Emotion navigation system using intelligent human-based illumination and method thereof

Abstract

An emotion navigation method is executed by an intelligent human factor irradiation system (100, 600), the intelligent human factor irradiation system (100, 600) is composed of a cloud end (610), an irradiation field domain end (620) and a client device (630) and is connected with each other through the Internet, emotion coordinate information is stored in the cloud end (610), and the emotion navigation method comprises the following steps: step 1, an initial emotion (3510) is confirmed, and then the initial emotion (3510) is sent to the cloud end (610); the method comprises the following steps: step 1, setting a target emotion (3520), step 2, re-setting a target emotion (3520), step 3, selecting a relay emotion to complete emotion navigation path setting (3530), step 4, editing a multispectral formula of the relay emotion and the target emotion according to an emotion navigation path (3540), step 5, executing an irradiation program of the multispectral formula of the relay emotion and the target emotion (3550), and step 6, if it is judged that the target emotion is not reached (3560), executing the irradiation program of the multispectral formula of the relay emotion and the target emotion (3550). Physiological signal data of the user is further provided, and a multispectral recipe of a corresponding emotion that can be adjusted to the target emotion is found based on the physiological signal data of the user (3590).

Description

Emotional navigation system and method using intelligent human-induced lighting Technical Field

[0001] The present invention provides an intelligent human-centered lighting method, particularly a method that measures the emotional state through human physiological signals and then adjusts the luminous spectrum, thereby achieving emotional navigation to shift emotions through the intelligent human-centered lighting process. Background Art

[0002] Humans are highly emotional beings, experiencing varying emotions depending on their state of mind. These emotions range from excitement, amusement, anger, disgust, fear, happiness, sadness, calmness, and neutrality. When negative emotions (such as anger, disgust, and fear) are not properly managed, they can cause psychological harm or trauma, ultimately leading to mental illness. Therefore, in today's highly competitive and stressful society, providing a timely emotional resolution, relief, or treatment system that meets user needs presents significant business opportunities.

[0003] Modern medical equipment now uses functional magnetic resonance imaging (fMRI) systems to measure changes in blood dynamics caused by neuronal activity. Due to its non-invasive nature and low radiation exposure, fMRI is currently primarily used for brain and spinal cord research in humans and animals. Electroencephalography (EEG) can also be used to examine subjects using the same emotional stimulation to reveal responses to different emotions. For example, distinct EEG patterns are observed for fear and happiness. When observing the responses to specific emotions under fMRI and EEG, for example, when stimulating happiness (e.g., through image induction and facial emotion recognition), fMRI blood oxygenation level-dependent contrast (BOLD) responses have revealed significant responses in the medial prefrontal cortex (MPFC) compared to corresponding emotions (anger and fear). In contrast, for example, when examining the blood oxygen concentration-dependent contrast (BOLD) response during fear and anger, fMRI reveals a significant response in the amygdala, indicating that the two emotions are responsive to distinct brain regions. Therefore, the BOLD response in these different brain regions can be used to clearly identify the subject's current emotion. Furthermore, when using an electroencephalogram (EEG) to examine and measure subjects, using the same emotional stimulation method, distinct brainwave patterns can be observed for fear and happiness. Therefore, these different EEG patterns can also be used to identify the subject's current emotion. As mentioned above, fMRI distinguishes emotions through different BOLD responses, while EEG distinguishes emotions through different EEG patterns. Obviously, the methods used by the two to judge the test subject's emotions and the content of the records are completely different. Therefore, based on current technology, it is impossible to use the brainwave patterns of an electroencephalogram (EEG) to replace the blood oxygen concentration-dependent contrast (BOLD) response of a functional magnetic resonance imaging (fMRI) system for the test results of the same emotion on the same test subject.

[0004] The above discussion of the use of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) for emotion judgment is based on the fact that fMRI systems are very expensive and large, making them unsuitable for commercial systems and methods for human-induced lighting. Similarly, if only EEG brainwave patterns are used to judge a subject's emotions, different subjects may have different brainwave patterns for different emotions. Therefore, it is currently impossible to use the blood oxygen concentration-dependent contrast (BOLD) response of a functional magnetic resonance imaging (fMRI) system alone, or the brainwave patterns of an EEG system alone, to create a commercially viable human-induced lighting method and system through light recipe editing.

[0005] Summary of the Invention

[0006] Based on the above description, the present invention provides a method for correlating EEG brainwave patterns with blood oxygenation-dependent contrast (BOLD) data from functional magnetic resonance imaging (fMRI). This method, using EEG brainwave patterns, can be applied to an intelligent human-centric lighting system as a shared platform for providing human-centric lighting. Furthermore, the present invention provides a method and system for emotional navigation that can achieve emotional transformation through intelligent human-centric lighting.

[0007] The present invention first provides a method for implementing emotion navigation using an intelligent human-centered lighting system. The method comprises: in step 1, determining the "initial emotion," then, in step 2, setting the "target emotion," and, in step 3, determining the "relay emotion," completing the "emotion navigation path." Next, in step 4, compiling a multispectral recipe related to the "relay emotion" and "target emotion" based on the "emotion navigation path." Next, in step 5, executing the lighting program for the multispectral recipe. In step 6, if the "target emotion" is determined not to have been reached, further providing the user's physiological signal data and, based on the user's physiological signal data, finding a multispectral recipe that can be adjusted to the "target emotion," repeating steps 4-6. Finally, if the "target emotion" is determined to have been reached, the lighting program is terminated.

[0008] The present invention further provides a method for performing emotion navigation using an intelligent human-centered lighting system. The method is performed using an intelligent human-centered lighting system comprising a cloud, a lighting field, and client devices, interconnected via the Internet. The cloud stores emotion coordinate information. The emotion navigation method is characterized by:

[0009] Step 1: confirming the user's "initial emotion" by using a physiological monitoring device or IAPS stimulation at the illumination field end, and storing the "initial emotion" in the cloud;

[0010] Step 2, setting the "target emotion", is to set it on the client device according to the user's needs and store the "target emotion" in the cloud;

[0011] Step 3, selecting "relay emotion" to complete the emotion navigation path setting, is to connect the client device to the cloud via the Internet, select the "relay emotion" from the emotion coordinate information in the cloud, and store the "relay emotion" in the cloud;

[0012] Step 4, editing the "Multispectral Recipe", is to edit the "Multispectral Recipe" corresponding to the "Target Emotion" and the "Multispectral Recipe" corresponding to the "Relay Emotion" selected by the emotion navigation path;

[0013] Step 5: Executing the "multi-spectral recipe" lighting program corresponding to the "relay emotion" is to control the lighting group in the lighting field end through the client device to perform a lighting process for a set time according to the "multi-spectral recipe" corresponding to the "relay emotion";

[0014] Step 6, determining whether the user has reached a "neutral mood", is to determine whether the user's mood has reached the "neutral mood" through a physiological monitoring device;

[0015] Step 7, executing the lighting program of the "multi-spectral recipe" corresponding to the "target emotion", is to determine that the user has reached the "neutral emotion", and then control the lighting group in the lighting field end through the client device to perform a lighting process of a set time according to the "multi-spectral recipe" corresponding to the "target emotion" on the user;

[0016] Step 8, determining whether the "target emotion" has been reached, is to determine whether the user's emotion has reached the "target emotion" through the physiological monitoring device;

[0017] Step nine, stopping the lighting process, is to stop the lighting process when it is determined that the user's emotion has reached the "target emotion".

[0018] The present invention further provides a method for performing emotion navigation using an intelligent human-centric lighting system. The human-centric lighting system comprises a cloud, a lighting field, and client devices, interconnected via the Internet. The cloud stores emotion coordinate information and the user's personal physiological data. The emotion navigation method is characterized by:

[0019] Step 1: confirming the user's "initial emotion" by using a physiological monitoring device or IAPS stimulation at the illumination field end, and storing the "initial emotion" in the cloud;

[0020] Step 2, setting the "target emotion" is to set it on the client device according to the user's needs and store the "target emotion" in the cloud;

[0021] Step 3, obtaining the user's personal physiological data, is to obtain the user's personal physiological data stored in the memory module from the client device to the cloud;

[0022] Step 4, selecting "relay emotion" to complete the emotion navigation path setting, is to connect the client device to the cloud via the Internet, select the "relay emotion" from the emotion coordinate information in the cloud, and store the "relay emotion" in the cloud;

[0023] Step 5: Editing the "Multispectral Recipe" is to edit the "Multispectral Recipe" corresponding to the "Target Emotion" and the "Multispectral Recipe" corresponding to the "Relay Emotion" based on the "Relay Emotion" selected by the emotion navigation path and the light recipe of the "Target Emotion" and the user's personal physiological data;

[0024] Step 6: Executing the "multi-spectral recipe" lighting program of the "relay emotion" is to control the lighting group in the lighting field end through the client device to perform a lighting process for a set time according to the "multi-spectral recipe" corresponding to the "relay emotion";

[0025] Step 7, determining whether the user has reached a "neutral mood", is to determine whether the user's mood has reached the "neutral mood" through a physiological monitoring device;

[0026] Step 8, executing the lighting program of the "multi-spectral recipe" for the "target emotion", is to determine that the user has reached the "neutral emotion", and then control the lighting group in the lighting field end through the client device to perform a lighting process for a set time according to the "multi-spectral recipe" corresponding to the "target emotion";

[0027] Step nine, determining whether the target emotion has been reached, is to determine whether the user's emotion has reached the target emotion through a physiological monitoring device;

[0028] Step 10, stopping the lighting process, is to stop the lighting process when it is determined that the user's emotion has reached the "target emotion".

[0029] In the above-mentioned method for performing emotion navigation, the user's personal physiological data can be further used to adjust the lighting parameters of the "multi-spectral recipe".

[0030] In the above-mentioned method for performing emotion navigation, the lighting parameters of the "multi-spectral recipe" include at least the color temperature, illuminance, flicker frequency, and color rendering index (Ra) of the lighting.

[0031] In the above-mentioned method for performing emotion navigation, the "multispectral formula" is a formula formed by the surrounding system score (LSS):

[0032] LSS=tx[axf eeg (freq)+bxf eeg (Ra)+cxf eeg (CTI)+dxf eeg (I)], where a, b, c, d are the coefficients of variation, CTI is the color temperature, and t is the exposure time.

[0033] This invention provides a method for achieving emotional navigation. After determining the user's current emotion, the system first applies a complementary spectrum of intermediate emotions to the initial emotion, followed by a second phase of target emotion spectrum exposure. This allows the user to achieve the target emotion's indicators. In particular, after confirming that the user's emotions have shifted to a neutral state after the complementary emotion spectrum exposure in the first phase, the second phase of target emotion spectrum exposure can significantly improve the target emotion's indicators.

[0034] Furthermore, if a neutral mood has not yet been reached after the relay emotional exposure, the user's personal physiological data can be further provided. This personal physiological data is a type of health check physiological data for the user, and is adjusted accordingly to adjust the color temperature, illuminance, flicker frequency, and color rendering index (Ra) of the user's lighting. Only after confirming that the mood has shifted to a neutral mood or near the emotional coordinate origin can the target emotion index be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] FIG1a is a raw data collection framework for human physiological and emotional responses to light according to the present invention;

[0036] FIG1b is a flowchart of collecting raw data on human physiological and emotional responses to light according to the present invention;

[0037] FIG1c is a flow chart of the present invention showing the determination process of a person's response to specific physiological emotions due to illumination;

[0038] FIG2a is a method of establishing a brainwave diagram of a person's physiological and emotional response to light exposure according to the present invention;

[0039] FIG2 b is a lighting database constructed by the present invention for users to perform effective ergonomic lighting;

[0040] FIG3 is a system architecture diagram of the intelligent human-induced lighting system of the present invention;

[0041] FIG4 a is an emotion coordinate system of the present invention;

[0042] FIG4 b is an EEG diagram of a specific emotion according to the present invention;

[0043] FIG4 c is an electroencephalogram of the emotion transfer test 1 of the present invention;

[0044] FIG4 d is an electroencephalogram of the emotion transfer test 2 of the present invention;

[0045] FIG4e is an electroencephalogram of the emotion transfer test 3 of the present invention;

[0046] FIG4 f is a navigation path of the emotion navigation example 1 of the present invention;

[0047] FIG4g is a navigation path of the emotional navigation example 2 of the present invention;

[0048] FIG4h is a navigation path of the emotional navigation example 3 of the present invention;

[0049] FIG5 is a method for performing emotion navigation according to the present invention;

[0050] FIG6 is another method for performing emotion navigation according to the present invention; and

[0051] FIG. 7 is another method for performing emotion navigation according to the present invention. DETAILED DESCRIPTION

[0052] In the following description of this invention, the functional magnetic resonance imaging system is referred to as the "fMRI system," the electroencephalogram (EEG) is referred to as the "EEG," and the blood oxygen concentration-dependent contrast (BOLD) is referred to as the "BOLD." Furthermore, in the color temperature test embodiment of this invention, the test is performed in 100K increments. However, to avoid excessive length, the specific emotions referred to in the following description refer to excitement, happiness, or pleasure, and the corresponding specific emotional responses will be described using color temperatures of 3000K, 4000K, and 5700K as examples. Therefore, the invention is not limited to these three color temperature embodiments. Furthermore, to facilitate a thorough understanding of the technical content of this invention by those skilled in the art, related embodiments and examples are provided herein. When reading the embodiments of this invention, please refer to the drawings and the following description. The shapes and relative sizes of the components in the drawings are provided solely to facilitate understanding of the present embodiments and are not intended to limit the shapes and relative sizes of the components. For this purpose, please note that the following description of the embodiments of this invention is provided in conjunction with the accompanying drawings. The shapes and relative sizes of the components in the drawings are provided solely to facilitate understanding of the present embodiments and are not intended to limit the shapes and relative sizes of the components.

[0053] The present invention uses an fMRI system to measure physiological signals, understanding the correspondence between light spectrum and emotion in the brain and developing a preliminary mechanism for how light influences human physiological and psychological responses. Because the fMRI system's brain images can determine which part of the brain experiences hyperemia when stimulated by light, they can also record the BOLD response. This BOLD response to brain hyperemia is also known as a "blood oxygen concentration-dependent increase." Therefore, the present invention can accurately and objectively infer the subject's physiological emotional changes based on the fMRI system's recorded brain hyperemia data under various emotions and the "blood oxygen concentration-dependent increase" response. Based on the physiological emotions confirmed by the fMRI system, electroencephalography (EEG) is used to record brainwave changes and establish a correlation between the two. This approach aims to replace the fMRI system's emotional judgments with those recorded by the EEG.

[0054] Therefore, the main purpose of the present invention is to record the BOLD response of the subject to the specific emotion while the subject is being tested with an fMRI system. This allows the subject to identify specific "effective color temperatures" that have a synergistic effect on the specific emotion, and to use this color temperature as the "effective color temperature" corresponding to the specific emotion. The subject is then illuminated with light at the "effective color temperature," and the brainwave pattern under the "effective color temperature" is recorded using an electroencephalogram (EEG). Once a correlation is established between the specific EEG brainwave pattern and the specific BOLD response, the EEG brainwave pattern can be used to assist in determining the user's emotional changes. This results in a commercially viable human-centered lighting system and method. This eliminates the need for expensive fMRI systems to implement human-centered lighting systems, reduces operating costs, and further meets customized service needs.

[0055] First, please refer to Figure 1a, which is the original data collection framework of the present invention's human-induced lighting response to physiological emotions. As shown in Figure 1a, the intelligent human-induced lighting system 100 is activated in an environment that has been configured with various adjustable lighting modules (for example, a test space) and provides light signal lighting parameters such as adjustable spectrum, light intensity, flicker rate, and color temperature. The present invention uses a compatible image interaction platform 110 (fMRI compatible image interaction platform) formed by an fMRI system to target different target emotions. It uses voice and image emotional guidance, and at the same time uses a specific effective spectrum for 40 seconds of stimulation to observe changes in the blood oxygen content in the test subject's brain to verify whether the "effective color temperature" can significantly induce the test subject's emotional response. The details are as follows.

[0056] Next, please refer to Figure 1b and Figure 1c, wherein Figure 1b is a flowchart of the original data collection of the human-induced lighting response to physiological emotions of the present invention, and Figure 1c is a judgment process of the human-induced lighting response to specific physiological emotions. As shown in step 1100 in Figure 1b, each tester is already on the compatible image interaction platform 110. Then, each tester is guided to stimulate various emotions through known pictures. Afterwards, as shown in step 1200, the BOLD response of the tester's brain to various emotions after being stimulated by the pictures is recorded through the compatible image interaction platform 110. Then, as shown in step 1300, the tester is visually stimulated by providing a spectrum of different color temperature parameters through lighting. For example: using LED lamps with electronic dimmers to provide a spectrum of different color temperature parameters. In an embodiment of the present invention, nine sets of visual stimuli with different color temperatures are provided: 2700K, 3000K, 3500K, 4000K, 4500K, 5000K, 5500K, 6000K, and 6500K. After each 40-second effective light exposure and stimulation, the subject can be given a one-minute ineffective light source (full-spectrum, flicker-free white light) to achieve emotional relaxation. Furthermore, the subject can be given a 40-second counter-effect light stimulus to observe whether areas that were responsive to the original effective light stimulus exhibit a decreased response. In an embodiment of the present invention, after the compatible image interaction platform 110 has recorded the BOLD response of the test subject to a specific emotion in the brain after being stimulated by the picture, spectra with different color temperature parameters are provided to provide visual stimulation to the test subject. As shown in step 1310 in Figure 1c, a spectrum with a color temperature of 3000K is provided. Then, as shown in step 1320, a spectrum with a color temperature of 4000K is provided. Finally, as shown in step 1330, a spectrum with a color temperature of 5700K is provided to provide visual stimulation to the test subject.

[0057] Next, as shown in step 1400, the compatible image interaction platform 110 records the BOLD responses of the subject's brain to various emotions after being exposed to light stimulation. In this embodiment of the present invention, after the subject is exposed to light stimulation with different color temperature parameters, the compatible image interaction platform 110 sequentially records the BOLD responses of the corresponding emotionally responsive regions in the subject's limbic system. Different emotions trigger different areas of the limbic system, and the limbic system regions with emotional responses are shown in Table 1 below.

[0058] Table 1

[0059] The compatible image interaction platform 110 records the BOLD response results in specific brain regions. This response result is determined by calculating the area size of these emotional response sites based on the number of limbic systems with BOLD emotional responses in the brain region. For example, the larger the area with BOLD emotional responses, the stronger the response to a specific physiological emotion. In an embodiment of the present invention, as shown in step 1410 in Figure 1c, the BOLD response results are recorded after irradiation with a spectrum having a color temperature of 3000K. Next, as shown in step 1420, the BOLD response results are recorded after irradiation with a spectrum having a color temperature of 4000K. Finally, as shown in step 1430, the BOLD response results are recorded after irradiation with a spectrum having a color temperature of 5700K. After the subject underwent the illumination procedure to induce excitement, the compatible image interaction platform 110 recorded the BOLD response results in specific brain regions, as shown in Table 2. Clearly, irradiation at a color temperature of 3000K enhances excitement. Therefore, according to the results of the present embodiment, the optimal stimulating color temperature is between 3000K and 4000K. However, it should be noted that irradiation at a color temperature of 5700K has a negative inhibitory effect on excitement.

[0060] Table 2

[0061] After the subjects went through a lighting procedure to induce happiness, the compatible image interaction platform 110 recorded BOLD responses in specific brain regions, as shown in Table 3. Clearly, illumination at a color temperature of 4000K enhanced BOLD responses in the happiness context more than illumination at the other two color temperatures. Therefore, according to the results of this embodiment of the present invention, the optimal stimulus color temperature for happiness is around 4000K.

[0062] Table 3

[0063] After the subjects underwent a lighting procedure that induced amusement, the compatible image interaction platform 110 recorded BOLD responses in specific brain regions, as shown in Table 4. Clearly, all three color temperatures enhanced the BOLD response in amusement-related brain regions, with higher color temperatures showing stronger BOLD responses.

[0064] Table 4

[0065] After the subjects underwent a lighting procedure that induced emotions related to the Serene Index, the compatible image interaction platform 110 recorded BOLD responses in specific brain regions, as shown in Table 5. Clearly, exposure to a low color temperature of 3000K enhanced feelings of tranquility or relaxation. However, higher color temperatures negatively inhibited Serene Index emotions, with the negative inhibitory effect becoming more pronounced at higher color temperatures.

[0066] Table 5

[0067] Next, as shown in step 1500, the specific color temperature that increases the BOLD-dependent response to a specific emotion is screened through illumination. This specific color temperature is referred to as the "effective color temperature." In this embodiment, the BOLD response results of the corresponding emotionally responsive regions in the subject's limbic system are recorded to summarize the stimulating effects of color temperature on the brain, as shown in Tables 2 through 5. To screen the specific color temperatures that increase the BOLD-dependent response to a specific emotion, the specific color temperatures that maximize the response to that specific emotion (i.e., the area with the largest BOLD response) are calculated.

[0068] As shown in step 1510 in FIG. 1c , after the tester has recorded the excitement emotion on the compatible image interaction platform 110 and completed the illumination procedure, the maximum response value is calculated. For example, according to the records in Table 2, the total score for 3000K (577) is subtracted from the total score for 4000K (226), resulting in 351. Next, the total score for 3000K (577) is subtracted from the total score for 5700K (-105), resulting in 682. Therefore, the total response value after the excitement emotion is induced and under the illumination of 3000K is 1033.

[0069] Next, as shown in step 1520 in FIG. 1c , after the tester has recorded the excitement emotion on the compatible image interaction platform 110 and completed the illumination procedure, the maximum response value is calculated. For example, according to the records in Table 2, the total score for 4000K (226) is subtracted from the total score for 3000K (577), resulting in -351. Next, the total score for 4000K (266) is subtracted from the total score for 5700K (-105), resulting in 371. Therefore, the total response value after the excitement emotion is induced and under the illumination of 4000K is 20.

[0070] Next, as shown in step 1530 in FIG1c , after the tester has recorded the excitement emotion on the compatible image interaction platform 110 and completed the illumination procedure, the maximum response value is calculated. For example, according to the records in Table 2, the total score for 5700K (-105) is subtracted from the total score for 3000K (577), resulting in -682. Next, the total score for 5700K (-105) is subtracted from the total score for 4000K (266), resulting in -371. Therefore, the total response value after the excitement emotion is induced and under the illumination of 5700K is -1033.

[0071] According to the above calculations, after excitement is induced, an illuminance of 3000K maximizes the response. This means that 3000K produces a more pronounced boost in excitement (i.e., compared to the calculated total scores for 4000K and 5700K, 3000K achieves the highest total score of 1033). Therefore, 3000K is used as the "effective color temperature" for excitement. For other emotions, such as happiness, joy, and tranquility, different "effective color temperatures" can be obtained using the calculations from steps 1510 to 1530, as shown in Table 6.

[0072] Table 6

[0073] Next, based on the statistical results in Table 6, the effective color temperature can be interpreted as the result of a specific physiological emotion-dependent response. This effective color temperature can be considered the "enhancement spectrum" of the fMRI system's "blood oxygen concentration dependence" on a particular emotion. For example, an effective color temperature of 3000K can represent the fMRI system's "enhancement spectrum" for the emotion of "excitement," where the optimal stimulation color temperature should fall between 3000K and 4000K. For example, an effective color temperature of 4000K can represent the fMRI system's "enhancement spectrum" for the emotion of "happiness," where the optimal stimulation color temperature for happy situations is around 4000K. For example, an effective color temperature of 5700K can represent the fMRI system's "enhancement spectrum" for the emotion of "pleasure." For example, an effective color temperature of 3000K can also represent the fMRI system's "enhancement spectrum" for the emotion of "tranquility," where the optimal stimulation color temperature for tranquility is around 3000K.

[0074] Finally, as shown in step 1600, a "light recipe" database of "enhanced spectra" corresponding to specific emotional effects can be established within the fMRI system. By stimulating the subject with the aforementioned human-induced lighting parameters, observing and recording the BOLD response of the subject's brain during the light stimulation using the fMRI system, and simultaneously recording fMRI brain images, determining which part of the brain experiences an additive "blood oxygen concentration dependency" response to the light stimulation, a specific effective color temperature can be considered the "light recipe" for the fMRI "blood oxygen concentration dependency" of a specific emotion. Clearly, based on the statistical results of additive BOLD responses to specific emotions at specific "effective color temperatures" as shown in Table 6, the present invention objectively infers the "light recipe" for the subject's specific physiological emotional response. This "light recipe" serves as evidence for the physiological emotional response that produces the strongest additive effect for a specific emotion (including excitement, happiness, pleasure, anger, disgust, fear, sadness, calmness, or neutrality).

[0075] It should be emphasized that in the entire implementation process of Figures 1b and 1c of the present invention, after conducting complete tests on multiple specific emotions on 100 test subjects, the statistical results in Table 6 were obtained based on this. For example, with respect to the emotion of excitement, providing an effective color temperature of 3000K as the "enhanced spectrum" under the physiological emotional response of excitement as a "light formula" can make the test subject's excitement produce a physiological emotional response with the strongest additive effect. For example, with respect to the emotion of happiness, providing an effective color temperature of 4000K as the "enhanced spectrum" under the physiological emotional response of happiness as a "light formula" can make the test subject's happy emotion produce a physiological emotional response with the strongest additive effect. For another example, with respect to the emotion of pleasure, providing an effective color temperature of 5700K as the "enhanced spectrum" under the physiological emotional response of pleasure as a "light formula" can make the test subject's pleasure produce a physiological emotional response with the strongest additive effect.

[0076] Furthermore, it should be emphasized that the three color temperatures described above represent only three examples of the present invention, and are not intended to be the only lighting systems to be used as an "enhanced spectrum" for physiological emotional responses. In practice, the entire process illustrated in Figures 1b and 1c can be repeated for different emotions (including excitement, happiness, joy, anger, disgust, fear, sadness, calmness, or neutrality) by increasing the color temperature from 2000K to 100K in increments of 100K. Therefore, Table 6 of the present invention only discloses a portion of the results and is not intended to limit the present invention to these examples.

[0077] Next, the present invention aims to establish an artificial intelligence model for the correlation between brain waves and brain images of general physiological emotions. This allows for future commercialization, allowing the inference of physiological emotions directly from other sensing devices without the need for an fMRI system. The sensing device used in the present invention includes an EEG. In the following embodiment, an EEG is used to establish physiological emotional responses to light. Alternatively, an eye tracker 150 or an expression recognition technology-assisted program 170 can be used to replace the fMRI system for physiological emotional responses. However, the eye tracker 150 and expression recognition technology-assisted program 170 will not be disclosed in this invention, but are hereby disclaimed.

[0078] Please refer to Figure 2a, which illustrates a method for grading physiological and emotional responses to human-induced lighting using electroencephalograms. As shown in Figure 2a, the present invention utilizes an electroencephalogram (EEG) device 130 to establish physiological and emotional responses to human-induced lighting. The method includes the following steps: First, as shown in step 2100, the "enhanced spectrum" database information in Table 6 is stored in the memory of the EEG device 130. Next, as shown in step 2200, the subject wears the EEG device and is guided through various emotional stimulations using known images or video elements. For example, the International Affection Picture System (IAPS) can be used as the emotional stimulation for known images or videos. Next, as shown in step 2300, the intelligent human-induced lighting system 100 is activated to provide variable light signal parameters, such as spectrum, light intensity, flicker rate, and color temperature, to stimulate the subject's light. The EEG device 130 then records the EEG data after exposure to light at different color temperatures under specific emotions. For example, in this embodiment, each subject is first stimulated with excitement. Subsequently, as in steps 2310 to 2330, different color temperatures of 3000K, 4000K, and 5700K are provided to the subject for light stimulation. EEG patterns of the subject's specific emotions during the excitement stimulation and after the different color temperature light stimulation are recorded and stored in the memory of the electroencephalogram 130. In this embodiment of the present invention, the EEG patterns of 100 subjects after the specific emotional stimulation and light exposure are recorded, thus requiring a larger memory.

[0079] Next, as shown in step 2400, the EEG pattern files corresponding to specific emotions (e.g., excitement, happiness, and joy) stored in the EEG device 130's memory are learned through artificial intelligence learning. Because the EEG device 130 can only memorize brainwave waveforms, the EEG patterns currently stored in the EEG device 130's memory are those generated in response to specific known triggering emotional stimuli and illumination at different color temperatures. It should be noted that in actual testing, different test subjects will experience different EEG patterns in response to the same emotional stimulus and illumination at the same color temperature. Therefore, during the learning process in step 2400, the present invention utilizes information from the "light recipe" database of the "enhanced spectrum" to classify EEG files associated with specific emotions. For example, for EEG files associated with excitement, only EEG files generated at a color temperature of 3000K across different test subjects are grouped together. For another example, for EEG files associated with happiness, only EEG files generated at a color temperature of 4000K across different test subjects are grouped together. For another example, for EEG files associated with pleasure, only EEG files generated at a color temperature of 5700K across different test subjects are grouped together. The EEG device is then trained using machine learning within artificial intelligence. In this embodiment of the present invention, a transfer learning model is specifically selected for learning and training.

[0080] During the learning and training process using the transfer learning model in step 2400, the learning and training is performed by statistically analyzing, calculating, and comparing similarities for groups of EEG patterns for specific emotions. For example, when learning and training a group of EEG patterns with a color temperature of 3000K, the learning and training is performed by statistically analyzing, calculating, and comparing the most similar and least similar EEG patterns for excitement, among the EEG patterns with a color temperature of 3000K. For example, the EEG patterns with the highest similarity can be considered the EEG patterns with the strongest emotions, while the patterns with the lowest similarity can be considered the EEG patterns with the weakest emotions. The EEG patterns ranked for the strongest emotions can be used as the "target value," while the patterns ranked for the weakest emotions can be used as the "starting value." For ease of explanation, the most similar EEG pattern is used as the "target value," while the least similar EEG pattern is used as the "starting value." Different scores are assigned, for example, a similarity score of 90 is assigned to the "target value," and a similarity score of 30 is assigned to the "starting value." Similarly, complete the EEG files for the levels of happiness in the 4000K color temperature category and the levels of surprise in the 5700K color temperature category. The "starting value" and "target value" can be used to form a similarity score range.

[0081] Next, as shown in step 2500, an artificial intelligence EEG classification and grading database (referred to as the artificial intelligence EEG file database) is established. After step 2400, the EEG file groups of various specific color temperatures are assigned "target value" scores and "starting value" scores, forming a database of the grading results, which is stored in the memory of the electroencephalogram (EEG) device 130. The purpose of establishing the EEG classification and grading database in step 2500 is to obtain an EEG file of an unknown subject after being exposed to specific emotional stimulation and illuminated with a specific color temperature. By comparing the similarity score range of this EEG file with the EEG files in the database, the current state of the unknown subject's cerebral congestion reaction can be determined or inferred. The detailed process is shown in Figure 2b.

[0082] Next, please refer to Figure 2b, which illustrates the present invention's construction of a lighting database for effective human-factored lighting. First, as shown in step 3100, the subject wears an electroencephalogram (EEG) monitor 130 and views images that induce specific emotions. Next, as shown in step 3200, the human-factored lighting system 100 is activated to apply a "light recipe" to the subject. This occurs in an environment (e.g., a testing space) equipped with various adjustable multi-spectral lighting modules. The management and control module 611 (shown in Figure 3) provides variable light signal parameters such as spectrum, light intensity, flicker rate, color temperature, and exposure time. Next, as shown in step 3300, the subject's EEG files after emotional induction and lighting exposure are acquired and recorded, and stored in the memory module 617 (shown in Figure 3) of the cloud 610. Next, as shown in step 3400, the artificial intelligence EEG file database is imported into the management and control module 611. The management and control module 611 sets a score to determine whether the similarity is sufficient. For example, a similarity score of 75 or above indicates that the subject's brain congestion response is sufficient. Next, as shown in step 3500, the management and control module 611 compares the subject's EEG file with the artificial intelligence EEG file for similarity. For example, if the similarity score after the comparison of the subject's EEG file is 90, the management and control module 611 immediately determines that the subject's brain congestion response is sufficient and proceeds to step 3600, terminating the specific emotion human factor illumination test. Next, step 3700 records the human factor illumination parameters at which the subject's brain congestion response reached the desired level of stimulation and stores them in the database in the memory module 617.

[0083] Next, in step 3500 of FIG2b , if the similarity score between the subject's EEG file and the artificial intelligence EEG file is 35, the management and control module 611 determines that the subject's brain hyperemia response is insufficient and proceeds to step 3800 . The management and control module 611 then continues to strengthen the human-factor illumination test, including increasing illumination time or intensity based on the similarity score. Step 3300 is then repeated to obtain EEG files after increasing illumination time or intensity. The human-factor illumination test is terminated only after the similarity score reaches a predetermined score of 75 or higher, proceeding through step 3400 . If the management and control module 611 determines that the subject's brain hyperemia response has reached a certain level of stimulation, a human-factor illumination parameter data file is created for the subject in step 3700 . Finally, the management control module 611 compiles each test subject's human factors lighting parameters into a "human factors lighting parameter database" and stores it in the memory module 617. Obviously, as more test subjects are included, the artificial intelligence EEG file database of the present invention will learn more EEG files, making the similarity score of the present invention increasingly accurate.

[0084] After establishing the artificial intelligence model for the "Human Factor Lighting Parameter Database," the present invention can, upon activation of the human factor lighting system 100, infer physiological and emotional changes in a new test subject's brain images simply by observing the electroencephalogram (EEG) analysis results from the electroencephalogram (EEG) monitor 130. This eliminates the need for expensive fMRI systems, as the artificial intelligence model for inferring physiological and emotional changes in brain images can be used. This allows the human factor lighting system 100 to be promoted and used commercially. Furthermore, to facilitate commercial use of the "Human Factor Lighting Parameter Database," the "Human Factor Lighting Parameter Database" can be stored in an internal private cloud 6151 within the cloud 610.

[0085] Next, please refer to Figure 3, which is a system architecture diagram of the intelligent human-centered lighting system 600 of the present invention. As shown in Figure 3, the overall architecture of the intelligent human-centered lighting system 600 of the present invention can be divided into three blocks, including: a cloud 610, a lighting field end 620, and a client device 630. These three blocks are connected via the Internet. Therefore, the three blocks can be distributed in different areas, or they can be configured together. The cloud 610 further includes a management and control module 611, which is used for cloud computing, cloud environment construction, cloud management, and use of cloud computing resources. It also allows users to access, construct, or modify the content in each module through the management and control module 611. The consumption module 613 is connected to the management and control module 611 to serve as a cloud service that users subscribe to and consume. Therefore, the consumption module 613 can access each module in the cloud 610. The cloud environment module 615 is connected to the management control module 611 and the consumption module 613 to divide the cloud backend environment into an internal private cloud 6151, an external private cloud 6153 and a public cloud 6155 (for example, a commercial cloud), etc., and can provide an interface for external or internal services of the system provider or user. The memory module 617 is connected to the management control module 611 to serve as a storage area for the cloud backend. The technical content required to be executed by each module in the present invention will be specifically described in different subsequent embodiments. The lighting field end 620 can communicate with the cloud 610 or the client device 630 via the Internet. Among them, a lamp group 621 composed of multiple light-emitting devices is configured in the lighting field end 620. And the client device 630 can communicate with the cloud 610 or the lighting field end 620 via the Internet. The client devices 630 of the present invention include general users and editors who use the intelligent human-centered lighting system 600 of the present invention for various business operations. All of these belong to the client devices 630 of the present invention. Representative devices or devices of the client devices 630 can be a fixed device with computing functions (including edge computing) or a portable intelligent communication device. In the following description, users, creators, editors, or portable communication devices can all represent client devices 630. In addition, in the present invention, the Internet can be an intelligent Internet of Things (AIoT).

[0086] The present invention undergoes a relatively complete electroencephalogram experiment process, for example, after having the subject wear the electroencephalogram 130, in step 2300, the intelligent human-factor lighting system 100 is activated to provide various adjustable spectra, including light signal parameters such as light intensity (illuminance), flicker rate, color temperature, and average color rendering index (Ra). After the subject is illuminated, the light signal parameters for each emotion on the emotion coordinate system shown in FIG4a can be obtained by using the results of the aforementioned BOLD brain area test and a literature search. The spectrum or light signal parameters of some emotions on the emotion coordinate system are summarized in Table 7 below:

[0087] Table 7

[0088] In Table 7, K refers to color temperature, lux refers to illuminance, Hz refers to flicker rate, and Ra refers to color rendering index (general color rendering index). Furthermore, Figure 4a and Table 7 show a trend: the emotions in coordinate zones I and III complement each other, while those in coordinate zones II and IV also complement each other. This phenomenon can serve as a guide for emotional transitions.

[0089] Obviously, in order to build a commercially viable human-centered lighting system and its methods, and to solve the problem of having to use an expensive fMRI system to implement the human-centered lighting system, it is necessary to use specific brainwave patterns of an electroencephalogram (EEG) to assist in judging the user's emotional changes, thereby further meeting customized service needs and at the same time reducing operating costs.

[0090] When performing emotion localization using fMRI and electroencephalography (EEG), the emotion results obtained by the fMRI system are used as a standard basis. We define this as the Limbic System Score (LSS). The LSS is used as a metric because the limbic system, including the hippocampus and amygdala, supports various emotions, behaviors, and long-term memory. For example, a higher BOLD index in fMRI indicates a greater emotional response. Alternatively, when the LSS represents the arousal of emotion, a higher value indicates greater emotional excitement. Therefore, the present invention uses the LSS to establish a formula that combines various light signal parameters, such as spectrum, light intensity, flicker rate, color temperature, and color rendering (Ra), to define the light signal parameters for each emotion on the emotion coordinate system shown in Figure 4a.

[0091] According to the paper "fMRI BOLD Correlates of EEG Independent Components: Spatial Correspondence With the Default Mode Network," published in the journal Front.Hum.Neurosci on November 27, 2018, two key findings were revealed: alpha waves surprisingly have no significant effect on emotion; and second, the correlations between delta waves, theta waves, beta2 waves, and gamma waves and brain blood oxygenation responses were -0.291, -0.26, 0.269, and -0.345, respectively.

[0092] Next, the present invention assigns different brainwaves corresponding to 13, 18, 14, and 17 brain regions, respectively. The number of brain regions represents the probability of affecting the peripheral system (LSS). For example, delta waves affect 13 brain regions. In Equation 1, the probability is 13 / (13+18+14+17)=0.2097. Therefore, the delta weight should be adjusted from -0.291 to -0.291 x 0.2097 = -0.0610. Using the same calculation method (theta = 18 / (13+18+14+17 = 0.2903, -0.26x0.2903 = -0.0755), we obtain Equation 1 as follows. This formula is used to derive the relationship between light parameters such as frequency (f), color rendering index (Ra), color temperature (Color Temperature Index, CTI), and light intensity (I): LSS = C1delta–C2theta+C3beta2–C4gamma Equation 1

[0093] Among them, coefficients C1 = -0.061, C2 = 0.0755, C3 = 0.0607 and C4 = 0.0945.

[0094] Next, we need to convert Equation 1 into an emotion equation related to (f, Ra, CTI, I) in the EEG. First, let the color temperature index of a certain emotion obtained by the fMRI system be a certain value, then fMRI(CTI) = LSS

[0095] Next, the EEG is decomposed into four light parameter results, and each light parameter is made equal to the corresponding parameter of fMRI.

[0096] Among them, 1 / W freq , 1 / W Ra , 1 / W CTI , 1 / W IIt is the adjustment ratio of each light parameter of the fMRI system and the electroencephalogram (EEG).

[0097] Then 1 / W CTI After the normalization process, the weight relationship between the four variable coefficients (a, b, c, d) can be obtained. Then, the final surrounding system score (LSS) formula can be obtained: LSS = tx[axf eeg (freq)+bxf eeg (Ra)+cxf eeg (CTI)+dxf eeg (I)] ………………………………………………..Procedure 3

[0098] Among them, a=w freq / w CTI b=w Ra / w CTI c=w CTI / w CTI =1 d=w I / w CTI

[0099] t = illumination time. It's important to note that a longer illumination time (t) indicates a longer exposure time for the user, leading to a stronger emotional response. Therefore, in the subsequent description of this invention, illumination time (t) is assumed to be 1.

[0100] According to the above formula, the present invention can obtain the light signal parameters of each emotion on the emotion coordinate system of FIG4a. For example, in a preferred embodiment of the present invention under the excitement scenario, the LSS formula is shown in the following equation 4: LSS = tx(-0.0002freq 2 +0.0393freq+0.0334Ra-0.2854CTI -0.0000009ΔI 2 +0.0004ΔI-4.4441) Equation 4

[0101] For example, in a preferred embodiment of the present invention under the happiness scenario, the LSS formula is shown in the following equation 5: LSS = tx(-0.0002freq 2 +0.0393freq+0.0334Ra-0.6374CTI -0.0000009ΔI 2 +0.0004ΔI-4.4436) Equation 5

[0102] Among them, the above ΔI 2Refers to the change in light intensity.

[0103] According to the above formula of the surrounding system score (LSS), the change in emotion under various light parameters can be calculated, for example:

[0104] LSS Example 1: If a 50Hz, Ra=90, 6000K color temperature lamp is illuminated by 200 lux within the user's visual range, how excited will the user feel after a specific time (t) of illumination?

[0105] Apply equation 4 and set t = 1, freq = 50 Hz, Ra = 90, CTI = (6000 - 3000) / 3000 = 1, ΔI = 200. Then we get

[0106] LSS = -0.1787, which means the excitement level decreased by 0.1787

[0107] LSS Example 2: If a 60Hz, Ra=95, 4000K color temperature lamp is illuminated within the user's visual range and the illuminance increases by 400 lux, how excited will the user feel after a specific time (t) of illumination?

[0108] Apply Equation 4 and set t = 1, freq = 60 Hz, Ra = 95, CTI = (4000 - 3000) / 3000 = 0.33, ΔI = 400. Then we get

[0109] ●LSS=0.4324, which means that the excitement level increased by 0.4324.

[0110] LSS Example 3: If a 60Hz, Ra=95, 4000K color temperature lamp has an illuminance increase of 400 lux within the user's visual range, how happy will the user feel after a specific time (t) of illumination?

[0111] Apply Equation 5 and set t = 1, freq = 60 Hz, Ra = 95, CTI = (4000-4000) / 4000 = 0, ΔI = 400. Then we get

[0112] ●LSS=0.5270, which means that happiness increased by 0.5270.

[0113] LSS Example 4: If the illuminance of a 50Hz, Ra=85, 2700K lamp increases by 400 lux within the user's visual range, how happy will the user feel after a specific time (t) of illumination?

[0114] Apply Equation 5 and set t = 1, freq = 50 Hz, Ra = 85, CTI = [(2700 - 4000) / 4000] = 0.325, and ΔI = 400.

[0115] ●LSS=-0.1871, which means that the sense of happiness decreased by 0.1871.

[0116] The calculation results of Examples 1 and 2 demonstrate that achieving the same level of excitement can involve many different approaches, such as increasing color rendering index (Ra), increasing flicker, increasing (or decreasing) light intensity (because it has an optimal value), and decreasing color temperature. This is because, in the excitement scenario, a higher CTI indicates a lower level of excitement. Conversely, varying degrees of excitement can also be achieved by adjusting color rendering index (Ra), flicker, light intensity, and color temperature. The calculation results of Examples 3 and 4 also yield the same conclusion. In this context, the present invention refers to the "light recipe" that combines lighting parameters such as color rendering index (Ra), flicker, light intensity, and color temperature as a "multispectral recipe." Clearly, Equation 3 is the multispectral recipe of the present invention, while Equations 4 and 5 are merely examples of the present invention applying Equation 3 to excitement and happiness, and are not intended to limit the conditions of Equation 3. In other words, by controlling "multispectral recipes" such as color rendering index (Ra), flicker, light intensity, and color temperature, the present invention can combine different emotions and varying degrees of emotion within the emotional coordinates shown in Figure 4a. Therefore, when the lighting system constructed in the present invention is commercially operated, an experienced operator (e.g., a professionally trained technician) can use a workstation or an app on a portable smart device at the lighting field end 620 to adjust the characteristics of the lighting parameters in the surrounding system score (LSS) formula (i.e., Equation 3) based on the user's emotional and physiological conditions. This allows the operator to edit a "multispectral recipe" for lighting execution, thereby controlling the lighting system to execute a specific "multispectral recipe" lighting program at the lighting field end 620 to meet the user's needs.

[0117] In general, emotion transformation can be achieved by applying a "multispectral recipe" tailored to the desired emotion. For example, to achieve a happy mood, a color temperature of 4000K can be directly applied, as shown in Table 7 or Figure 4a. Furthermore, if the user is currently experiencing a state of nervousness and the goal is to shift their mood to happiness, intuitively, continuous exposure to 4000K light for a period of time will achieve a state of happiness. However, this is inaccurate. According to the present invention, under the conditions of Equation 3, the same emotion can be expressed in varying degrees. These varying degrees of expression can be achieved by adjusting the lighting parameters of the "multispectral recipe," such as color rendering index (Ra), flicker, light intensity, and color temperature.

[0118] Next, the present invention provides an effective method for emotional transformation through the following process. In one embodiment of the present invention, the goal is to help users transition from a tense to a happy mood. This process is conducted using electroencephalograms (EEG) measured using an electroencephalogram (EEG). The coordinate dimensions in Figures 4b through 4e are normalized.

[0119] As shown in FIG4b, it is a relative intensity index diagram of a specific brain wave graph of the present invention. The relative intensity index of the brain wave graph is determined using the definition of NeuroSky, wherein the definitions of the brain wave waveform graph and relative intensity index of the emotions related to this embodiment include: tension, relaxation and happiness, as shown in FIG4b. The brain wave waveform graph shown in FIG4b is the intensity value (High) of the Beta wave, the weakness value (Low) of the Beta wave, the intensity value (High) of the Alpha wave and the weakness value (Low) of the Alpha wave under a specific condition. It should be noted that in the following experiment, after different light stimulations are given, the values ​​of the brain wave graph of the same emotion may be different, but the trends of the strength values ​​of the Beta wave and the Alpha wave are similar. The calculation method of the relative intensity index of different emotions is also different. The relative intensity index of the emotions of tension, relaxation and happiness according to NeuroSky is defined as follows:

[0120] Relative Strength Index = (BetaHigh + BetaLow) / (AlphaLow + AlphaHigh)

[0121] Relative Strength Index of Relaxation = (AlphaLow + AlphaHigh) / (BetaHigh + BetaLow)

[0122] Happiness Relative Strength Index = (AlphaLow + AlphaHigh + BetaHigh) / (BetaLow)

[0123] Next, please refer to Figure 4c, which shows the EEG graph of Emotional Shift Test 1 of the present invention. The first emotion shift test involves directly applying a 4000K color temperature of happiness. The process first confirms that the user is already experiencing stress. For example, by requiring them to complete a math problem within two minutes, which might cause them to become stressed. Once the user's EEG waveform, as shown on the left side of Figure 4c, is obtained, and it is determined that the user is already experiencing stress, the user is then exposed to a 4000K color temperature for two minutes. The resulting EEG graph is shown on the right side of Figure 4c. The relative intensity index of happiness, 3.34, is then calculated.

[0124] Next, please refer to Figure 4d, which is the brain wave graph of the emotion transfer test 2 of the present invention. The second emotion transfer test is carried out, and the user is asked to go from nervousness to relaxation, and then to happiness. The process is to first confirm that the user is already in a nervous mood. For example, the user is required to fully answer some math problems within 2 minutes, which may make them nervous. After the user's brain wave graph is obtained and the waveform graph shown in the left side of Figure 4d is displayed, it is judged that the user is already in a nervous mood. Then, the user is first irradiated with a relaxing spectrum (10Hz orange light) for 5 minutes. After that, the user is irradiated with a color temperature of 4000K for 2 minutes, and the user's brain wave graph is obtained as shown in the middle and right side of Figure 4d. Then, the relative intensity index 4 of happiness shown by the brain waves is calculated.

[0125] Next, please refer to Figure 4e, which shows the EEG graph of Emotion Shift Test 3 of the present invention. The third emotion shift test involves the user transitioning from nervousness to neutrality and then to happiness. The process first confirms that the user is already in a state of nervousness. For example, they are asked to complete a math problem within two minutes, which may cause them to fall into a state of nervousness. Once the user's EEG waveform is obtained, as shown on the left side of Figure 4e, and it is determined that the user is already in a state of nervousness, they are then exposed to a relaxing spectrum (10Hz orange light) for two minutes to reduce their nervousness and guide them into a neutral state. For example, the relaxed EEG graph in the middle of Figure 4e shows a relative intensity index of 1.15, which is lower than the relative intensity index in the second test. Therefore, the user's emotional state is determined to have entered a neutral state. Finally, after 2 minutes of exposure to a color temperature of 4000K, the EEG graph obtained is shown on the right side of Figure 4e. The relative intensity index of happiness, 10, is then calculated.

[0126] Based on the above experimental process, we can conclude that after determining that the user is in a state of tension, first administering alternative light spectrums that can alleviate tension, such as those in Table 7 or Figure 4a that produce complementary emotional spectra, followed by a second phase of exposure to the happy spectrum, can result in a better indicator of happiness. In particular, after further confirming that the user's mood has shifted to a neutral state after the first phase of exposure, the second phase of exposure to the happy spectrum can significantly improve the happy index. Theoretically, the neutral index refers to the return of the BOLD response in the limbic system to zero. Neutral emotion is located near the center or origin of the emotional coordinate graph in Figure 4a. Its theoretical characteristic is that it can help the average person maintain a balanced and stable mental state, unaffected by excessive positive or negative emotions, thereby enabling a more objective perspective on things and problems. In particular, after the above-mentioned experimental process, if the user can first be exposed to the complementary emotional spectrum in the first stage and it is determined that the user's mood has returned to neutral, and then the final desired emotional spectrum is exposed, the optimal emotional transfer effect can be adjusted. This is to first confirm the current emotion, then set the first stage of exposure spectrum according to the current emotion, and after determining that the user's emotion has reached neutral after the first stage of exposure, then the second stage of final target emotional spectrum exposure is carried out. This path or process of planning to reach the target emotion is called Motion Navigation.

[0127] In addition, the present invention also found that the change of emotions is not linear. For example, when the physiological conditions of different users are different or when the social status of different users is different, different users will have different degrees of response to the same emotion. For example: in terms of physiological conditions, users with epilepsy cannot be stimulated by stroboscopic light and must be supplemented with color temperature or intensity. For example: in terms of different social status, the experience of happiness is different, which will cause different degrees of response. Therefore, a more rigorous approach is to consider the user's "current state" before providing lighting stimulation for the target emotion. This is because for users in different emotional states (degrees), different "multi-spectral formula" stimulation can be given by adjusting the lighting parameters in the surrounding system score (LSS) formula to achieve a similar final effect.

[0128] According to the above emotion transfer test results, the present invention provides three different emotion transfer paths to execute the emotion navigation process of the present invention.

[0129] ■Emotional Navigation Example 1:

[0130] For example, the user is currently in a state of nervousness, and the goal is to shift the emotion to happiness.

[0131] The emotional navigation path during the transition process can be designed based on the user's or instructor's experience. For example, to first alleviate the user's current state of tension, a "multi-spectral formula" for tranquility (3000K color temperature according to Table 5) could be selected for illumination. Next, a "multi-spectral formula" for happiness (4000K color temperature according to Table 5) could be applied. If the user reaches a state of desire, the illumination process can be discontinued. Please refer to Figure 4e for the navigation path of Emotional Navigation Example 1.

[0132] ■Emotional Navigation Example 2:

[0133] Emotional Guidance Example 2 is a preferred embodiment of the present invention, specifically incorporating neutral indicators into the emotional guidance process. For example, the user is currently in a stressed state, and the goal is to shift their emotions to excitement. The emotional shift process can be divided into two steps:

[0134] The first step is to eliminate the stressed state first, so as to pull the user's emotions back to neutral (Neutral) or the origin of Figure 4a. Therefore, according to the emotional coordinate diagram of Figure 4a, the relaxed (Relaxed) emotion item relative to the stressed emotion can be selected to complement it. So we must first give the "multi-spectral formula" that can achieve relaxation for lighting (give 10Hz orange light according to Table 5), and monitor the user's physiological signals (such as brain area reactions) at the same time to determine whether the user's emotions have returned to neutral or near the origin. If it has returned to neutral or near the origin, it means that the stress has been eliminated. Alternatively, it can be determined by interviewing the user or conducting a questionnaire to determine whether the user's emotions have returned to neutral or near the origin. Afterwards,

[0135] Step 2: Applying an arousing "multispectral formula": After confirming that the user's mood has returned to neutral or normal, we apply an arousing "multispectral formula" for illumination (using a color temperature of 3000K according to Table 5). Simultaneously, we monitor the user's physiological signals (e.g., brain responses) to determine if the user's mood has reached a state of arousal. If so, we cease the illumination process. Please refer to Figure 4f for the navigation path of Emotional Navigation Example 2.

[0136] ■Emotional Navigation Example 3:

[0137] The present invention further discloses another preferred embodiment. For example, the user is currently in a state of sadness, and the goal is to transfer emotions to erotic. The transfer process includes two steps:

[0138] The first step is to eliminate sadness in order to bring the user's emotions back to neutral or the origin. Therefore, according to the emotional coordinate diagram in Figure 4a, we can choose the happiness emotion item that complements sadness. Therefore, we first apply a "multi-spectral formula" that can achieve happiness (using a color temperature of 4000K according to Table 5) while monitoring the user's physiological signals (such as brain region reactions) to determine whether the user's emotions have returned to neutral or near the origin. If they have returned to neutral or near the origin, it means that sadness has been eliminated. Afterwards,

[0139] Step 2: Apply a "Multi-spectral Formula" for lust: After the user's emotions return to their starting point, apply the "Multi-spectral Formula" for lust again (using a strobe frequency of 4-7Hz and a color temperature of 3500K according to Table 5). Simultaneously, monitor the user's physiological signals (e.g., brain responses) to determine if their emotions have reached lustful levels. If so, discontinue the lighting process. Please refer to Figure 4g for the navigation path of Emotional Navigation Example 3.

[0140] The above examples illustrate the emotional navigation disclosed in this invention, intended to briefly illustrate the concept of emotional navigation implementation. In practice, depending on the user's physiological condition or life experience, multiple emotional transitions may be required to navigate to the target emotion. In this case, the ultimate goal of emotional navigation can be achieved by adjusting color rendering index (Ra), strobe frequency, light intensity, and color temperature. Therefore, this invention does not impose a limit on the number of emotional transitions required to reach the target emotion.

[0141] Next, the present invention will further disclose preferred embodiments that can be specifically operated.

[0142] Based on the above, it is obvious that when the present invention performs emotion navigation, it is necessary to first know the user's emotional state at the time of emotion conversion (i.e., current emotional state), followed by the emotional changes during the emotion navigation process, and whether the target emotion is finally reached. The emotional state in these stages must be recorded using a physiological monitoring device as a basis for correcting the emotion navigation path. Among them, the physiological monitoring device described in the present invention can be an electroencephalogram (EEG) or a wearable electronic device (such as a mobile phone or wearable device, etc.) or can be stimulated using an emotion picture library, such as the International Affection Picture System (IAPS).

[0143] Next, please refer to Figure 5, which illustrates a method for performing emotional navigation according to the present invention. First, as shown in step 3510, the user's "initial emotion" is determined. For example, the user is positioned within the illumination field 620, and after measuring brainwaves via the wearable electroencephalogram 130, the recorded electroencephalogram data indicates the user's current "initial emotion"—for example, if the "initial emotion" is nervous. The "initial emotion" has already been uploaded and stored in the memory module 617 of the cloud 610 via the electroencephalogram 130 (as shown in Figure 3). Next, step 3520 is performed.

[0144] Step 3520 is to set the "target emotion." If the user wishes to adjust or convert their "initial emotion" to happiness, they can set the "target emotion" to happiness via the client device 630 in the intelligent human-centered lighting system 600. The data is then uploaded and stored in the memory module 617 of the cloud 610 via the client device 630. Next, proceed to step 3530.

[0145] Step 3530: Select "Relay Emotion" to complete the emotional navigation path. Based on the user's "initial emotion" and "target emotion," one or more "relay emotions" different from the "initial emotion" or "target emotion" are selected through the client device 630 in the management and control module 611 of the cloud 610. This creates a sequence from the "initial emotion" to the "relay emotion," and then to the "target emotion." This sequence is called the "emotion navigation path." For example, if the user's "initial emotion" is nervous and the "target emotion" is happiness, the client device 630 can select "Serene" as the "relay emotion" in the management and control module 611 of the cloud 610, so that the "emotion navigation path" goes from nervousness to serenity, then to happiness. Alternatively, the "relay emotion" can be selected first through calmness, then to serene, so that the "emotion navigation path" goes from nervousness to calmness, then to serenity, and finally to happiness. The "relay emotion" in the above-mentioned setting of the emotion navigation path can be selected by the user based on his or her own experience, or by professionals based on the user's "initial emotion" and "target emotion." Next, step 3540 is performed.

[0146] Step 3540: Editing a multispectral recipe based on the emotional navigation path. After the emotional navigation path is confirmed in step 3530, the multispectral recipe corresponding to the relay emotion and the target emotion needs to be found. For example, if the emotional coordinate information in Figure 4a has been stored in the memory module 617 of the cloud 610, the user can use the client device 630 in the intelligent human-centered lighting system 600 to search the cloud environment module 615 for a multispectral recipe corresponding to the relay emotion and the target emotion. These recipes include a multispectral recipe for tranquility (3000K color temperature), a multispectral recipe for calmness (4Hz strobe frequency, 3000K color temperature with an illuminance of less than 7 lux), and a multispectral recipe for happiness (4000K color temperature). The client device 630 can be a smartphone, a personal digital assistant (PDA), a notebook computer (NB), or a personal computer (PC) in a workstation. Then, step 3550 is performed. It is particularly important to note that in the above-mentioned process of editing the "multispectral recipe", the "multispectral recipe" for executing the lighting is edited by adjusting the characteristics of the lighting parameters in the surrounding system score (LSS) formula one by one.

[0147] Step 3550: Execute the lighting program based on the "multi-spectral recipe." When executing the lighting program, the intelligent human-factor lighting system 600 of the present invention can be controlled via two control paths. One control path involves the client device 630 controlling the lighting fixtures 621 in the lighting field 620 to sequentially illuminate the user based on the "multi-spectral recipe" corresponding to the "relay emotion" and the "target emotion." The other control path involves the management control module 611 in the cloud 610, via the intelligent Internet of Things (AIoT), controlling the lighting fixtures 621 in the lighting field 620 to sequentially illuminate the user based on the edited "relay emotion" and the "target emotion" corresponding to the "multi-spectral recipe." For example, both control methods involve illuminating the user's "relay emotion" for 10 minutes, followed by illuminating the user's "target emotion" for 15 minutes.

[0148] Step 3560: If it is determined that the user's emotion has reached the "target emotion", go to step 3570; if it is determined that the user's emotion has not reached the "target emotion", go to step 3580;

[0149] Step 3570: Stop the illumination process. If it is determined that the user's emotion has reached the "target emotion," the illumination process is stopped. The determination in step 3560 is based on information displayed by a physiological monitoring device attached to the user. The physiological monitoring device may be an electroencephalogram (EEG) monitor or a wearable device capable of detecting sympathetic and parasympathetic nerve signals.

[0150] Step 3580: If the user's emotion is determined to have not reached the "target emotion," adjustments can be made by reviewing the information displayed by the physiological monitoring device attached to the user. Based on the "emotion navigation path," the user can determine which segment (i.e., the "relay emotion" or "target emotion" segment) has not reached the set emotion. Then, the client device 630 can be used to communicate with the management control module 611 in the cloud 610 to adjust the emotion in the segment that has not reached the set emotion. Afterwards, a new "emotion navigation path" can be set. For example, if the originally set "emotion navigation path" is from calm to serene, then the physiological monitoring device can be reviewed in the calm to serene illumination interval. If either calm or serene has not reached the set emotion, adjustments can be made to those emotions. Adjustment can be achieved by selecting a new "relay emotion" through the management and control module 611 if the target emotion has not yet been reached. For example, if the result of the review indicates that calmness has not reached the target emotion, then "Relax" is selected as the new "relay emotion." Steps 3540 to 3560 are then repeated, with the process returning to step 3530. This creates a new "emotional navigation path" from tension to relaxation, then to serenity, and finally to happiness. Finally, when it is determined that the user's emotion has reached the "target emotion," the lighting process is terminated at step 3570. In a preferred embodiment, selecting a new "relay emotion" can be accomplished by adjusting the lighting parameters of the "multispectral recipe" (color rendering index (Ra), flicker, light intensity, or color temperature) using Equation 3, either on the client device 630 or in the cloud 610 management and control module 611. For example, increasing the color rendering index (Ra) can enhance the calm emotion.

[0151] Furthermore, if the physiological monitoring device has determined in step 3580 which segment has not reached the set emotion, the client device 630 can proceed to step 3590 to retrieve the user's personal physiological data stored in the cloud 610, such as the user's health check data. For example, if the health check data indicates that the user suffers from hypertension, diabetes, or epilepsy, the parameters of the "multispectral recipe" in Equation 3 (color rendering index (Ra), flicker, light intensity, or color temperature, etc.) can be adjusted accordingly. For example, if the user suffers from epilepsy, flicker can stimulate epilepsy and potentially induce it, so in Equation 3, color rendering index (Ra), color temperature, or intensity must be enhanced. Steps 3530 to 3560 are then repeated to identify a new "relay emotion" that can be adjusted to the "target emotion" based on the user's physiological signal data, forming a new "emotional navigation path" until the user's emotion is determined to have reached the "target emotion." The lighting process is then terminated in step 3570.

[0152] It is important to emphasize that when the present invention uses physiological data from health examinations to adjust the lighting parameters of the "multispectral recipe" (color rendering index - Ra, flicker, light intensity, or color temperature) in Equation 3, the limitations of the lighting parameters (color rendering index - Ra, flicker, light intensity, or color temperature) for each disease can be adjusted based on medical information. For example, too low a color temperature can affect the user's visual clarity. Therefore, when a user requires stimulation in a work environment, lowering the color temperature is not the best approach. Instead, other parameters should be strengthened, such as providing a higher color rendering index (Ra) to compensate for the need to use too low a color temperature to maintain a certain level of stimulation. Therefore, the use of flicker as a limitation for epilepsy patients in the above-mentioned embodiment of the present invention is merely an example and is not intended to limit the present invention's use of the "multispectral recipe" parameter adjustments (color rendering index - Ra, flicker, light intensity, or color temperature) in Equation 3 to epilepsy.

[0153] Furthermore, because the client device 630 and cloud 610 in the intelligent human-centered lighting system 600 of the present invention communicate using the Artificial Intelligence of Things (AIoT), the "emotional navigation paths" used by numerous users are stored in the cloud environment module 615, forming a massive amount of data. For example, after processing this massive amount of data stored in the private cloud 6151 or the public cloud 6153 using artificial intelligence algorithms, a ranking of the most commonly used "emotional navigation paths" during various emotion transitions can be obtained. Therefore, when a user wishes to perform a specific emotion transition, they can use the client device 630 to access the private cloud 6151 or the public cloud 6153 to find the most commonly used "emotional navigation paths" within the data for that specific emotion transition, providing a shortcut for step 3530: completing the setting of the emotion navigation path. Clearly, because the "emotional navigation paths" of the present invention operate via a network platform and also provide public cloud services, the intelligent human-centered lighting system 600 of the present invention is well-suited to providing higher-quality "emotional navigation path" services to various professionals through a cloud platform.

[0154] Please refer to FIG6 , which shows another method for performing emotion navigation according to the present invention.

[0155] First, execute step 4510: Confirm the "initial emotion." The detailed process is the same as step 3510, so please refer to step 3510 and will not be repeated here. The "initial emotion" is a state of stress. Next, proceed to step 4520.

[0156] Step 4520: Set the "target emotion" again. The detailed process is the same as step 3520, so please refer to step 3520 and will not be repeated here. The "target emotion" is set to excitement. Then, proceed to step 4530.

[0157] Step 4530: Select "Relay Emotion" to complete the "Emotion Navigation Path" setting. In this embodiment, step 4530 is based on the processes of Emotion Navigation Examples 2 and 3 to set the "Emotion Navigation Path." For example, using Emotion Navigation Example 2 as an example, after the user has confirmed in step 4510 that their "initial emotion" is a stressful state and, in step 4520, has confirmed that they want to shift their emotion to an excited "target emotion," they then select and set the "Emotion Navigation Path" in the management control module 611 of the cloud 610 via the client device 630. The first step is to eliminate stress in order to return the user's emotions to neutrality or the starting point. Therefore, in this embodiment, based on the emotion coordinate diagram shown in Figure 4a, the Relaxed emotion corresponding to stress can be selected as the "Relay Emotion" of this embodiment. At this point, the "emotion navigation path" completed in this step transitions from stress (the "initial emotion") to relaxation (the "intermediate emotion") and then to excitement (the "target emotion"). Of course, the "intermediate emotion" in this embodiment can also be a combination of more than one different emotion. For example, the "intermediate emotion" can first select the corresponding calm emotion, then further select the relaxation emotion. Therefore, in this embodiment, the "emotion navigation path" is set to transition from stress (the "initial emotion") to calmness and relaxation (the "intermediate emotion") and then to excitement (the "target emotion"). Therefore, the present invention does not limit the number of "intermediate emotion" combinations. Next, proceed to step 4540.

[0158] Step 4540: Edit the "multispectral recipe" based on the emotional navigation path. After the "emotional navigation path" has been confirmed in step 4530, it is necessary to find the multispectral recipe corresponding to the "relay emotion" and the "target emotion." For example, if the emotional coordinate information in Figure 4a has been stored in the cloud environment module 615, the user can use the client device 630 to search the cloud environment module 615 for the "multispectral recipe" corresponding to the "relay emotion" and the "target emotion." These recipes include: a "multispectral recipe" that provides relaxation (orange light with a flash frequency of 10Hz) and a "multispectral recipe" that provides excitement (a color temperature of 3000K). The client device 630 can be a smartphone, a personal digital assistant (PDA), a notebook computer (NB), or a personal computer (PC) in a workstation. Then, proceed to step 4550. It is particularly important to note that in the process of editing the "multi-spectral recipe" in step 4540, the "multi-spectral recipe" for executing the lighting is edited by adjusting the characteristics of the lighting parameters in the surrounding system score (LSS) formula one by one.

[0159] Step 4550: Execute the first stage of illumination. Based on the "emotional navigation path" set in step 4530, the lighting fixture group 621 in the illumination field end 620 is controlled via the client device 630 or the management control module 611 in the cloud 610, and then via the intelligent Internet of Things (AIoT). In this embodiment, the first stage of illumination involves only executing the edited relaxing emotion "multispectral recipe" (orange light with a 10Hz flash frequency). For example, after illuminating the user with the edited relaxing emotion "multispectral recipe" for 10 minutes, the illumination process is terminated, completing the first stage of the multispectral recipe illumination process. If the "relay emotion" is a combination of more than one different emotion, all of these different emotions must be illuminated before the illumination process is terminated. Then, proceed to step 4560.

[0160] Step 4560: Determine whether the user's stress has been eliminated. This is done by the physiological monitoring device determining whether the user's current mood has adjusted to the neutral or near-origin level of the emotion coordinate information in FIG. 4c after the first stage of illumination. If the user's mood after the first stage of illumination has not reached the neutral or near-origin level of the emotion coordinate information in FIG. 4c , indicating that the stress has not been completely eliminated, step 4570 is executed. If it is confirmed that the user's mood has reached the neutral or near-origin level of the emotion coordinate information in FIG. 4c , step 4580 is executed.

[0161] Step 4570: If the user's emotion is determined not to have reached "neutral or origin," further review of the user's personal physiological data (e.g., health check data) can be performed to adjust the "relayed emotion." In a preferred embodiment, the client device 630 retrieves the user's personal physiological data, such as health check data, stored in the memory module 617 from the cloud 610. The lighting parameters in the surrounding system score (LSS) formula for "relayed emotion" are then adjusted based on the user's personal physiological data. For example, if the health check data indicates that the user suffers from hypertension, diabetes, or epilepsy, the lighting parameters of the "multispectral recipe" in Equation 3 (color rendering index (Ra), flicker, light intensity, or color temperature) can be adjusted accordingly. The process then returns to step 4530, creating a new "emotional navigation path." Steps 4550 and 4560 are then repeated until the user's emotion is determined to have reached "neutral or origin," at which point step 4580 is performed.

[0162] Step 4580: Execute the second-stage multispectral recipe illumination process. Based on the multispectral recipe for the "emotional navigation path" in step 4540, the client device 630 or the management and control module 611 in the cloud 610, via the intelligent Internet of Things (AIoT), controls the lighting fixtures 621 in the lighting field 620 to execute the "multispectral recipe" for excitement (3000K color temperature). For example, after 15 minutes of irradiating the user with the "multispectral recipe" for excitement, the illumination process is terminated, completing the second-stage multispectral recipe illumination process. Then, proceed to step 4590.

[0163] Step 4590: If the user's emotion has reached the "target emotion," the process proceeds to step 4610. If the user's emotion has not reached the "target emotion," the process returns to step 4530, and the client device 630 proceeds to step 4570 to retrieve the user's personal physiological data, such as health check data, stored in the cloud 610. The lighting parameters in the "multispectral recipe" for the "target emotion" are then adjusted based on the user's personal physiological data. For example, if health check data indicates that the user suffers from hypertension, diabetes, or epilepsy, the lighting parameters in the "multispectral recipe" in Equation 3 (color rendering index (Ra), flicker, light intensity, or color temperature) can be adjusted based on these conditions and then stored in the cloud 610. Next, go directly to step 4580, and the user uses the client device 630 to find the lighting parameters of the "multi-spectral recipe" corresponding to the adjusted "target emotion" in the cloud 610. After that, repeat steps 4580 and 4590 until it is determined that the user's emotion has reached the "target emotion", then proceed to step 4610: stop the lighting program.

[0164] Please refer to FIG. 7 , which shows a third method for performing emotion navigation according to the present invention.

[0165] First, execute step 6510: Confirm the "initial emotion." The detailed process is the same as step 4510, so please refer to step 4510 and will not be repeated here. The "initial emotion" is a state of stress. Next, proceed to step 6520.

[0166] Step 6520: Set the "target emotion" again. The detailed process is the same as step 4520, so please refer to step 4520 and will not be repeated here. The "target emotion" is set to excitement. Then, proceed to step 6530.

[0167] Step 6530: Obtain the user's personal physiological data. The client device 630 accesses the cloud 610 to obtain the user's personal physiological data, such as the user's health checkup data, stored in the memory module 617. Next, the process proceeds to step 6540.

[0168] Step 6540: Select "Relay Emotion" to complete the "Emotion Navigation Path" setting. This embodiment uses Emotion Navigation Example 2 as an example. After the user confirms in step 6510 that their "Initial Emotion" is a stressful state and confirms in step 6520 that they wish to shift their emotion to an excited "Target Emotion," they then proceed to set the "Emotion Navigation Path" in the management and control module 611 of the cloud 610 via the client device 630. The first step is to eliminate stress in order to return the user's emotions to neutrality or their starting point. Therefore, in this embodiment, based on the emotion coordinate diagram in Figure 4a, the Relaxed emotion corresponding to stress is selected as the "Relay Emotion" of this embodiment. Furthermore, in this embodiment, the management and control module 611 of the cloud 610 also simultaneously reviews the user's personal physiological data and adjusts the parameters of the "Multispectral Recipe" for the "Relay Emotion" based on the user's personal physiological data. For example, if a user suffers from epilepsy, flickering can stimulate and potentially trigger epilepsy. Therefore, in Equation 3, the flicker ratio must be reduced or eliminated. This requires enhancements to the color rendering properties (Ra), color temperature, or intensity to generate a new "relay emotion." In this case, the "emotional navigation path" completed in this step transitions from stress (the "initial emotion") to relaxation (the "relay emotion"), which is adjusted, and then to excitement (the "target emotion." The parameters of the "multispectral recipe" for this adjusted relaxation emotion are adjusted based on the user's personal physiological data. Next, proceed to step 6550.

[0169] Step 6550: Edit a multispectral recipe based on the "emotion navigation path." After the "emotion navigation path" has been confirmed in step 6530, it is necessary to find the multispectral recipes corresponding to the "relay emotion" and the "target emotion." For example, if the emotion coordinate information in Figure 4a has been stored in the cloud environment module 615, the user can use the client device 630 to search the cloud environment module 615 for the "multispectral recipes" corresponding to the "relay emotion" and the "target emotion." These recipes include: a multispectral recipe that provides an adjusted relaxation emotion; and a multispectral recipe that provides excitement (color temperature of 3000K). The adjusted relaxation recipes adjust the parameters of the multispectral recipe, such as Ra, light intensity, or color temperature, in Equation 3. Alternatively, the client device 630 can be a smartphone, a personal digital assistant (PDA), a notebook computer (NB), or a personal computer (PC) in a workstation. Then, proceed to step 6560. It should be noted that in the process of editing the "multi-spectral recipe" in step 6550, the "multi-spectral recipe" for executing the lighting is edited by adjusting the characteristics of the lighting parameters in the surrounding system score (LSS) formula one by one.

[0170] Step 6560: Execute the first stage of lighting. After completing the editing of the multi-spectral recipe of the "emotional navigation path" in step 6550, the lighting fixture group 621 in the lighting field end 620 is controlled through the client device 630 or the management control module 611 in the cloud 610, and then through the intelligent Internet of Things (AIoT). In this embodiment, executing the first stage of lighting is to first execute the lighting program of the edited relaxing emotion "multi-spectral recipe". For example: after irradiating the user with the relaxing emotion "multi-spectral recipe" for 15 minutes, the lighting program is stopped first, and the first stage of the multi-spectral recipe lighting program is completed. If the "relay emotion" is a combination of more than one different emotions, it is also necessary to irradiate the user with these multiple different emotions before stopping the lighting program. Afterwards, proceed to step 6570.

[0171] Step 6570: Determine whether the user's stress has been eliminated. This is done by using the physiological monitoring device to determine whether the user's current mood has adjusted to neutral or the origin after the first stage of illumination. If the user's mood after the first stage of illumination has not reached neutral or the origin of the emotion coordinates in Figure 4c, indicating that the stress has not been completely eliminated, the process returns to step 6530. If it is confirmed that the user's mood has reached neutral or the origin of the emotion coordinates in Figure 4c, step 6580 is executed.

[0172] If step 6570 determines that the user's mood has not reached "neutral or origin," the process returns to step 6530, where the user's personal physiological data (e.g., physiological data from a health check) can be further reviewed to adjust the "relayed mood." In a preferred embodiment, client device 630 retrieves the user's physiological data stored in memory module 617 from cloud 610. The lighting parameters in the peripheral system score (LSS) formula for "relayed mood" are then adjusted based on the user's health check information. For example, if the health check physiological data indicates that the user has hypertension or diabetes, the lighting parameters of the "multispectral recipe" in Equation 3 (color rendering index (Ra), flicker, light intensity or color temperature, etc.) can be adjusted accordingly. Steps 6540 and 6570 are then repeated until it is determined that the user's mood has reached "neutral or origin," at which point the process proceeds to step 6580. Obviously, when adjusting the lighting parameters of the "multi-spectral recipe" corresponding to the "relay emotion" again, you can choose not to change the original "emotion navigation path" setting in step 6540, that is, only adjust the lighting parameters of the originally set "relay emotion".

[0173] Step 6580: Execute the second-stage multispectral recipe illumination process. Based on the multispectral recipe for the "emotional navigation path" in step 6550, the client device 630 or the management control module 611 in the cloud 610, via the intelligent Internet of Things (AIoT), controls the lighting fixtures 621 in the lighting field 620 to execute the "multispectral recipe" for excitement (3000K color temperature). For example, after 15 minutes of irradiating the user with the "multispectral recipe" for excitement, the illumination process is terminated, completing the second-stage multispectral recipe illumination process. Then, proceed to step 6590.

[0174] Step 6590: If the user's emotion has reached the "target emotion," the process proceeds to step 6610. If the user's emotion has not reached the "target emotion," the process returns to step 6530, and the client device 630 retrieves the user's health check physiological data stored in the cloud 610. The lighting parameters in the "multispectral recipe" for the "target emotion" are then adjusted based on the user's health check personal physiological data. For example, if the health check physiological data indicates that the user suffers from hypertension, diabetes, or epilepsy, the lighting parameters of the "multispectral recipe" in Equation 3 (color rendering index (Ra), flicker, light intensity, or color temperature)) can be adjusted accordingly to achieve the adjusted "target emotion," which is then stored in the cloud 610. Next, the process proceeds directly to step 6550, where the user uses client device 630 to retrieve the adjusted lighting parameters for the multispectral recipe corresponding to the target emotion from cloud 610. Steps 6590, 6530, and 6550 are then repeated, with the process then proceeding directly to step 6580 from step 6550 until the user's emotion reaches the target emotion. The process then proceeds to step 6610.

[0175] Step 6610: Stop the lighting process.

[0176] Finally, it's important to reiterate that when the present invention uses physiological data from health exams to adjust the parameters of the "multispectral recipe" (color rendering index - Ra, flicker, light intensity, or color temperature) in Equation 3, the limitations on these parameters for each disease can be adjusted based on medical information. For example, a too-low color temperature can affect a user's visual clarity. Therefore, when a user requires stimulating stimulation in a work environment, lowering the color temperature is not the optimal approach. Instead, other parameters should be strengthened, such as providing a higher color rendering index (Ra) to compensate for the necessitated use of the too-low color temperature to maintain a certain level of stimulating stimulation. Therefore, the use of flicker as a constraint for epilepsy patients in the above-mentioned embodiments is merely illustrative and is not intended to limit the present invention's use of the "multispectral recipe" parameters (color rendering index - Ra, flicker, light intensity, or color temperature) in Equation 3 to epilepsy.

[0177] Finally, it should be emphasized that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. Furthermore, the above description should be readily apparent to those skilled in the relevant art and should be readily applicable. Therefore, any equivalent changes or modifications that do not depart from the concepts disclosed herein are intended to be encompassed by the scope of the present invention.

Claims

1. A method for performing emotional navigation is performed by an intelligent human-factor lighting system, wherein the human-factor lighting system is composed of a cloud, a lighting field end, and a client device and is interconnected via the Internet, and the cloud stores emotional coordinate information, wherein: The emotion navigation method is characterized by: Step S1, confirming the user's "initial emotion", is to confirm the user's "initial emotion" at the illumination field end through a physiological monitoring device or by selecting to use IAPS stimulation, and store the "initial emotion" in the cloud; Step S2, setting the "target emotion", is to set it on the client device according to the needs of the user, and store the "target emotion" in the cloud; Step S3, selecting "relay emotion" to complete the emotion navigation path setting, is that the client device is connected to the cloud through the Internet, and the "relay emotion" is selected from the emotion coordinate information in the cloud, and the "relay emotion" is stored in the cloud; Step S4, editing the "multi-spectral recipe", is to edit the "multi-spectral recipe" according to the light recipe on the "relay emotion" and the "target emotion" selected by the emotion navigation path; Step S5, executing the lighting procedure of the "multi-spectral recipe", which is to control the lamp group in the lighting field end through the client device to perform a lighting process for a set time in sequence according to the "multi-spectral recipe" corresponding to the "relay emotion" and the "multi-spectral recipe" corresponding to the "target emotion"; Step S6, determining whether the "target emotion" has been reached, is to determine whether the user's emotion has reached the "target emotion" through a physiological monitoring device; Step S7, stopping the lighting process, is to stop the lighting process when it is determined that the user's emotion has reached the "target emotion".

2. The method of emotion navigation as claimed in claim 1, characterized in that The physiological monitoring device may be an electroencephalogram (EEG) or a wearable electronic device, including a smart phone or a wearable device or a wearable device capable of measuring sympathetic and parasympathetic nerve signals.

3. The method of emotion navigation as claimed in claim 1, characterized in that The "multi-spectrum formula" is composed of at least the following light parameters, including the color temperature, illumination, flicker frequency, and color rendering index (Ra) of the lighting.

4. The method of emotion navigation as claimed in claim 3, characterized in that The "multi-spectral formula" is a formula formed by the surrounding system score (LSS): LSS=tx[axf eeg (freq)+bxf eeg (Ra)+cxf eeg (CTI)+dxf eeg (I)], where a, b, c, d are the coefficients of variation, CTI is the color temperature, and t is the illumination time.

5. The method of emotion navigation as claimed in claim 1, characterized in that : If step S6 determines that the user's emotion has not reached the "target emotion", reselect another "relay emotion".

6. The method of emotion navigation as claimed in claim 7, characterized in that : After reselecting another "relay emotion", re-execute steps S3 to S6.

7. The method of emotion navigation as claimed in claim 1, characterized in that : If it is determined that the user's emotion has not reached the "target emotion", it further includes step S8, providing personal physiological data of the user.

8. The method of emotion navigation as claimed in claim 7, characterized in that :The user's personal physiological data is a physiological data of the user's health check.

9. A method for performing emotional navigation is performed by an intelligent human-factor lighting system, wherein the human-factor lighting system is composed of a cloud, a lighting field end, and a client device and is interconnected via the Internet, and the cloud stores emotional coordinate information, wherein: The emotion navigation method is characterized by: Step S1, confirming the user's "initial emotion", is to confirm the user's "initial emotion" at the illumination field end through a physiological monitoring device or by selecting to use IAPS stimulation, and The “initial emotion” is stored in the cloud; Step S2, setting the "target emotion", is to set it on the client device according to the needs of the user, and store the "target emotion" in the cloud; Step S3, selecting "relay emotion" to complete the emotion navigation path setting, is that the client device is connected to the cloud through the Internet, and the "relay emotion" is selected from the emotion coordinate information in the cloud, and the "relay emotion" is stored in the cloud; Step S4, editing the "multi-spectral formula", is to edit the "multi-spectral formula" corresponding to the "target emotion" and the "multi-spectral formula" corresponding to the "relay emotion" according to the light formula on the "relay emotion" and the "target emotion" selected by the emotion navigation path; Step S5, executing the "multi-spectrum recipe" lighting program corresponding to the "relay emotion", is to control the lamp group in the lighting field end through the client device to perform a lighting process for a set time on the user according to the "multi-spectrum recipe" corresponding to the "relay emotion"; Step S6, determining whether the user has reached a "neutral emotion", wherein the physiological monitoring device is used to determine whether the user's emotion has reached the "neutral emotion"; Step S7, executing the lighting program of the "multi-spectral recipe" corresponding to the "target emotion", is to control the lamp group in the lighting field end to perform a lighting process of a set time on the user according to the "multi-spectral recipe" corresponding to the "target emotion" through the client device after determining that the user has reached the "neutral emotion"; Step S8, determining whether the "target emotion" has been reached, is to determine whether the user's emotion has reached the "target emotion" through a physiological monitoring device; Step S9, stopping the lighting process, is to stop the lighting process when it is determined that the user's emotion has reached the "target emotion".

10. The method of emotion navigation as claimed in claim 9, characterized in that :The "relay emotion" in step S3 is an emotion selected to complement the "initial emotion" of the user.

11. The method of emotion navigation as claimed in claim 9, characterized in that :When judging whether the user's emotion has reached the "neutral emotion", it is to judge whether the user's emotion is neutral or near the origin.

12. The method of emotion navigation as claimed in claim 9, characterized in that If it is determined that the user's emotion has not reached the "neutral emotion", the method further includes step S10, providing personal physiological data of the user.

13. The method of emotion navigation as claimed in claim 12, characterized in that :The user's personal physiological data is a physiological data of the user's health check.

14. The method of emotion navigation as claimed in claim 12, characterized in that If it is determined that the user's emotion has not reached the "neutral emotion", the "multi-spectral formula" corresponding to the "relay emotion" is adjusted according to the user's personal physiological data, and steps S5 to S6 are re-executed.

15. The method of emotion navigation as claimed in claim 9, characterized in that The physiological monitoring device may be an electroencephalogram (EEG) or a wearable electronic device, including a smart phone or a wearable device or a wearable device capable of measuring sympathetic and parasympathetic nerve signals.

16. The method of emotion navigation as claimed in claim 9, characterized in that The "multi-spectrum formula" is composed of at least the following light parameters, including the color temperature, illumination, flicker frequency, and color rendering index (Ra) of the lighting.

17. The method of emotion navigation as claimed in claim 16, characterized in that The "multi-spectral formula" is a formula formed by the surrounding system score (LSS): LSS=tx[axf eeg (freq)+bxf eeg (Ra)+cxf eeg (CTI)+dxf eeg (I)], where a, b, c, d are the coefficients of variation, CTI is the color temperature, and t is the illumination time.

18. A method for performing emotional navigation is performed by an intelligent human-factor lighting system, wherein the human-factor lighting system is composed of a cloud, a lighting field end, and a client device and is interconnected via the Internet, and the cloud stores emotional coordinate information and personal physiological data of the user, wherein: The emotion navigation method is characterized by: Step S1, confirming the user's "initial emotion", is to confirm the user's "initial emotion" at the illumination field end through a physiological monitoring device or by selecting to use IAPS stimulation, and store the "initial emotion" in the cloud; Step S2, setting the "target emotion", is to set it on the client device according to the needs of the user, and store the "target emotion" in the cloud; Step S3, obtaining the user's personal physiological data, is to obtain the user's personal physiological data stored in the memory module from the client device to the cloud; Step S4, selecting "relay emotion" to complete the emotion navigation path setting, is when the client device is connected to the cloud through the Internet, and the "relay emotion" is selected from the emotion coordinate information in the cloud, and the "relay emotion" is stored in the cloud; Step S5, editing the "multi-spectral formula", is to edit the "multi-spectral formula" corresponding to the "target emotion" and the "multi-spectral formula" corresponding to the "relay emotion" according to the light formula on the "relay emotion" selected by the emotion navigation path and the information on the user's personal physiological data; Step S6, executing the "multi-spectrum recipe" lighting program of "relaying emotion", is to control the lamp group in the lighting field end through the client device to perform a lighting process for a set time on the user according to the "multi-spectrum recipe" corresponding to the "relaying emotion"; Step S7, determining whether the user has reached a "neutral emotion", wherein the physiological monitoring device is used to determine whether the user's emotion has reached the "neutral emotion"; Step S8, executing the lighting program of the "multi-spectral recipe" of the "target emotion", is to control the lamp group in the lighting field end through the client device to perform a lighting process for a set time on the user according to the "multi-spectral recipe" corresponding to the "target emotion" after determining that the user has reached the "neutral emotion"; Step S9, determining whether the "target emotion" has been reached, is to determine whether the user's emotion has reached the "target emotion" through a physiological monitoring device; Step S10, stopping the lighting process, is to stop the lighting process when it is determined that the user's emotion has reached the "target emotion".

19. The method of emotion navigation as claimed in claim 18, characterized in that :The "relay emotion" in step S3 is an emotion selected to complement the "initial emotion" of the user.

20. The method of emotion navigation as claimed in claim 18, characterized in that :When judging whether the user's emotion has reached the "neutral emotion", it is to judge whether the user's emotion is neutral or near the origin.

21. The method of emotion navigation as claimed in claim 18, characterized in that :The user's personal physiological data is a physiological data of the user's health check.

22. The method of emotion navigation as claimed in claim 18, characterized in that If it is determined that the user's emotion has not reached the "neutral emotion", the "multi-spectral formula" corresponding to the "relay emotion" is adjusted according to the user's personal physiological data, and steps S6 to S7 are re-executed.

23. The method of emotion navigation as claimed in claim 18, characterized in that The physiological monitoring device may be an electroencephalogram (EEG) or a wearable electronic device, including a smart phone or a wearable device or a wearable device capable of measuring sympathetic and parasympathetic nerve signals.

24. The method of emotion navigation as claimed in claim 18, characterized in that The "multi-spectrum formula" is composed of at least the following light parameters, including the color temperature, illumination, flicker frequency, and color rendering index (Ra) of the lighting.

25. The method of emotion navigation as claimed in claim 24, characterized in that The "multi-spectral formula" is a formula formed by the surrounding system score (LSS): LSS=tx[axf eeg (freq)+bxf eeg (Ra)+cxf eeg (CTI)+dxf eeg (I)], where a, b, c, d are the coefficients of variation, CTI is the color temperature, and t is the illumination time.

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