WEARABLE DEVICE FOR REAL-TIME SUSPECT EMOTIONAL MONITORING AND FORENSIC PSYCHOLOGIST RESPONSE ENHANCEMENT

Abstract

Wearable Device for Real-Time Suspect Emotional Monitoring and Forensic Psychologist Response Enhancement This invention describes a wearable system designed for real-time physiological and Suspect emotional monitoring, particularly for use in clinical psychology and law enforcement. The system integrates multiple physiological sensors to monitor heart rate, skin conductance, and facial expressions, coupled with voice stress analysis to assess emotional states. A machine learning engine processes these data inputs, generating an emotional profile that is refined by context-aware algorithms considering session-specific variables. The system includes biometric authentication to ensure data security and accuracy, and predictive behavioral modeling to forecast potential emotional escalations. An augmented reality interface provides real-time visual feedback, displaying emotional data and alerts during sessions. The system also features encryption protocols and ethical compliance mechanisms to ensure secure and responsible data handling. This invention provides an advanced tool for professionals, allowing for precise, real-time emotional assessments in high-pressure environments, improving decision-making and intervention strategies.

Specification

Description:[0001] This invention relates to the field of Medical psychology, particularly a Wearable Device for Real-Time Emotional Monitoring, specifically designed to assist clinical psychologists and law enforcement officers during sessions with suspects who may have psychiatric disorders. The system integrates physiological data collection, voice stress analysis, machine learning algorithms, biometric authentication, context-aware emotional analysis, and predictive behavioral modeling. It can continuously assess emotional states based on real-time inputs and contextual factors, providing professionals with accurate, secure, and actionable emotional insights. The invention addresses the need for objective, continuous emotional monitoring and intervention, enhancing decision-making in environments where emotional stability is critical.

PRIOR ART AND PROBLEM TO BE SOLVED

[0002] In the field of mental health care, particularly in therapy and psychological assessment, a key challenge is the accurate monitoring of a patient's emotional state in real-time. Current methods for evaluating emotions, such as self-reporting or periodic clinical assessments, have significant limitations. Self-reporting, for example, relies heavily on the patient's ability and willingness to communicate their emotions accurately, which may not always reflect their true state. Moreover, emotions can fluctuate throughout the day, and periodic assessments during scheduled therapy sessions fail to capture these dynamic shifts. This gap in real-time emotional tracking often results in missed opportunities to address critical emotional changes that may require immediate intervention.

[0003] Another significant drawback is the latency in clinical psychologist response time. Due to limitations in current monitoring methods, clinicians are often unable to react to emotional crises as they occur. The delay between the onset of a critical emotional state and the psychologist's awareness and response could mean the difference between successful intervention and the exacerbation of a patient's condition, potentially leading to self-harm, heightened anxiety, or other severe consequences. In clinical practice, the ability to continuously and non-invasively monitor a patient's emotional state and respond in real-time could vastly improve therapeutic outcomes. However, creating such a device presents significant technological and ethical challenges, including the need for accurate emotion detection, non-invasive monitoring methods, and artificial intelligence (AI) integration to enhance psychologist intervention strategies without overwhelming the clinician with false alerts or irrelevant data.

[0004] The field of emotional monitoring and psychological intervention faces several critical drawbacks that limit the effectiveness of current technologies. One major issue is the limited accuracy of physiological metrics used by wearable devices. Devices that track parameters such as heart rate, skin conductivity, and body temperature often produce unreliable results when it comes to identifying emotional states. These physiological responses can be influenced by a variety of external factors such as physical activity, illness, or environmental conditions, which can lead to false assessments of a person's emotional condition. Consequently, wearable technology fails to consistently provide accurate insights into a patient's real-time emotions, making it less useful in clinical practice where precision is crucial.

[0005] Another significant drawback is the inconsistency of data generated through manual mood tracking via mobile applications. These apps rely heavily on patients self-reporting their emotional states, which introduces substantial variability and unreliability into the data. Patients may forget to log their emotions regularly, or they may misjudge and inaccurately report their feelings due to memory bias, emotional avoidance, or a lack of self-awareness. This inconsistency makes it difficult for therapists to gain a comprehensive and accurate view of their patients' emotional fluctuations, especially between clinical sessions.

[0006] Additionally, AI-based emotion recognition systems, while promising in theory, come with their own set of challenges. These systems, which analyse facial expressions, voice patterns, or textual inputs, raise significant privacy concerns. The constant surveillance required for these systems to function effectively can feel intrusive to patients, and there are ethical dilemmas surrounding data security and the potential misuse of sensitive emotional information. Moreover, AI systems often struggle to interpret nuanced emotional cues, particularly when individual or cultural differences in emotional expression are involved. As a result, these systems may misinterpret emotions, generating false positives or failing to recognize critical emotional states that require immediate attention.

[0007] One of the most pressing limitations across all current solutions is their inability to enable real-time clinical intervention. Many systems focus solely on collecting data for later analysis rather than facilitating immediate therapeutic responses when a patient's emotional state becomes critical. This reactive approach can leave psychologists unaware of urgent emotional changes, thereby missing the opportunity to intervene during moments of heightened distress. Without the ability to trigger timely alerts and interventions, these technologies fall short in addressing the real-time needs of mental health care.

[0008] There is a significant issue related to the overwhelming amount of data generated by these systems. While continuous emotional monitoring can be valuable, the influx of data and notifications can lead to alert fatigue for clinicians. Many devices generate frequent alerts, often triggered by minor physiological fluctuations that do not necessarily indicate emotional crises. This overload of information can make it difficult for psychologists to discern when a patient truly needs immediate attention, thereby reducing the overall effectiveness of these tools in clinical environments.

[0009] Several technologies have been proposed to address the challenges in real-time emotional monitoring, but each comes with significant limitations. One of the most common solutions is the use of wearable emotional monitoring devices, such as smartwatches and wristbands. These devices typically track physiological metrics like heart rate variability, skin conductance, and body temperature, which are then analysed to infer emotional states such as stress or anxiety. However, these physiological markers are often unreliable, as they can be affected by non-emotional factors like physical exertion or environmental changes. This makes it difficult for these devices to consistently and accurately differentiate between emotional shifts and ordinary physiological responses.

[0010] Another popular approach is the use of mobile applications for mood tracking, which allow patients to log their emotions throughout the day or rely on smartphone usage patterns, such as social media activity and communication behaviour, to infer emotional states. While these apps can provide useful insights, they rely heavily on self-reporting, which is prone to inconsistencies. Patients may forget to log their moods or inaccurately assess their emotional state, leading to gaps or biases in the data. Moreover, these apps often lack the depth of data needed to draw meaningful, real-time conclusions about a patient's emotional condition, especially in fast-changing emotional environments.

[0011] Advances in artificial intelligence (AI) have also led to the development of emotion recognition technologies that analyse facial expressions, speech patterns, or written text to gauge emotional states. These AI systems show promise in their ability to process large amounts of data and detect emotional cues. However, they face several challenges. The constant monitoring required to gather sufficient data for AI analysis can raise privacy concerns, as patients may feel uncomfortable with the continuous collection of personal information. Additionally, emotion detection through AI is not foolproof; it can misinterpret facial expressions or voice tones, especially in individuals who consciously mask their emotions or in cultural contexts where emotional expressions vary widely. These inaccuracies limit the effectiveness of AI-based emotion recognition in clinical settings.

[0012] Biofeedback therapy tools, which measure physiological functions like muscle tension and brain waves to provide real-time feedback to both the patient and therapist, have also been explored as a potential solution. While biofeedback has shown benefits in controlled therapeutic environments, its application is limited outside of these settings. The devices are often expensive and require specialized training to operate, making them less accessible for continuous, day-to-day emotional monitoring. Additionally, biofeedback data can be complex and challenging to interpret, reducing its utility for real-time intervention outside clinical or research environments.

[0013] To resolve the above mentioned problem the Suspect Emotional Monitoring System (SEMS) is a wearable device developed to enhance emotional assessments in clinical and law enforcement settings. It integrates advanced physiological sensors, voice stress analysis, and machine learning algorithms to provide a continuous and objective emotional profile of the user. The device monitors key physiological parameters such as heart rate, skin conductance, and facial expressions. It is equipped with context-aware algorithms that adjust the emotional analysis based on session conditions. The system offers biometric authentication to secure data integrity, while predictive behavioural modelling helps professionals anticipate emotional escalations. An augmented reality interface overlays emotional data on a video feed of the session, offering real-time insights. SEMS also integrates ethical compliance mechanisms and supports additional sensors to enhance emotional analysis.

THE OBJECTIVES OF THE INVENTION:

[0014] The current solutions for real-time emotional monitoring face numerous challenges that prevent them from being fully effective in clinical practice. One of the primary issues is the limited accuracy of physiological metrics in wearable devices. While wearables attempt to monitor emotional states by tracking heart rate, skin conductance, and other bodily indicators, these physiological markers are often unreliable as they can be influenced by factors unrelated to emotions, such as physical activity, environmental conditions, or health fluctuations. As a result, these devices frequently produce false emotional readings, making it difficult to draw precise conclusions about a patient's mental state.

[0015] Another significant problem lies in the inconsistency of data gathered from mobile applications that rely on manual mood tracking. These apps often depend on patients to self-report their emotional states, which introduces a high degree of variability and unreliability. Patients may forget to log their emotions, misjudge their feelings, or underreport negative emotions due to social desirability or cognitive bias. This inconsistency leads to incomplete or biased data, limiting the ability of clinicians to accurately monitor emotional changes over time. Furthermore, mobile apps often lack real-time analysis capabilities, meaning they cannot provide the immediate insights needed for timely interventions.

[0016] AI-based emotion recognition systems, though advanced, bring additional complications, particularly in terms of privacy and accuracy. These systems require continuous monitoring of facial expressions, speech patterns, or text inputs, which raises serious privacy concerns. Patients may feel uncomfortable or even violated by the constant surveillance required for these technologies to function effectively. Moreover, emotion recognition algorithms can misinterpret emotional cues, especially when individuals mask their emotions or when cultural differences in emotional expression are present. False positives or misclassifications can occur, leading to incorrect assessments and inappropriate interventions, which undermines the trust and efficacy of these systems.

[0017] One of the most critical limitations across all existing solutions is the lack of real-time intervention capability. Many devices and applications are designed to collect data for later analysis rather than facilitate immediate clinical responses. This reactive approach means that emotional crises or significant mood changes may go unnoticed until they are reported or reviewed later, resulting in missed opportunities for timely therapeutic intervention. Without the ability to trigger alerts when a patient's emotional state becomes critical, these systems fall short in preventing potential emotional breakdowns or crises.

[0018] There is a substantial issue related to the overwhelming amount of data generated by these emotional monitoring systems. Continuous tracking often results in an excessive number of notifications and alerts, many of which are based on minor or irrelevant changes in physiological or emotional data. This can lead to alert fatigue among clinicians, who may become desensitized to the alerts or struggle to discern which ones truly warrant immediate action. This overload of information dilutes the effectiveness of these systems, as clinicians may miss important emotional cues amidst a flood of less relevant data.

[0019] The principal objective of the invention is to provide a wearable apparatus capable of real-time physiological and emotional monitoring, specifically designed for clinical psychologists and law enforcement officers. The system integrates multi-sensor technology to monitor heart rate, skin conductance, and facial expressions, alongside voice stress analysis for enhanced emotional profiling. It further incorporates biometric authentication mechanisms, including fingerprint and retina scan functionalities, to ensure secure and accurate data collection. The system utilizes context-aware algorithms to dynamically adjust emotional interpretations based on session-specific conditions, and it features an augmented reality (AR) interface for intuitive real-time visualization of emotional data. The system is further equipped with predictive behavioral modeling, facilitating the forecasting of potential emotional escalations to enable pre-emptive interventions.

[0020] Another objective of the invention is to equip the system with sensors capable of measuring heart rate, skin conductance, and facial expressions in real-time, offering continuous monitoring of physiological states. These sensors are embedded in a hypoallergenic wearable device to ensure long-term, comfortable use without interfering with the suspect's or patient's activities.

[0021] The further objective of the invention is to integrate a voice stress analysis module into the system that monitors vocal patterns and detects emotional states or stress that may not be apparent through physiological monitoring alone. This feature enhances the emotional profile generated by the system, providing a more comprehensive and multi-faceted emotional analysis.

[0022] The further objective of the invention is to incorporate biometric authentication mechanisms, including fingerprint scanning and retina recognition, to secure the integrity of the collected data. This ensures that the emotional and physiological data is accurately attributed to the correct individual and is protected from unauthorized access or manipulation.

[0023] The further objective of the invention is to deploy context-aware algorithms that refine emotional interpretations by factoring in environmental variables such as session duration, time of day, and the subject matter being discussed. These algorithms dynamically adjust the system's emotional assessments to account for situational influences, thereby improving accuracy and relevance in real-time.

[0024] The further objective of the invention is to provide an augmented reality interface for the associated mobile or desktop application, which visually overlays the emotional data on a live video feed of the session. This interface is designed to present emotional insights intuitively, facilitating easier interpretation by clinical psychologists or law enforcement officers during live sessions.

[0025] The further objective of the invention is to incorporate predictive behavioral modeling algorithms that analyze historical and real-time emotional data, forecasting potential emotional escalations or behavioral shifts. This feature allows professionals to take pre-emptive measures to manage the emotional state of the subject, thereby improving the effectiveness of interventions and preventing crises.

[0026] The further objective of the invention is to implement ethical monitoring and secure data management protocols, including data anonymization and blockchain-based storage, ensuring compliance with legal and ethical standards. This objective ensures the system handles sensitive data responsibly, maintaining both privacy and integrity.

SUMMARY OF THE INVENTION

[0027] In the context of real-time emotional monitoring, one of the core challenges is the reliance on physiological data alone to infer emotional states. While wearables and other devices can track metrics such as heart rate variability, skin conductance, and body temperature, these signals often provide an incomplete picture of a person's emotional experience. Emotions are influenced not only by physiological responses but also by cognitive processes, memories, and psychological states, all of which cannot be easily captured by wearable technology. For example, a patient may exhibit calm physiological signs while experiencing significant mental distress due to anxious thoughts or traumatic memories. This disconnect between physical and emotional experiences means that relying solely on physiological data can result in misinterpretations, limiting the overall accuracy of real-time emotional monitoring.

[0028] Another significant issue arises from the cognitive biases present in self-reporting, which is often a primary method of tracking emotions in clinical settings. Patients are prone to various types of biases when recalling or reporting their emotional states, which undermines the reliability of the data. Memory bias, for instance, can lead to an inaccurate recollection of emotions, while social desirability bias may cause patients to underreport negative feelings or exaggerate positive ones during clinical sessions. These biases can distort the true emotional picture, making it difficult for therapists to understand the full scope of a patient's emotional fluctuations. This problem highlights the necessity for an objective, real-time monitoring system that can complement self-reporting, offering a more accurate and continuous assessment of a patient's emotional health. The dynamic nature of emotional states further complicates real-time monitoring efforts. Emotions can change rapidly in response to external stimuli or internal thoughts, and any system designed to monitor these shifts must be adaptive and capable of filtering out irrelevant data. For instance, temporary spikes in heart rate due to physical exertion or environmental factors should not trigger false emotional alerts. An effective system would need to incorporate machine learning algorithms that can learn from a patient's emotional patterns over time, differentiating between normal fluctuations and significant emotional events. Current monitoring technologies often lack this adaptive intelligence, limiting their ability to provide clinicians with meaningful and actionable insights in real-time.

[0029] A major challenge for clinicians is the overwhelming volume of data generated by continuous emotional monitoring systems. While the ability to track emotions in real-time can be beneficial, it also risks producing too many alerts and notifications, many of which may not be critical. Minor fluctuations in physiological data or misinterpretations of emotional cues can result in false alarms, which can lead to alert fatigue among clinicians. When overwhelmed by frequent notifications, clinicians may become desensitized to alerts, causing them to miss critical emotional shifts that require immediate intervention. This problem underscores the need for a monitoring system that can filter out irrelevant data and provide only the most pertinent information to clinicians, ensuring that they are alerted only when necessary. Current emotional monitoring systems' lack of timely intervention capabilities significantly limits their effectiveness. Most technologies are designed to collect and analyse data for later review, rather than facilitating immediate action when emotional crises occur. This reactive approach often means that patients in distress may not receive timely support, leaving them vulnerable to escalating emotional issues such as anxiety attacks or depressive episodes. A more proactive system would monitor emotions in real-time and alert clinicians when immediate intervention is required, allowing them to respond swiftly and potentially prevent emotional crises. The absence of this functionality in current solutions represents a critical gap in real-time emotional health management.

[0030] So, in this invention, the Suspect Emotional Monitoring System (SEMS) is a highly advanced wearable system designed for real-time emotional monitoring in clinical psychology and law enforcement. It captures physiological data, including heart rate, skin conductance, and facial expressions, using multi-sensor technology embedded in a compact, hypoallergenic wristband. SEMS is further enhanced with voice stress analysis, context-aware algorithms, and biometric authentication for data security and accuracy. The system integrates predictive behavioural modelling that forecasts emotional escalations based on historical and real-time data. Its augmented reality interface allows emotional data to be visualized in real time, aiding professionals in effective decision-making. SEMS ensures continuous data transmission to associated apps or computers with seamless Bluetooth and Wi-Fi connectivity. Ethical monitoring compliance mechanisms ensure all data collected adhere to legal and ethical standards. This system is designed for continuous wear, offering professionals a comprehensive tool for enhanced emotional monitoring.

DETAILED DESCRIPTION OF THE INVENTION

[0031] While the present invention is described herein by example, using various embodiments and illustrative drawings, those skilled in the art will recognise that invention is neither intended to be limited to the embodiment of drawing or drawings described nor designed to represent the scale of the various components. Further, some features that may form a part of the invention may not be illustrated with specific figures for ease of illustration. Such omissions do not limit the embodiment outlined in any way. The drawings and detailed description are not intended to restrict the invention to the form disclosed. Still, on the contrary, the invention covers all modification/s, equivalents, and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the description's size or the claims. As used throughout this specification, the worn "may" be used in a permissive sense (That is, meaning having the potential) rather than the mandatory sense (That is, meaning, must).

[0032] Further, the words "an" or "a" mean "at least one" and the word "plurality" means one or more unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents and any additional subject matter not recited, and is not supposed to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents acts, materials, devices, articles and the like are included in the specification solely to provide a context for the present invention.

[0033] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is also understood that it contemplates the same component or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group comprising", "including", or "is" preceding the recitation of the element or group of elements and vice versa.

[0034] Before explaining at least one embodiment of the invention in detail, it is to be understood that the present invention is not limited in its application to the details outlined in the following description or exemplified by the examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for description and should not be regarded as limiting.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention belongs. Besides, the descriptions, materials, methods, and examples are illustrative only and not intended to be limiting. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention.

[0036] The present invention is a Wearable System for Real-Time Physiological and Suspect Emotion Monitoring that serves a critical purpose in clinical and law enforcement settings by providing professionals with an advanced tool to assess the emotional state of individuals, such as suspects or patients, in real-time. Traditional methods of emotional assessment often rely on subjective observations, which may lead to inaccurate conclusions, especially when dealing with individuals exhibiting psychiatric disorders or under high stress. This system offers an objective, data-driven approach, allowing professionals to make more informed decisions during critical interactions. It is designed to continuously monitor and analyse emotional states with minimal interference in the subject's activities, ensuring a seamless and non-intrusive experience.

[0037] The system is specifically developed to assist in high-stakes environments where understanding an individual's emotional condition is paramount to effective interventions. By providing real-time emotional insights, the system enhances the ability of clinical psychologists and law enforcement officers to respond proactively to emotional escalations. The integration of predictive behavioural modelling further supports this goal, enabling professionals to anticipate potential emotional crises and take appropriate pre-emptive measures. As such, this system is invaluable in scenarios where timely and accurate emotional assessments can prevent undesirable outcomes, such as emotional breakdowns or non-cooperation during interrogations.

[0038] One of the defining features of this wearable system is its capacity to analyse multiple emotional indicators simultaneously, ensuring a comprehensive emotional profile. This profile is developed through the monitoring of physiological and emotional responses, alongside voice stress analysis to detect emotions that might not be evident through other means. The system's context-aware algorithms add another layer of accuracy, refining emotional assessments based on the specific circumstances of each session. By factoring in variables such as the time of day and topics discussed, these algorithms ensure that the emotional data is both relevant and contextualized.

[0039] In addition, the system emphasizes security and accuracy through its biometric authentication feature, which guarantees that the emotional data is precisely linked to the correct individual. This ensures the integrity of the monitoring process, safeguarding the collected data from unauthorized access or misattribution. Professionals using the system can trust that the emotional insights they receive are both accurate and secure, enhancing the reliability of their assessments.

[0040] The system also provides a user-friendly interface via an augmented reality (AR) platform. This feature allows real-time emotional data to be overlaid on a live video feed of the session, offering professionals an intuitive visual representation of the emotional state of the individual being monitored. The AR interface enhances the immediacy of the emotional insights, allowing professionals to interpret the data quickly and efficiently during active interactions.

[0041] This wearable system represents a significant advancement in emotional monitoring technology, offering a robust solution for professionals tasked with understanding and managing the emotional states of individuals in sensitive and high-stress environments. Its combination of real-time monitoring, predictive capabilities, context-aware analysis, and secure data management makes it an essential tool for clinical and law enforcement applications, where precise and timely emotional assessments are critical for effective intervention and decision-making.

[0042] The primary wearable component is a wristband, meticulously engineered to balance discretion and professionalism. Its form factor is sleek, compact, and minimalistic, avoiding any superfluous design elements that might draw undue attention or interfere with the user's natural movements. The housing of the wristband is water-resistant, designed to resist accidental splashes or exposure to humid environments, further emphasizing the system's reliability under varied operational conditions. The flexible nature of the band ensures that it comfortably wraps around the wrist, conforming to different wrist sizes without causing any discomfort or irritation.

[0043] At the forefront of the wristband lies a high-resolution touchscreen display, modest in size yet clear enough to provide real-time feedback on the system's operation. This screen, crafted with tempered glass for enhanced durability, is seamlessly integrated into the band's design, maintaining the smooth contour of the device without protruding elements. The display itself is interactive, allowing the wearer to access system information or adjust certain settings, though its primary function is to provide visual feedback to the professional users monitoring the data. The interface is kept deliberately simple, focusing on legibility and ease of use, with clear, crisp icons and text visible under varying lighting conditions, ensuring usability in both bright and dim environments.

[0044] The fastening mechanism of the wristband is designed for both security and ease of use. Utilizing a combination of magnetic closures and adjustable straps, the system ensures a snug fit while allowing for quick and easy removal when necessary. The magnetic locking system is discreetly incorporated into the band, ensuring no visible seams or awkward protrusions, contributing to the overall minimalist aesthetic of the device. This fastening method is reinforced to prevent accidental detachment during high-movement activities, further supporting its use in law enforcement scenarios where physical interaction may be frequent.

[0045] From a tactile perspective, the wristband offers a soft-touch feel, ensuring that the user experiences no friction or discomfort even during prolonged use. The band's ergonomic design takes into consideration the contours of the human wrist, with slight curvature adjustments built into the structure to ensure that it sits flush against the skin without creating pressure points or gaps. Despite its compact and discreet form, the wristband exudes a sense of robustness, signalling to the user that it is not merely a consumer-grade wearable, but a high-precision instrument designed for professional use.

[0046] To the external observer, the Wearable System is deliberately designed to be inconspicuous, not immediately recognizable as a piece of emotional monitoring technology. This subtlety is crucial for its intended use in environments where overt monitoring may influence behavior or create discomfort. The external appearance emphasizes professionalism, with an understated elegance that belies the advanced technology housed within. Its form factor, combining comfort, durability, and aesthetic discretion, makes it well-suited for both clinical sessions with patients and high-pressure law enforcement interactions, ensuring that it fits seamlessly into the professional attire of its users without standing out as an intrusive technological element.

[0047] The Wearable System for Real-Time Physiological and Suspect Emotional Monitoring is an intricate assembly of advanced components, each meticulously integrated to perform specific functions, collectively contributing to the system's overall capability. At its core, the system relies on a series of physiological sensors embedded within the wristband. These sensors are designed to measure critical physiological indicators, such as heart rate, skin conductance, and facial micro-expressions, which serve as the foundation for the emotional analysis. The heart rate sensor, utilizing photoplethysmography, captures pulse data through light reflection and absorption by blood vessels beneath the skin. This data is constantly transmitted to the processing unit, where it is analysed in real-time to detect variations associated with emotional states such as stress, anxiety, or calmness.

[0048] Simultaneously, the skin conductance sensor measures the electrical conductance of the skin, a widely recognized marker of emotional arousal. Changes in sweat gland activity, even at minute levels, are detected by this sensor, providing insights into the subject's emotional intensity. The integration of these two sensors enables a multi-dimensional analysis of emotional responses, wherein heart rate data correlates with skin conductance to enhance the accuracy of emotional profiling. These sensors continuously relay data to the central processing unit, which functions as the nerve center of the system, integrating inputs from all components.

[0049] In addition to physiological data collection, the system integrates a voice stress analysis module, responsible for evaluating vocal patterns to detect stress and emotional states that might not be observable through physiological data alone. This module operates by capturing the subject's voice during interactions, analysing minute modulations in tone, pitch, and frequency that are indicative of emotional states. The voice stress analysis module is directly connected to the system's machine learning engine, which processes this vocal data alongside physiological inputs. The machine learning algorithms employed here have been trained to recognize complex emotional patterns by evaluating correlations between physiological and vocal data, refining the overall emotional profile produced by the system.

[0050] To ensure data security and accuracy, biometric authentication is integrated into the wearable device. The fingerprint and retina scan modules serve as the primary authentication mechanisms, ensuring that the data collected is securely tied to the correct individual. These biometric modules are interconnected with the system's encrypted data storage, where sensitive emotional data is stored and protected from unauthorized access. This authentication mechanism not only guarantees the security of the data but also ensures that the emotional profile generated is accurately associated with the subject under monitoring.

[0051] A key component that enhances the system's analytical capacity is the context-aware algorithm, which continuously refines emotional interpretations based on session-specific factors. These algorithms integrate data from both the physiological sensors and the voice stress module, while also factoring in contextual elements such as the time of day, session duration, and external stimuli. The context-aware system adjusts the weight of physiological and vocal data depending on these factors, improving the precision of the emotional analysis. This module interacts closely with the machine learning engine, providing it with contextual information to fine-tune its predictive modelling, thus ensuring that the emotional insights provided are as relevant and accurate as possible.

[0052] The system's predictive behavioural modelling is another crucial component, leveraging historical and real-time data to forecast potential emotional escalations. This component is tightly integrated with both the machine learning engine and the context-aware algorithms. It operates by analysing patterns over time, identifying trends in the subject's emotional state that may indicate impending stress or emotional outbursts. The predictive behavioural model works in tandem with the augmented reality interface, enabling professionals to receive visual alerts when emotional escalations are predicted, thereby facilitating timely interventions.

[0053] The augmented reality interface serves as a visual bridge between the collected data and the user, overlaying emotional insights on a live video feed of the session. This interface interacts directly with the data processing and predictive modelling components, ensuring that the most up-to-date emotional data is presented in real-time. The AR interface is designed to be intuitive, displaying emotional trends and predictive alerts in a manner that is easily interpretable by psychologists or law enforcement officers. It enhances the system's utility by providing immediate visual feedback, allowing professionals to make real-time decisions based on the emotional state of the subject.

[0054] These components collectively enable the Wearable System to function as an advanced emotional monitoring tool. Each element-whether it be the physiological sensors, voice stress module, biometric authentication, context-aware algorithms, or predictive behavioural modelling-plays a distinct and crucial role in fulfilling the system's objective of providing real-time emotional insights. Their seamless interaction ensures that emotional data is not only accurate but also contextually relevant and secure, enabling professionals to monitor and assess emotional states with unparalleled precision.

[0055] The Wearable System for Real-Time Physiological and Suspect Emotional Monitoring operates as a fully integrated platform designed to continuously monitor, analyse, and interpret the emotional state of a subject in real-time. The system's working mechanism is initiated through the simultaneous collection of physiological and vocal data. The embedded physiological sensors, including heart rate and skin conductance sensors, continuously gather biometric data from the subject. This data is transmitted to the system's central processing unit, where it is analysed in real time. The heart rate data provides critical insights into cardiovascular activity, correlating with stress levels, while the skin conductance sensor detects changes in sweat gland activity, offering an additional measure of emotional arousal.

[0056] In parallel, the system's voice stress analysis module captures and analyses the subject's vocal patterns during interactions, using sophisticated algorithms to detect subtle changes in pitch, tone, and frequency. These vocal indicators, which may not be perceptible through traditional observation, are analysed to infer stress or anxiety levels. The voice data, together with the physiological data, is processed through a machine learning engine that has been trained to recognize complex emotional patterns based on the correlations between these inputs. This real-time emotional analysis is continuously updated as new data is collected, providing a dynamic emotional profile of the subject.

[0057] The biometric authentication feature plays a critical role in securing the data collected. Upon the initial use of the system, the subject undergoes fingerprint or retina scanning, ensuring that all emotional and physiological data is accurately attributed to the correct individual. This process safeguards the integrity of the system by preventing unauthorized access and ensuring that the collected data is securely linked to the appropriate subject.

[0058] Once the system begins to process the incoming data, context-aware algorithms are employed to refine the emotional analysis. These algorithms consider session-specific variables such as the time of day, the duration of the session, and the topics being discussed. By factoring in these contextual elements, the system adjusts the interpretation of emotional data to provide a more nuanced and accurate understanding of the subject's emotional state. The integration of these algorithms ensures that the emotional profile is not merely a reflection of raw physiological and vocal data but a contextually relevant interpretation of the subject's emotional condition.

[0059] The predictive behavioural modelling component works in tandem with the machine learning engine to forecast potential emotional escalations. By analysing both historical and real-time emotional data, the system identifies patterns that suggest the likelihood of an emotional outburst or escalation in stress levels. When such patterns are detected, the system triggers an alert, allowing the professional to take pre-emptive measures to de-escalate the situation. This predictive feature is particularly valuable in high-pressure environments, such as law enforcement interrogations or clinical sessions with patients experiencing psychological distress, where timely intervention can prevent crises.

[0060] The system's augmented reality interface further enhances its functionality by providing a real-time visual overlay of emotional data on a video feed of the session. As the physiological and emotional data is processed, the AR interface displays emotional trends, stress levels, and predictive alerts directly within the professional's field of vision. This allows the psychologist or law enforcement officer to interpret the data intuitively and respond immediately to changes in the subject's emotional state. The AR interface is designed to be user-friendly, ensuring that professionals can focus on their interactions with the subject while still receiving critical emotional insights.

[0061] Throughout the entire process, data security and ethical compliance are ensured through a combination of encryption protocols and ethical monitoring mechanisms. The system anonymizes sensitive data where necessary and ensures that all data handling adheres to legal and ethical standards, providing professionals with confidence in the security and integrity of the system.

[0062] In conclusion, the Wearable System for Real-Time Physiological and Suspect Emotional Monitoring operates through a seamless integration of physiological data collection, voice stress analysis, machine learning, biometric authentication, context-aware algorithms, and predictive behavioural modelling. The system's real-time processing capabilities allow it to continuously monitor and assess the emotional state of a subject, while its augmented reality interface and predictive features provide professionals with actionable insights that enhance decision-making and intervention strategies. This highly advanced system represents a significant advancement in emotional monitoring technology, particularly in the context of clinical and law enforcement applications.

[0063] Case Study Example: In a high-stakes interrogation scenario, a law enforcement officer was tasked with interviewing a suspect involved in a sensitive criminal investigation. The suspect had a known history of psychiatric disorders, which made traditional interrogation methods challenging. Recognizing the need for a more precise and real-time understanding of the suspect's emotional state, the officer employed the Wearable System for Real-Time Physiological and Emotional Monitoring during the interrogation session. The suspect wore the device, which continuously collected data on heart rate, skin conductance, and vocal stress, allowing the officer to monitor the suspect's emotional state in real time without interrupting the natural flow of the interview.

[0064] As the session progressed, the system detected heightened physiological arousal through elevated heart rate and increased skin conductance. Concurrently, the voice stress analysis module indicated subtle vocal changes that suggested the suspect was becoming increasingly anxious, despite maintaining a calm demeanour. The system's context-aware algorithms analysed the subject's emotional response within the context of the interrogation's sensitive subject matter, further refining the emotional profile.

[0065] Midway through the session, the system's predictive behavioural modelling feature flagged a potential emotional escalation, based on patterns identified from both historical data (gathered from prior interactions with the suspect) and the real-time data being processed. The augmented reality interface provided the officer with a visual alert, projecting the emotional risk in real-time. Recognizing the importance of preventing an emotional breakdown, the officer took a tactical pause, shifting the conversation to a less confrontational topic to de-escalate the situation.

[0066] This pre-emptive action proved effective. The suspect's physiological indicators stabilized, and their vocal stress returned to baseline levels. By proactively adjusting the interrogation strategy based on the system's emotional insights, the officer was able to maintain control over the session and gather critical information without provoking an emotional outburst from the suspect. Additionally, the secure biometric authentication ensured that all emotional data remained tied to the correct individual, preserving the integrity of the data collected during the session.

[0067] The use of the Wearable System not only allowed the officer to gain real-time insights into the suspect's emotional state but also helped prevent a potential crisis, enabling a smoother and more effective interrogation. The system's predictive modelling and real-time monitoring features proved invaluable in this high-pressure situation, demonstrating its critical role in law enforcement settings where emotional stability can influence the outcome of an investigation.

[0068] While there has been illustrated and described embodiments of the present invention, those of ordinary skill in the art, to be understood that various changes may be made to these embodiments without departing from the principles and spirit of the present invention, modifications, substitutions and modifications, the scope of the invention being indicated by the appended claims and their equivalents.

FIGURE DESCRIPTION

[0069] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate an exemplary embodiment and explain the disclosed embodiment together with the description. The left and rightmost digit(s) of a reference number identifies the figure in which the reference number first appears in the figures. The same numbers are used throughout the figures to reference like features and components. Some embodiments of the system and methods of an embodiment of the present subject matter are now described, by way of example only, and concerning the accompanying figures, in which:

[0070] Figure - 1 illustrates the Wearable System for Suspect Real-Time Physiological and Emotional Monitoring is designed with key components integrated into a wristband form factor. At the core of the device is the heart rate sensor, positioned on the underside of the wristband in direct contact with the user's skin. This placement ensures accurate detection of blood flow variations, essential for monitoring emotional states. Alongside this, the skin conductance sensor is also located on the wrist-facing side of the band, measuring changes in skin conductivity that signal emotional arousal. The biometric authentication mechanism is placed near the clasp or closure of the wristband. This includes a fingerprint scanner, which ensures that the system securely attributes data to the correct user. The placement is optimized for ease of use, allowing the user to authenticate themselves each time the device is worn. The voice stress analysis module, integrated near a small microphone embedded in the band, captures vocal data to analyse stress levels. This module works in harmony with other sensors to provide a comprehensive emotional profile. Internally, the machine learning processor and predictive behavioural modelling module are housed within the wristband. These components process the real-time physiological and vocal data, applying advanced algorithms to generate emotional insights and predict potential escalations in emotional intensity. The predictive behavioural modelling module is particularly crucial, as it allows professionals to anticipate changes in the subject's emotional state, facilitating timely interventions. The system includes a Bluetooth or Wi-Fi module on the side of the wristband, which enables wireless communication with external devices. This connection allows real-time emotional data to be transmitted to a companion mobile device or computer system. On these external devices, an augmented reality interface displays emotional insights in real time. This interface overlays data on a live video feed, offering professionals immediate, intuitive visual feedback on the subject's emotional state, making it easier to respond effectively during interactions. , Claims:1. A wearable system for real-time physiological and emotional monitoring, comprising:
a plurality of physiological sensors configured to measure heart rate, skin conductance, and facial expressions in real-time;
a voice stress analysis module operatively connected to analyse vocal patterns to detect emotional states;
a machine learning engine operatively connected to the physiological sensors and the voice stress analysis module, configured to process real-time physiological and vocal data and generate an emotional profile of the subject;
context-aware algorithms configured to refine emotional assessments based on session-specific variables, including time, duration, and external stimuli;
a biometric authentication mechanism comprising fingerprint and retina scanning components, ensuring secure and accurate association of emotional data with the subject;
an augmented reality interface configured to display real-time emotional data and predictive alerts on a live video feed of the session;
a predictive behavioural modelling module configured to analyse historical and real-time data to forecast emotional escalations;
a data encryption and ethical compliance module configured to anonymize and secure data, ensuring compliance with legal and ethical standards.
2. The system of claim 1, wherein the physiological sensors include a photoplethysmography sensor configured to measure heart rate by detecting blood volume changes beneath the skin in real-time.
3. The system of claim 1, wherein the voice stress analysis module is configured to detect minute modulations in pitch, tone, and frequency, providing supplementary emotional data beyond physiological indicators.
4. The system of claim 1, wherein the machine learning engine is pre-trained with datasets correlating physiological and vocal indicators to emotional states, enabling the generation of a multi-faceted emotional profile in real-time.
5. The system of claim 1, wherein the context-aware algorithms are configured to automatically adjust the weight assigned to physiological and vocal data based on external conditions such as session type, environmental factors, and topics discussed.
6. The system of claim 1, wherein the biometric authentication mechanism ensures that all physiological and emotional data collected is securely attributed to the correct subject, preventing unauthorized access and ensuring the integrity of emotional assessments.
7. The system of claim 1, wherein the augmented reality interface is configured to overlay emotional data on a live video feed, providing intuitive visual cues to the professional, including stress levels, emotional trends, and predictive alerts.
8. The system of claim 1, wherein the predictive behavioural modelling module is configured to analyse long-term emotional patterns to forecast potential emotional escalations, allowing professionals to take timely pre-emptive interventions.
9. The system of claim 1, wherein the data encryption and ethical compliance module is configured to employ blockchain-based encryption to ensure secure transmission and storage of emotional data, with built-in compliance alerts to maintain adherence to legal and ethical standards.

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