclosed spaces, irregular shift work, and continuous operations.

5. The method for enhancing alertness based on biofeedback according to claim 4, characterized in that: The alertness enhancement feedback training tasks include meta-attention monitoring training, arousal training, and cognitive resource training, respectively corresponding to factors such as long-term isolation and confinement, irregular shift work, and continuous work.

6. The method for enhancing alertness based on biofeedback according to claim 5, characterized in that: The training scenarios for the arousal training and meta-attention monitoring training are matched with the work scenarios of the trainees in long-haul operation tasks.

7. The method for enhancing alertness based on biofeedback according to claim 5, characterized in that, Also includes: Step S6, transfer training: After the feedback training task is completed, transfer training is performed without real-time feedback.

8. An alertness enhancement system based on biofeedback, characterized in that, include: Near-infrared data acquisition module is used to acquire optical signal data from the prefrontal cortex of the training user; The biofeedback training module is used to provide feedback training tasks and receive raw data collected by the near-infrared data acquisition module during the feedback training task stage. It processes the data, extracts HBO indicators, adjusts the difficulty of the feedback training task based on the extracted HBO data, and adjusts the visual feedback parameters during the visualization biofeedback stage. The database uses a relational data format to store the basic information of the trainees and the training data, and provides data editing and download functions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements claim 1. The vigilance enhancement method based on biofeedback as described in any one of the 7 claims.

10. A computer device, 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 program, it implements claim 1. The vigilance enhancement method based on biofeedback as described in any one of the 7 claims.

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