Patent Publication Number: US-2021178603-A1

Title: Emotional intelligent robotic pilot

Description:
FIELD OF THE DISCLOSURE 
     This disclosure is generally related to the field of robotic and automatic pilot systems and, in particular, to an emotional intelligent robotic pilot. 
     BACKGROUND 
     Human interactions are an integral part of any team performance. The dynamics in a team with an imbalance of power may reduce the overall efficiency and performance of the team. A balanced crew resource management (CRM) policy in a vehicle or control room environment seeks to have each user&#39;s (e.g., pilot&#39;s) contributions equally weighted and valued. Successful CRM policies may result in a significant reduction of incidents. Effective CRM is dependent on crew coordination and human interactions, of which emotional awareness may be a critical component. 
     In some cases, a user may find themselves in a reduced crew environment. A reduced crew environment is one in which a user is placed in a piloting or control environment that typically includes more pilots or controllers than are present. Robotic assistance may be used to compensate for having fewer users. For example, in some cases, an aircraft may typically have two pilots, but may become a reduced crew environment when flown by a single pilot along with a robotic piloting system. 
     Reduced crew environments may increase the possibility of unbalanced CRM. For example, existing robotic pilots may have relevant information and equipment control but may lack the ability to communicate effectively with people. In such systems, robotic pilots may perform knob turning, button pushing, and column maneuvering aspects of flying. However, these systems may lack the critical component of having an equal partner and providing balanced CRM because they are unable to connect with a human at an emotional level. 
     SUMMARY 
     Described is an emotionally intelligent robotic pilot that measures physiological signals associated with a user and generates an appropriate natural language statement corresponding to an emotion of the user. In an example, a system for providing user support in a robotic-assisted user environment includes at least one processor and memory storing instructions that, when executed by the at least one processor, cause the at least one processor to receive a set of measurements of a set of physiological signals from a set of sensors applied to a user. The processor further generates at least one emotional parameter value corresponding to the set of measurements based on an emotional analyzer model. The processor also generates a natural language statement corresponding to the at least one emotional parameter value based on a natural language processing model. 
     In some examples the set of sensors includes one or more microphones, one or more biometric sensors, one or more cameras, or any combination thereof. In some examples, the set of physiological signals includes a galvanic skin response, a blood volume measurement, a heart rate variability, an electroencephalogram, an eye dilation measurement, a thermal response, a muscle response, or any combination thereof. In some examples, the instructions further cause the at least one processor to convert the set of measurements into a format that is compatible with the emotional analyzer model. 
     In some examples, the instructions further cause the at least one processor to receive user calibration data associated with the user, where the at least one emotional parameter value is generated based at least in part on the user calibration data, and where the natural language statement is generated based at least in part on the user calibration data. In some examples, the user calibration data includes a value representing a level of emotional response associated with a user, a user communication preference value, or both. In some examples, the system includes a user corpus stored in a database, the user corpus including the user calibration data associated with the user along with additional user calibration data associated with additional users, where the user calibration data is retrieved from the user corpus. 
     In some examples, the instructions further cause the at least one processor to receive environmental data, wherein the at least one emotional parameter value is generated based at least in part on the environmental data. In some examples, the environmental data indicates a temperature, a pressure, weather conditions, visual conditions, or any combination thereof. In some examples, the instructions further cause the at least one processor to receive vehicle activity data, where the emotional parameter value is generated based at least in part on the vehicle activity data, and where the natural language statement is generated based at least in part on the vehicle activity data. In some examples, the vehicle activity data indicates a state of an aircraft, a phase of flight associated with the aircraft, activated warnings associated with the aircraft, air traffic control communications, aircraft imaging or radar inputs or any combination thereof. 
     In some examples, the emotional parameter value includes a valence parameter, an arousal parameter, a dominance parameter, an emotion parameter, a strength parameter, or any combination thereof. In some examples, the system includes an affect training database storing training data that maps sample physiological signals to emotional parameter values, where the emotional analyzer model is a machine learning model trained using the training data. In some examples, the instructions further cause the at least one processor to output the natural language statement to the user, receive a user response to the natural language statement, and initiate an update to an affect training database, a user corpus database, or both based on the user response. In some examples, the system is implemented within a robotic-assisted transport vehicle or a vehicle control station. 
     In an example, a method for providing user support in a robotic-assisted user environment includes receiving a set of measurements of a set of physiological signals from a set of sensors applied to a user. The method further includes generating at least one emotional parameter value corresponding to the set of measurements based on an emotional analyzer model, based on user calibration data retrieved from a user corpus stored at a database, and based on environmental data, where the emotional analyzer model is trained based on an affect training database. The method also includes generating a natural language statement corresponding to the at least one emotional parameter value based on a natural language processing model. The method includes outputting the natural language statement to the user. The method further includes receiving a user response to the natural language statement. The method also includes updating the affect training database, the user corpus, or both based on the user response. 
     In some examples, the method includes receiving training data that maps sample physiological signals to emotional parameter values from the affect training database and training the emotional analyzer model using the training data. In some examples, outputting the natural language statement to the user includes generating a visible signal, and audio signal, or a combination thereof. 
     In an example, a system for providing pilot or controller support in a reduced crew transport environment includes a set of sensors configured to collect a set of measurements of a set of physiological signals of a pilot. The system further includes an emotional analyzer model usable to generate at least one emotional parameter value corresponding to the set of measurements. The system also includes a natural language processing model usable to generate a natural language statement corresponding to the at least one emotional parameter value for output to the pilot. 
     In some examples, the system includes one or more aircraft systems to provide environmental data and aircraft activity data for use in generating the at least one emotional parameter value, an affect training database to provide training data for training the emotional analyzer model, and a user corpus stored in a database, the user corpus including the user calibration data associated with the pilot to provide pilot-specific data for use in generating the at least one emotional parameter value and for use in generating the natural language statement. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram depicting an example of a system for providing user support in a robotic-assisted user environment. 
         FIG. 2  is a block diagram depicting an example of a robotic-assisted transport vehicle or control. 
         FIG. 3  is a diagram depicting example training data for use with training an emotional analyzer model. 
         FIG. 4  is a conceptual diagram depicting an example of a process for providing user support in a robotic-assisted user environment. 
         FIG. 5  is a flow diagram depicting an example of a method for providing user support in a robotic-assisted user environment. 
         FIG. 6  is a flow diagram depicting an example of a method for training an emotional analyzer model and for providing user support in a robotic-assisted user environment. 
         FIG. 7  is a flow diagram depicting an example of a method for providing user support in a robotic-assisted user environment. 
     
    
    
     While the disclosure is susceptible to various modifications and alternative forms, specific examples have been shown by way of example in the drawings and will be described in detail herein. However, it should be understood that the disclosure is not intended to be limited to the particular forms disclosed. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the scope of the disclosure. 
     DETAILED DESCRIPTION 
     A robotic assisted piloted aircraft, another type of vehicle, or a control room may be more productive when a robotic counterpart has the capability, not only turn knobs and push buttons, but to respond to a user in an emotionally intelligent manner, both in actions and communications. The disclosed system and method may enable a robotic pilot to act like a human co-pilot responding with increased awareness and commentary, and with increased workload. 
     Referring to  FIG. 1 , a system  100  for providing user support in a robotic-assisted user environment is depicted. The system  100  may include at least one processor  102  and memory  104 . The processor  102  may include a central processing unit (CPU), a graphical processing unit (GPU), a digital signal processor (DSP), a peripheral interface controller (PIC), or another type of microprocessor. It may be implemented as an integrated circuit, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a combination of logic gate circuitry, other types of digital or analog electrical design components, or the like, or combinations thereof. In some examples, the processor  102  may be distributed across multiple processing elements, relying on distributive processing operations. 
     The memory  104  may include random-access memory (RAM), read-only memory (ROM), magnetic disk memory, optical disk memory, flash memory, another type of memory capable of storing data and processor instructions, or the like, or combinations thereof. In some examples, the memory  104 , or portions thereof, may be located externally or remotely from the rest of the system  100 . The memory  104  may store instructions  106  that, when executed by the processor  102 , cause the processor  102  to perform operations. The operations may correspond to any operations described herein as being performed by the system  100 . 
     The system  100  may include a set of sensors  110 , a biometric analyzer  130 , an emotional analyzer model  140 , a natural language processing model  150 , and an output device  160 . These devices may be implemented as part of the instructions  106  and their functions may be performed by the at least one processor  102 . Alternatively, they may be implemented as discrete circuit modules. In some cases, one or more modules may be combined. The system  100  may further include vehicle systems  170 , an affect training database  190 , and a database  180  including a user corpus  182 . These may be part of the system  100 , as depicted in  FIG. 1 . Alternatively, some or all of them may be distinct and/or remotely located from the system  100 .  FIG. 1  depicts a bus  108  connecting each of the systems, modules, and databases. In practice, multiple buses, networks, and/or the internet may be used to communicatively connect the modules depicted in  FIG. 1 . 
     The set of sensors  110  may be configured to measure a set of physiological signals  122  associated with the user  120 . The user  120  may be a pilot. The set of sensors  110  may include one or more microphones  112 , one or more biometric sensors  114 , one or more cameras  116 , another type of wearable or user specific sensor, or any combination thereof. The set of physiological signals  122  may include a galvanic skin response, a blood volume measurement, a heart rate variability, an electroencephalogram, an eye dilation measurement, a thermal response, a muscle response, another type of physiological measurement related to user stress or emotion, or any combination thereof. 
     The biometric analyzer  130  may be configured to convert a set of measurements  132  of the set of physiological signals  122  to compatible measurements  134  that are usable with the emotional analyzer model  140 . The conversion may include analog-to-digital conversions, file format conversions, digital preprocessing processes, data extraction processes, and/or other types of conversion processes. As a non-limiting illustrative example, the conversions may include processes such as analyzing video or photographic footage of the user&#39;s  120  face to generate a measurement corresponding to a facial state value. Another example may include converting raw heart rate data into a time varying frequency pattern. Many other examples are possible. 
     The emotional analyzer model  140  may be usable to generate at least one emotional parameter value  142  corresponding to the set of measurements  132 . The emotional analyzer model  140  may include an artificial intelligence and/or machine learning model usable to identify and/or quantify the at least one emotional parameter value  142 . For example, the emotional analyzer model  140  may use various algorithms to process the biometric signal data into one or more emotional parameters, some examples include Ant colony optimization, genetic algorithms, evolutionary algorithms, learning classifier systems, self-organizing maps, other types of machine learning classification techniques, or an ensemble model. It may be implemented as a neural network, decision trees, nonlinear regression, logistic regression, other types of machine learning classification models, or combinations thereof. 
     The emotional parameter  142  may include a valence parameter, an arousal parameter, a dominance parameter, an emotion parameter, a strength parameter, or any combination thereof. Together, these parameters may be usable to analyze emotions associated with the user  120 . For example, a positive valence with low emotional parameters may indicate that the user  120  is relaxed and may be more open to conversation with the system  100 , while a negative valence with high emotional parameters may indicate that the user  120  is in a tense situation and quick, succinct communication is preferable. 
     The natural language processing model  150  may be usable to generate a natural language statement  152  corresponding to the at least one emotional parameter value  142 . The natural language processing model  150  may likewise include content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realization. Any of these phases may use one or more algorithms for generating the content. Common algorithms for generating referring expressions include greedy algorithms, Incremental algorithms, Boolean expressions, Context-Sensitive extensions and Sets algorithms, among others, or combinations thereof. 
     The natural language statement  152  may complement the one or more emotional parameter  142 . For example, if the emotional parameter indicates a relaxed situation, then the natural language statement may include a relaxed and familiar tone (e.g., “I can help you with a few tasks if you′d like”). In contrast, if the emotional parameter indicates a tense situation, then the natural language statement may include a more direct tone to address the situation (e.g., “The engine warning is on, I&#39;m starting engine warning procedures”). 
     The output device  160  may include any device capable of communicating the natural language statement  152  to the user  120 . The natural language statement  152  may be communicated through a visual signal  162 , an audio signal  164 , or both. Examples of the output device  160  may include a visual display screen, a speaker system, another type of audio/visual device, or any combination thereof. 
     The vehicle systems  170  may include systems usable to operate a vehicle, such as an aircraft, and may be associated with vehicle activity data  172 . For example, the vehicle activity data  172  may indicate a state of the vehicle, such a phase of flight, activated warnings, air traffic control communications, aircraft imaging or radar inputs, other types of system states and/or data, or any combination thereof. The emotional parameter value  142  may be generated based at least in part on the vehicle activity data  172 . Likewise, the natural language statement  152  may be generated based at least in part on the vehicle activity data  172 . As an illustrative example, during a takeoff and/or landing phase, the emotional parameter value  142  may be more indicative of stress and the natural language statement  152  may be less conversational and more concise as compared to other phases of flight. Other examples exist. 
     The vehicle system  170  may further be configured to measure, or otherwise detect, environmental data  174 . The environmental data  174  may indicate a temperature, a pressure, weather conditions, visual conditions, other indications of environmental conditions, or any combination thereof. The at least one emotional parameter value  142  may be generated based at least in part on the environmental data  174 . 
     The user corpus  182  stored in the database  180  may include user collaboration data associated with a plurality of users that may use the system  100 . For example, the user corpus  182  may include user calibration data  184  associated with the user  120  along with additional user calibration data associated with additional users. The user calibration data  184  may include a value representing a level of emotional response associated with the user  120 , a user communication preference value, or both. The at least one emotional parameter value  142  may be generated based at least in part on the user calibration data  184 , and the natural language statement  152  may be generated based at least in part on the user calibration data  184 . To illustrate, the emotional parameter value  142  may be more indicative of a calm emotion when the user calibration data indicates that the user  120  is less prone to emotional responses. Other examples exist. 
     The affect training database  190  may store training data  192  that maps sample physiological signals to emotional parameter values. The emotional analyzer model  140  may be a machine learning model trained using the training data  192 . Training the emotional analyzer model  140  may be an ongoing process. For example, a user response  124  may be received from the user  120  in response to the natural language statement  152 . The affect training database  190  may be updated based on the user response  124  and the emotional analyzer model  140  may be further trained or otherwise updated. The user corpus  182  may also be updated based on the user response  124 . 
     During operation the processor  102  may detect an event or situation for which the system  100  may assist the user  120 . For example, a particular phase of flight may be detected, or a situation typically associated with stress, or some level of stress with respect to the user  120  may be detected. Physiological signals  122  associated with the user  120  may be measured and stored as the measurements  132 . In order to use the emotional analyzer model  140 , the measurements may be converted or otherwise altered for compatibility to generate the compatible measurements  134 . The emotional analyzer model  140  may classify and quantify various properties of the physiological signals  122  in order to determine one or more emotional parameters  142  associated with the user  120 . Based at least partially on the emotional parameters  142 , the system  100  may use the natural language processing model  150  to generate a natural language statement  152  that is appropriate based on the physiological signals  122  and output the natural language statement  152  to the user  120  via the output device  160 . The system  100  may also take appropriate action by controlling one or more of the vehicle systems  170 . 
     Further during operation, the system  100  may use the user calibration data  184  to tune the emotional analyzer model  140 . For example, a level of emotion may be different for different users. The user calibration data  184  can be used to apply different weights or importance to the measurements  132  based on which user is interacting with the system  100 . By weighting the measurements  132 , the emotional parameters  142  may be more accurate with respect to a specific user. Further, the user  120  may have a preference in language used to address the user  120 . That preference may be stored in the user calibration data  184  and used by the natural language processing model  150  to ensure the natural language statement  152  is appropriate for the user  120 . 
     The system  100  may also use the vehicle activity data  172  and the environmental data  174  in generating the emotional parameters  142  and the natural language statement  152 . To illustrate, in a situation where the emotional parameters  142  indicate high levels of stress in the user  120 , the natural language statement  152  may note that the user  120  seems agitated. However, if the vehicle activity data  172  indicates that a phase of flight is in takeoff or landing, then it is less likely that the stress is due to agitation because the takeoff and landing phases are inherently higher stress activities. 
     The user  120  may respond to the natural language statement  152 , or to actions taken by the system  100 . These user responses  124  may be used to add to the training data  192  for further training of the emotional analyzer model  140 . In this way, the system  100  may continuously improve its accuracy in sensing emotions associated with the user  120  and generating the appropriate natural language statement  152  as a response. 
     A benefit of the system  100  is that a good CRM balance may be achieved between the user  120  and a robotic co-pilot to ensure safety and reliability of flying. The system  100  may provide sufficient emotional sensing and feedback to enable a robotic pilot to fully contribute to the piloting task. Other benefits may exist. 
     Referring to  FIG. 2 , an example of a robotic-assisted transport vehicle or control system  200  is depicted. For example, the vehicle or control system  200  may include an aircraft, watercraft, spacecraft, an air traffic control system, or another type of piloted vehicle or control system. The vehicle or control system  200  may include a robotic-assisted user environment  202 . The robotic assistance may compensate for the vehicle or control system  200  having fewer crew members than a typical vehicle or control system. The system  100  may be incorporated into the robotic-assisted user environment  202 . By incorporating the system  100  into the robotic-assisted user environment  202 , a good CRM balance may be maintained within the robotic-assisted transport vehicle or control system  200  without performance loss in the case of a reduced crew. 
     Referring to  FIG. 3 , an example of training data  192  for use with the affect database  190  is depicted. As shown in  FIG. 3 , the training data  192  may map sample physiological signals  302 ,  303 ,  304 , to emotional parameter values  306 . The emotional parameter values  306  may combine to describe a particular affect. For example, the emotional parameter values  306  may include describe a valence  308 , an arousal  310 , a dominance  312 , an emotion  314 , and/or a strength  316 . In some cases, the dominance  312 , the valence  308 , and the arousal  310  may be used individually or to identify the emotion  314 . The training data may be used to train the emotional analyzer model  140 . By using a sufficient amount of training data  192  and machine learning techniques, the emotional analyzer model  140  may learn how the sample physiological signals  302 ,  303 ,  304 , apply to particular levels of each of the emotional parameters  306 . Thus, the emotional analyzer model  140  may develop the ability to detect particular nuances in various combinations of the sample physiological signals  302 ,  303 ,  304 . In some cases, training data, such as the training data  192 , may enable predictive models to be generated that are more complete and accurate than predictions made based on human observations. 
     Referring to  FIG. 4  a conceptual diagram of an example of a process  400  for providing user support in a robotic-assisted user environment is depicted. The process  400  may apply in the particular case of an aircraft. However, it should be noted that other applications exist. The process  400  may include measuring a set of physiological signals  422  associated with a user  420 . The physiological signals may correspond to the set of physiological signals  122  of  FIG. 1 . Then, a biometric analyzer and signal processing unit  425  may clean and analyze the physiological signals  422  and aggregate data. An emotional analyzer  440  may use machine learning algorithms and constructs (e.g., the emotional analyzer model  140  of  FIG. 1 ) to perform feature extraction and classification of emotion. A pilot calibration corpus  430  may be used to tailor emotional sensing and responses for individual pilots. 
     Environmental data  474 , such as a temperature or pressure, may be used to calibrate the biometric signals and influence the extraction and classification of the emotion from the emotional analyzer  440 . Aircraft activity indicator data  472  may include information such as a phase of flight and aircraft activity level, which can be used in determining the emotion at the emotional analyzer  440 . For example, a high heart rate may be expected during landing in icy conditions, and communication with the user  420  may be brief and limited to critical information. A natural language statement generator  450  may generate appropriate communications based on the output of the emotional analyzer  440 . The natural language statement generator  450  may also rely on the aircraft activity indicator data  472  in generating outputs to the user  420 . An affect training database  490  may store training data to continuously improve the machine learning model associated with the emotional analyzer  440 . 
     As a sample output of the process  400 , the natural language statement generator  450  may generate a first comment  402  such as “Are you okay today? You seem a bit off,” or a second comment  404 , such as “The engine warning is on, I&#39;m starting engine warning procedures.” Different comments may be generated based on the different situations and emotions detected by the emotional analyzer  440 . 
     Each of the components depicted in  FIG. 4  may include separate circuitry to perform their associated functions. Alternatively, each of the components depicted in  FIG. 4  may be performed by one or more computing devices, such as a processor. Further, any of the components of  FIG. 4  may be combined with any other of the components. 
     Referring to  FIG. 5 , a dataflow diagram depicting an example of a dataflow  500  for providing user support in a robotic-assisted user environment is depicted. The dataflow  500  may begin with a pilot  502 . As with  FIG. 4 , although  FIG. 5  is described with reference to an aircraft, other applications are possible. 
     Raw physiological signal data  550  may be measured from the pilot  502  and passed to a physiology analyzer and signal processing unit  504 . Processed, aggregated, and cleaned data  552  may pass from the physiology analyzer and signal processing unit  504  to an emotional analyzer  506 . The emotional analyzer  506  may receive temperature, pressure, weather, and/or visual imagery data  554  from environmental signal data  508 . The emotional analyzer  506  may include a machine learning model that was trained using affect computing training data  556  received from an affect training database  510 . The emotional analyzer  506  may receive pilot emotional calibration and prior response data  558  from a pilot corpus database  512 . The emotional analyzer  506  may also receive aircraft situational awareness data  560 , including but not limited to, phase of flight data, system failures data, aircraft warnings data, and aircraft traffic control communication data, from an aircraft activity indictor  516 . Based on the received data, the emotional analyzer  506  may generate predicted affect, valence, arousal, emotion, and strength data  562 , which may be passed to a natural language processor  518 . 
     The natural language processor  518  may include a statement gate  520  that can be used in determining an emotionally appropriate response. For example, the statement gate  520  may determine whether to make a statement or not to make a statement, and whether the statement is relatively brief or long. The determination may be made based on pilot communication preferences  564  received from the pilot corpus database  512 , based on the aircraft situational awareness data  560 , and based on the predicted affect, valence, arousal, emotion, and strength data  562 . When the statement gate  520  determines that a statement should be generated, a generate statement module  522  may be used to generate an affect influenced action statement  563 , which may be passed to a robotic co-pilot  590 . The statement  563  may indicate that a particular action is being taken by the robotic co-pilot  590 . 
     The robotic co-pilot  590  may send the statement  568  to the pilot  502  and may receive a response from the pilot  502 . For example, the pilot  502  may instruct to robotic co-pilot  590  to refrain from taking the suggested action. A pilot response  570  may also be used to update the affect training database  510  and the pilot corpus database  512 . 
     The dataflow  500  may enable a system, such as the system  100  of  FIG. 1 , to sense an emotion of the pilot  502  and generate an appropriate emotional response. The system may also be continuously updated to ensure that the robotic co-pilot takes actions and makes statements that are appropriate to the pilot  502 . 
     Referring to  FIG. 6 , an example of a method  600  for providing user support in a robotic-assisted user environment is depicted. The method  600  may include receiving training data that maps sample physiological signals to emotional parameter values from an affect training database, at  602 . For example, the processor  102 , or another processing device used for training, may receive the training data  192  that maps the sample physiological signals  302 - 304  to the emotional parameter values  306  from the affect training database  190 . 
     The method  600  may further include training an emotional analyzer model using the training data, at  604 . For example, the emotional analyzer model  140  may be trained using the training data  192 . 
     The method  600  may also include receiving a set of measurements of a set of physiological signals from a set of sensors applied to a user, at  606 . For example, the set of measurements  132  of the set of physiological signals  122  may be received by the processor  102  from the set of sensors  110 , which may be applied to the user  120 . 
     The method  600  may include generating at least one emotional parameter value corresponding to the set of measurements based on an emotional analyzer model, based on user calibration data retrieved from a user corpus stored at a database, and based on environmental data, where the emotional analyzer model is trained based on the affect training database, at  608 . For example, the at least one emotional parameter value  142  may be generated and may correspond to the set of measurements  132 . The emotional parameter value  142  may be generated based on the emotional analyzer model  140 , based on the user calibration data  184 , and based on the environmental data  174 . 
     The method  600  may further include generating a natural language statement corresponding to the at least one emotional parameter value based on a natural language processing model, at  610 . For example, the natural language statement  152  may be generated based on the natural language processing model  150 . 
     The method  600  may also include outputting the natural language statement to the user, at  612 . For example, the natural language statement  152  may be output to the user  120 . 
     The method  600  may include receiving a user response to the natural language statement, at  614 . For example, the user response  124  may be received by the processor  102 . 
     The method  600  may further include updating the affect training database, the user corpus, or both based on the user response, at  616 . For example, the affect training database  190 , the user corpus  182 , or both may be updated based on the user response  124 . 
     A benefit of the method  600  is that a robotic co-pilot can respond to a user in an emotionally intelligent manner, both in actions and communications. This may enable a user and the robotic co-pilot to work more effectively together. 
     Referring to  FIG. 7 , an example of a method  700  for providing user support in a robotic-assisted user environment is depicted and may correspond to portions (i.e.,  606 - 612 ) of the method  600  with some additional elements. For example, the method  700  may include receiving a set of measurements of a set of physiological signals from a set of sensors applied to a user, at  702 . For example, the set of measurements  132  of the set of physiological signals  122  may be received by the processor  102  from the set of sensors  110 , which may be applied to the user  120 . 
     The method  700  may further include converting the set of measurements into a format that is compatible with an emotional analyzer model, at  704 . For example, the measurements  132  may be converted into the compatible measurements  134 . 
     The method  700  may also include receiving user calibration data associated with a user, at  706 . For example, the user calibration data  184  may be received. 
     The method  700  may include receiving environmental data, at  708 . For example, the environmental data  174  may be received. 
     The method  700  may further include receiving vehicle activity data, at  710 . For example, the vehicle activity data  172  may be received. 
     The method  700  may also include generating at least one emotional parameter value corresponding to the set of measurements based on an emotional analyzer model, based on the user calibration data, based on the environmental data, and based on the vehicle activity data, where the emotional analyzer model is trained based on an affect training database, at  711 . For example, the at least one emotional parameter value  142  may be generated. 
     The method  700  may further include generating a natural language statement corresponding to the at least one emotional parameter value based on a natural language processing model, based on the user calibration data, and based on the vehicle activity data, at  712 . For example, the natural language statement  152  may be generated. 
     The method  700  may also include outputting the natural language statement to the user, at  714 . For example, the natural language statement  152  may be output to the user  120 . Outputting the natural language statement may include generating a visible signal, an audio signal, or a combination thereof, at  716 . For example, the visual signal  162 , the audio signal  164 , or both may be generated. 
     A benefit of the method  700  is that a robotic co-pilot can respond to a user in an emotionally intelligent manner, both in actions and communications, and the interaction may be fine tuned based on user calibration data, environmental data, and vehicle activity data. This may enable a user and the robotic co-pilot to work more effectively together. 
     Although various examples have been shown and described, the present disclosure is not so limited and will be understood to include all such modifications and variations as would be apparent to one skilled in the art.