Adaptive digital prevention technology system and method for multimodal emotion detection and adaptive intervention

Abstract

A method and system for detecting and regulating emotional instability by detecting a pattern of physiological data baseline deviations and mapping them to stimuli to determine appropriate interventions. The method includes collecting physiological and environmental data and extracting features from the physiological data to determine an individualized physiological baseline. Deviations in the individualized physiological baseline are quantified and used to determine emotional stability and prescribe appropriate interventions. The system comprises a plurality of sensors, a sensor data processing module, a sensor fusion module, a stimulus identification module, and a detection intervention prevention module.

Description

ADAPTIVE DIGITAL PREVENTION TECHNOLOGY SYSTEM AND METHOD FOR MULTIMODAL EMOTION DETECTION AND ADAPTIVE INTERVENTION CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States provisional patent application US63 / 833,859 filed 24 January 2025, which is hereby incorporated by reference herein in its entirety.TECHNICAL FIELDThe present invention pertains to the field of mental health monitoring and intervention technologies, specifically leveraging Emotion Al, sensor fusion, and adaptive frameworks to address a broad spectrum of mental health challenges.BACKGROUND

[0002] Mental health challenges are increasingly recognized as a critical global issue, affecting individuals across all demographics and geographies. Despite advancements in both traditional and digital mental health care, several persistent gaps undermine the accuracy, efficacy, and scalability of existing solutions. These limitations often prevent individuals from receiving timely and personalized care, contributing to the progression of mental health conditions from manageable stages to acute crises.

[0003] Traditional mental health care relies heavily on intermittent clinician observations, as well as clinical interviews and patient health questionnaires or general anxiety scoring, and other self-reported questionnaires for assessing mental and emotional, and related scales. While these instruments provide standardized assessment snapshots, they are limited in frequency, objectivity, and sensitivity to early subclinical changes. These methods are inherently subjective and fail to capture real-time fluctuations in emotional states, limiting their reliability.Geographic, financial, and cultural barriers restrict equitable access to mental health care, particularly in underserved or low-resource areas. The stigma surrounding mental health deters individuals from seeking intervention, often delaying care until conditions escalate. Traditional care models primarily address mental health conditions after they have progressed to severe stages, rather than focusing on early detection and preventive interventions.

[0004] Many digital mental health treatment platforms provide generalized interventions that overlook individual differences in emotional patterns, cultural context, and environmentaltriggers. This static approach diminishes their effectiveness. Current digital solutions offered by digital mental health treatment platforms rely on periodic check-ins or self-reported data, offering only fragmented insights into a user’s mental health state. For instance, mood-tracking apps that prompt users once daily fail to capture dynamic fluctuations in mood throughout the day. Ensuring compliance with regulations like the European General Data Protection Regulation (GDPR) and United States Health Insurance Portability and Accountability Act (HIPAA) while maintaining user trust poses significant challenges for digital mental health systems.

[0005] In one example method for sensing of biometric data and use thereof for determining emotional state of a user, United States patent US 12232886 B2 describes collecting biometric data via a sensor platform from sensors, extracting a plurality of representative features from the plurality of biometric data, receiving a plurality of user affect parameters associated with the plurality of representative features, correlating the plurality of user affect parameters with the plurality of representative features to determine a set of representative feature-affect pairings for the data model as a plurality of model data parameters, and storing the data model for subsequent use in determining the real time state of the user.

[0006] One of the most pervasive issues in both traditional and digital mental health solutions is the lack of actionable, real-time insights to guide users on when to seek professional help. This gap often results in delays, allowing mild emotional disruptions to escalate into severe mental health conditions. Existing solutions fail to provide personalized, real-time guidance, leaving users uncertain about when their symptoms warrant professional intervention.

[0007] Existing wearable devices can capture physiological signals such as heart rate variability (HRV) or electrodermal activity (EDA), however, these systems typically apply fixed, population-based thresholds and do not integrate behavioral or contextual information to interpret emotional regulation in real time. As a result, subtle deviations in emotional stability often remain undetected until they progress to measurable dysfunction or distress.SUMMARY

[0008] It is an object of the present invention to provide a computer-implemented system and method for continuously detecting, interpreting, and responding to changes in emotional regulation using multimodal physiological, behavioral, and contextual data. The system is configured to personalize interpretation of emotional states, associate deviations with contextual stimuli, and calibrate preventative or supportive interventions through adaptive feedback, therebyenabling earlier, individualized mental health support while maintaining non-diagnostic operation.

[0009] In an aspect there is provided a method for monitoring and regulating an emotional state comprising: generating an individualized physiological baseline by: collecting a plurality of physiological data signals from a user using a plurality of physiological sensors; collecting at least one environmental data signal using at least one environmental sensor; extracting a plurality of physiological features from the plurality of physiological data signals using a sensor data processor module; and calculating a rolling mean and a standard deviation for each of the plurality of physiological features over a temporal window to generate the individualized physiological baseline; characterizing a deviation from the individualized physiological baseline by: collecting real-time physiological data for the user from the plurality of physiological sensors; determining a frequency of deviation, a magnitude of deviation, and a duration of deviation between the real-time physiological data and the individualized physiological baseline; and generating an emotion pattern profile expressed as a three-dimensional vector of a regularity index, an intensity coefficient, and a duration parameter of the deviation between the real-time physiological data and the individualized physiological baseline, the regularity index, intensity coefficient, and duration parameter calculated based on the magnitude of the deviation between the real-time physiological data and the individualized physiological baseline; and prescribing an intervention to the user if the deviation between the real-time physiological data and the individualized physiological baseline exceeds at least one operating threshold.

[0010] In an embodiment, the individualized physiological baseline further incorporates user provided information comprising one or more of previous medical history, user personality profile, personal calendar, workplace schedule, medication use, diagnosed conditions, prior episodes of stress, prior episodes of anxiety, sleep schedule, self-reported mood rating, selfreported stress rating, symptom reporting, lifestyle habits, caffeine intake, alcohol intake, drug intake, physical activity level, travel, time-zone changes, shift-work pattern, workplace schedule, and clinician-provided guidance.

[0011] In another embodiment, the temporal window is one or more of a minute, a number of minutes, an hour, an a number of hours, a day, a number of days, a week, a number of weeks, a month, a number of months, a year and a number of years.

[0012] In another embodiment, the intervention is prescribed by providing an alert on an electronic device.

[0013] In another embodiment the method further comprises preprocessing the physiological data and the environmental data using one or more of motion-artifact labelling, adaptive filtering, on-device noise characterization, cross-validation, varying sampling frequency, signal quality scoring, bandpass filtering, baseline wander removal, powerline interference suppression, outlier detection, missing-data imputation, sensor contact, impedance validation, timestamp alignment, resampling and interpolation, beat detection and artifact correction, normalization, and calibration deviation correction.

[0014] In another embodiment, the at least one operating threshold comprises a first threshold and a second threshold.

[0015] In another embodiment, the at least one operating threshold is determined based on one or more of a personality coefficient, historical behavioral patterns, prior intervention response history, and circadian indicators.

[0016] In another embodiment, the personality coefficient is derived from one or more of a stored personality profile, a clinician administered assessment, a psychometric questionnaire, a structured onboarding assessment, longitudinally from behavioral response patterns, and longitudinal inference from user engagement behavior.

[0017] In another embodiment, the intervention prescribed by the detection intervention prevention module is a self-regulated intervention comprising one or more of breathing guidance, posture adjustment, haptic cues, paced respiration, guided grounding exercises, mindfulness prompts, progressive muscle relaxation, cognitive reframing prompts, micro-break scheduling, hydration prompts, sleep hygiene prompts, notification suppression, audio cues, visual prompts, and environmental modification.

[0018] In another embodiment the method further comprises collecting post-intervention physiological data and updating the at least one operating threshold.

[0019] In another embodiment, updating the at least one operating threshold is based on a postintervention recovery measured by calculating a recovery time, an adherence, and a residual deviation, and transmitting the recovery time, the adherence, and the residual deviation to a feedback controller.

[0020] In another embodiment, the intervention prescribed is a clinician escalation protocol.

[0021] In another aspect there is provided a system for monitoring and regulating an emotional state comprising: a plurality of physiological sensors; at least one environmental sensor; a sensor data processing module configured to: validate and preprocess a plurality of sensor data signals collected by the plurality of physiological sensors and the at least one environmental sensor; and extract a plurality of physiological features from the plurality of physiological data signals collected by the plurality of physiological sensors; a sensor fusion module configured to: construct an individualized physiological baseline for a user based on the plurality of physiological features; quantify deviation between the individualized physiological baseline and real-time physiological data collected by the plurality of physiological sensors; and generate an emotion pattern profile expressed as a three-dimensional vector of a regularity index, an intensity coefficient, and a duration parameter of the deviation between the real-time physiological data and the individualized physiological baseline; a stimulus identification and detection module configured to map the plurality of deviations onto a plurality of stimuli to establish a plurality of causal links; and a detection intervention prevention module configured to prescribe an intervention, deliver the appropriate intervention to a user electronic device, collect postintervention physiological data, and update at least one operating threshold in the individualized physiological baseline.

[0022] In an embodiment, the plurality of physiological sensors comprise one or more of a electrocardiography (ECG) sensor, photoplethysmography (PPG) sensor, thermistor, electrodermal activity (EDA) sensor, accelerometer, gyroscope, respiration sensor, skin temperature sensor, blood oxygen saturation (SpCh) sensor, and electromyography (EMG) sensor.

[0023] In another embodiment, the at least one environmental sensor is one or more of a noise sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, an air quality sensor, and a location sensor.

[0024] In another embodiment, the plurality of physiological features include one or more of the heart rate, root mean square of successive differences, standard deviation of normal-to-normal intervals, percentage of normal-to-normal intervals greater than a time duration, and electrodermal activity.

[0025] In another embodiment, the system further comprises a clinician alert protocol for alerting a clinician on a clinician electronic device, wherein the clinician alert protocol is initiated after persistence or recurrence of deviations exceeding a safety-bounded threshold.

[0026] In another embodiment, the system further comprises an adaptive feedback loop for evaluating an effectiveness of the intervention.

[0027] In another embodiment, the system further comprises a personality calibration engine for calibrating an operating threshold.

[0028] In another embodiment, the intervention prescribed by the detection intervention prevention module is a self-regulated intervention comprising one or more of breathing guidance, posture adjustment, haptic cues, paced respiration, guided grounding exercises, mindfulness prompts, progressive muscle relaxation, cognitive reframing prompts, micro-break scheduling, hydration prompts, sleep hygiene prompts, notification suppression, audio cues, visual prompts, and environmental modification.

[0029] In an embodiment, there is a computer-implemented method, the method comprising acquiring a plurality of real-time multimodal data describing an emotional state, determining a first emotional state based on the multimodal data, and calibrating an intervention responsive to the emotional state based on the multimodal data. In some implementations, the method further comprises receiving feedback to the intervention and re-calibrating the intervention based on the feedback.

[0030] In an embodiment, there is a computer-implemented method comprising acquiring realtime multimodal data from one or more physiological, behavioral, and environmental sources; processing the data to identify deviations from a personalized baseline associated with emotional regulation; associating the deviations with contextual stimuli; and calibrating an intervention responsive to the identified emotional state. In some implementations, the method further comprises receiving post-intervention feedback and adaptively recalibrating subsequent interventions based on the feedback.

[0031] In an embodiment, there is a system for precision mental health care, the system comprising a data hub configured to collect multimodal data and to manage contextual metadata, wherein the multimodal data includes physiological signals, behavioral inputs, environmental factors (e.g., noise, light), medication adherence data, personality data, and nutritional data. In some implementations, the data hub is further configured to respond to specific requests fortargeted data or sensor insights, enabling the system to adjust dynamically collect multimodal data to meet evolving sensor strengths or limitations. In further implementations, the data hub is further configured to annotate the multimodal data with sensor metadata including sensor type and quality. In certain implementations, the system further comprises a pattern recognition module configured to build baselines for emotion patterns and medication adherence, including: tracking trends in regularity, intensity, and duration of emotional responses, and identifying deviations that may indicate a need for medication adjustments or changes in emotional state; and identify emotion recognition and detect deviations from established emotional baselines and medication adherence, providing feedback on treatment efficacy of ongoing interventions, while incorporating personality traits to tailor emotional and medication interventions.

[0032] In some implementations, the system further comprises a stimulus identification module configured to: map emotional triggers to emotion response patterns and medication adherence data, infer emotional states and treatment efficacy based on the emotion response patterns, and refine emotion patterns and medication responses based on detected deviations from external stimuli (e.g., stressful events) and user behavior, enabling dynamic adaptation of interventions and improving treatment efficacy over time.

[0033] In certain implementations, the system further comprises a personalized intervention module configured to provide real-time medication dosage recommendations, emotion regulation interventions, and nutrition guidance, based on continuous feedback from a data hub, a pattern recognition module, and a stimulus identification module; and adapt therapy interventions, medication recommendations, and nutritional interventions in real-time, ensuring dynamic adjustment of emotional progression, therapy accuracy, behavior, and overall wellness (including through tailored nutritional recommendations).

[0034] In some implementations, the system is configured to provide mental health treatment based on an emotion cycle framework, the emotion cycle framework comprising the stages of emotion patterns, emotion recognition, emotion regulation, emotional quotient, behavior, feedback loop and autonomy.

[0035] In certain implementations, each stage of the emotion cycle framework is linked to at least one of the data hub, the pattern recognition module, the stimulus identification module, and the personalized intervention module, and wherein the system tracks treatment efficacy, therapy accuracy, and adapts over time by monitoring changes in emotional stability, medicationadherence, and user feedback, adjusting interventions when specific personality-driven thresholds for emotional state, medication adherence, and emotion regulation are exceeded.

[0036] In further implementations, the multimodal data includes at least one of the following: heart rate variability (HRV), electrodermal activity (EDA), sleep patterns, activity levels, environmental conditions, personality data, and medication adherence data, and wherein the system tracks medication adherence, treatment efficacy, and personality traits over time and adjusts recommendations accordingly, based on real-time thresholds that identify when emotional states, personality profiles, or physiological responses deviate from established baselines.

[0037] In certain implementations, the system is configured to implement a feedback loop, the feedback loop configured to: continuously monitor and adapt emotion recognition, emotion regulation, medication adherence, nutritional interventions, treatment efficacy, and personality-driven recommendations in real-time; and optimize personalized interventions based on changes in the user's emotional state, personality, medication adherence, physiological responses, and nutritional intake, ensuring dynamic adjustments in both emotional regulation, therapy accuracy, and overall wellness outcomes, automatically adjusting interventions when preset personality-driven thresholds for emotional or physiological states are exceeded, thus enhancing treatment outcomes.

[0038] In an embodiment, there is a system for trauma-informed mental health care, comprising a data hub configured to: collect multimodal data, including physiological signals (e.g., HRV, EDA), behavioral inputs (e.g., sleep patterns, activity levels), environmental factors (e.g., noise, light), medication adherence data, personality traits, and nutritional intake data, annotate collected data with contextual metadata to dynamically adjust data collection based on sensor capabilities or limitations, leverage predictive analytics to identify early warning signs of trauma-related risks and forecast potential deviations in emotional or physiological baselines, and apply prescriptive analytics to recommend therapeutic interventions, such as emotion regulation techniques, medication adjustments, or nutrition plans. The system further comprises a pattern recognition module configured to: establish baselines for emotional and physiological patterns, medication adherence, and personality-driven thresholds by tracking trends in regularity, intensity, and duration of responses, and detect deviations from these baselines, including those indicative of trauma stages (pre-trauma, peri-trauma, post-trauma), andrecommend early interventions using predictive analytics. The system further comprises a stimulus identification module configured to: map emotional triggers to patterns of response and correlate deviations in emotional and physiological trends with external stimuli, medication adherence, and personality traits, and refine emotion patterns using prescriptive analytics to recommend targeted interventions tailored to individual recovery trajectories. The system further comprises a personalized intervention module configured to: deliver real-time interventions, including emotion regulation techniques (e.g., mindfulness exercises, grounding), medication dosage recommendations, and nutrition guidance tailored to the user’s physiological and emotional state, adapt interventions dynamically based on continuous feedback, ensuring therapy accuracy, treatment efficacy, and alignment with trauma-specific needs and personality traits, and continuously optimize intervention strategies using insights derived from machine learning algorithms and real-time feedback loops.

[0039] In some implementations, the system employs a trauma-phase framework comprising: monitoring baseline resilience and identifying risk factors using predictive analytics to suggest resilience-building interventions in a pre-trauma stage; detecting acute emotional and physiological responses during trauma exposure and delivering immediate psychological first aid in a peri-trauma stage; identifying early post-traumatic symptoms and optimizing therapeutic interventions using prescriptive analytics in an early post-trauma stage; and monitoring longterm symptom patterns to manage chronic trauma responses and refine care strategies using predictive and prescriptive analytics in a late post-trauma stage.

[0040] In further implementations, the multimodal data includes: physiological signals (e.g., HRV, EDA), behavioral inputs (e.g., sleep patterns, activity levels), environmental factors (e.g., noise, light exposure), personality traits, including emotional resilience, nutritional intake data allowing the system to incorporate dietary factors into recovery strategies, medication adherence data enabling dynamic adjustments to treatment recommendations, and predictive and prescriptive analytics insights to forecast risks and recommend interventions.

[0041] In certain implementations, the system further comprises a feedback loop configured to monitor and refine interventions in real time based on physiological signals, emotional states, personality traits, medication adherence, and nutritional data; utilize predictive analytics to anticipate risks of relapse or chronic symptoms; apply prescriptive analytics to deliver preciseadjustments to emotion regulation strategies, medication dosages, and nutritional plans; and provide actionable insights to healthcare providers to support collaborative trauma care.

[0042] In some implementations, the trauma-phase framework integrates: real-time monitoring and intervention during the peri-trauma stage triggered by acute physiological or emotional changes; predictive capabilities to identify deviations from baseline patterns and anticipate trauma-related risks; prescriptive capabilities to recommend interventions tailored to the user’s physiological state, personality profile, and historical data; long-term monitoring and relapse prevention during the post-trauma stage, with feedback-driven updates to interventions; and personality-driven thresholds to dynamically adjust therapeutic recommendations, ensuring alignment with recovery trajectories.BRIEF DESCRIPTION OF THE DRAWINGS

[0043] For a better understanding of the present invention, as well as other aspects and further features thereof, reference is made to the following description which is to be used in conjunction with the accompanying figures which illustrate embodiments or aspects of the invention, where:

[0044] Figure l is a schematic diagram illustrating an adaptive digital prevention technology (ADPT) system;

[0045] Figure 2 shows a method for multimodal emotion detection and adaptive intervention;

[0046] Figure 3 is a schematic diagram illustrating the development of an emotion pattern profile;

[0047] Figure 4 is a schematic diagram illustrating the generation of an individualized physiological baseline;

[0048] Figure 5 is a schematic diagram illustrating an embodiment of the adaptive digital prevention technology system;

[0049] Figure 6 illustrates a computing system configured to implement the ADPT systems and methods described in accordance with some embodiments herein;

[0050] Figure 7 illustrates a process flow for calibrating an intervention responsive to an emotional state in accordance with some embodiments described herein;

[0051] Figure 8 illustrates a table indicating the relationship between emotion categories and multimodal data in accordance with some embodiments described herein; and

[0052] Figure 9 illustrates a non-diagnostic adaptive inference and intervention loop.DETAILED DESCRIPTION

[0053] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Working examples provided herein are considered to be non-limiting and merely for purposes of illustration.

[0054] As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise.

[0055] The term “comprise” and any of its derivatives (e.g. comprises, comprising) as used in this specification is to be taken to be inclusive of features to which it refers, and is not meant to exclude the presence of any additional features unless otherwise stated or implied. The term “comprising” as used herein will also be understood to mean that the list following is non-exhaustive and may or may not include any other additional suitable items, for example one or more further feature(s), component(s) and / or element(s) as appropriate.

[0056] As used herein, the terms “having,” “including” and “containing,” and grammatical variations thereof, are inclusive or open-ended and do not exclude additional, unrecited elements and / or method steps, and that that the list following is non-exhaustive and may or may not include any other additional suitable items, for example one or more further feature(s), component(s) and / or element(s) as appropriate. A composition, device, article, system, use, process, or method described herein as comprising certain elements and / or steps may also, in certain embodiments consist essentially of those elements and / or steps, and in other embodiments consist of those elements and / or steps and additional elements and / or steps, whether or not these embodiments are specifically referred to.

[0057] As used herein, the term “about” refers to an approximately + / - 10% variation from a given value. It is to be understood that such a variation is always included in any given value provided herein, whether or not it is specifically referred to. The recitation of ranges herein is intended to convey both the ranges and individual values falling within the ranges, to the same place value as the numerals used to denote the range, unless otherwise indicated herein.

[0058] The use of any examples or exemplary language, e.g. “such as”, “exemplary embodiment”, “illustrative embodiment" and “for example” is intended to illustrate or denote aspects, embodiments, variations, elements or features relating to the invention and not intended to limit the scope of the invention.

[0059] As used herein, the terms “connect” and “connected” refer to any direct or indirect physical association between elements or features of the present disclosure. Accordingly, these terms may be understood to denote elements or features that are partly or completely contained within one another, attached, coupled, disposed on, joined together, in communication with, operatively associated with, etc., even if there are other elements or features intervening between the elements or features described as being connected.

[0060] Herein is described a system and method for continuously measuring emotional dynamics across physiological, behavioral, and environmental domains, adapting to individual baselines and personality factors, and providing real-time interventions that prevent deterioration of mental well-being. The presently described adaptive system and method provide continuous detection, calibration, and prevention of emotional dysregulation. The present system and method integrate physiological, behavioral, and environmental sensing within a modular computing architecture comprising an Autonomous Intelligent Device (AID), a Regularity-Intensity-Duration (RID) baseline engine, a Stimulus Identification Module (SID), and a Detection-Intervention-Prevention Module (DIP) to provide an autonomous digital personalized therapy (ADPT) system. The system operates through a closed-loop Measure — Manage — Adapt control framework that compares real-time multimodal data to individualized baselines and personality-calibrated thresholds to determine emotional state and select context-appropriate interventions. Continuous feedback updates the baseline and threshold parameters to improve precision over time. The system supports early, presymptomatic identification of emotional deviation, enabling preventive mental-health support while maintaining data privacy and security through federated-learning synchronization.

[0061] Systems and methods described herein are configured to deliver real-time emotion recognition, regulation, and resilience-building. The ADPT systems described herein are continuous and adaptive such that they can provide different and updated interventions responsive to multimodal data and feedback received in real-time. By analyzing multimodal physiological, behavioral, and contextual data, the ADPT systems described herein may track transitions across the mental health continuum (e.g., including presymptomatic, mild, acute, relapse, resilience-building, and post-symptomatic stages). By tracking such transitions across the mental health continuum, The ADPT systems and methods relating to ADPT systemsdescribed herein may provide thresholds for timely professional intervention and relapse management. The system detects deviation events and estimates regulation state over one or more monitoring windows and does not require longitudinal trajectory scoring.

[0062] The following examples are illustrative and non-limiting. All numeric values, ranges, decay factors, and thresholds are illustrative and non-limiting.

[0063] Figure 1 is a schematic diagram illustrating an adaptive digital prevention technology system. The adaptive digital prevention technology (ADPT) system can be used to identify, interpret, and manage emotional states in real time through multimodal sensing and adaptive feedback. The ADPT system comprises a sensor data processor module 16, a sensor fusion module 18 (RID), a stimulus identification module (SID) 20, and a detection intervention prevention module (DIP) 22. A plurality of physiological sensors 12 can be used to collect physiological data from a user, together with user provided information 36 and environmental sensors 14. The physiological data collected from the physiological sensors 12 can be used to determine the emotional state of the user. Examples of physiological sensors 12 can include but are not limited to electrocardiography (ECG) sensors, photoplethysmography (PPG) sensors, thermistors, and electrodermal activity sensors. Additional physiological sensors can include, without limitation, respiration sensors, skin temperature sensors, inertial measurement units (IMUs), blood oxygen saturation sensors (SpCh), and galvanic skin response sensors. For a given physiological sensor 12, a physiological feature can be determined that is indicative of an emotional state.

[0064] In an example, ECG signals measure the electrical activity of the heart and comprise wave patterns that represent atrial and ventricular contraction or relaxation. From the raw ECG data, physiological features such as the heart rate, and heart rate variability metrics can be determined. Other examples of physiological features that can be extracted from heart rate variability can also be determined, including but not limited to the root mean square of successive differences (RMSSD) and standard deviation of normal-to-normal intervals (SDNN). Heart rate variability metrics are reflective of the autonomic nervous system which include sympathetic influences which indicate a fight or flight response or parasympathetic influences which indicate rest and digest type states. Non-limiting examples of physiological feature signals and their corresponding extracted features and operational uses are provided in Table 1.Additionally, environmental sensors 14 can be used to collect data about the surroundings of auser and can be used to provide context for physiological data and physiological features. The environmental sensors 14 can include but are not limited to microphones, accelerometers, ambient noise sensors, and ambient light sensors. Environmental sensors can further include location sensors, barometric pressure sensors, proximity sensors, and time-of-day indicators derived from system clocks. Table 1 includes environmental signals that can be collected with examples of features that can be extracted from them and their operational uses.

[0065] Table 1 : Physiological and environmental sensors used in the ADPT system&&

[0066] Physiological data collected by the plurality of physiological sensors 12 and environmental sensors 14 is received and preprocessed by the sensor data processor module 16 (AID), which is the signal integrity hub of the ADPT system. The sensor data processor module 16 can comprise a computing device that includes a processor, memory, storage, input / output devices, a communication bus, and a secure network interface. Alternatively, the sensor data processor module 16 can be entirely cloud based. The sensor data processor module 16 acquires, validates, and preprocesses multimodal data from the physiological sensors 12 and the environmental sensors 14 such that data analysis conducted by the ADPT system is based on verified, high fidelity inputs. The functions of the sensor data processor module 16 can include but are not limited to determining sensor availability and managing redundancy. The sensor data processor module 16 can also monitor the contact quality of the plurality of physiological sensors 12 and can automatically recalibrate during motion or sweat interference. For example, thesensor data processor module 16 can include a contact quality algorithm that monitors skin impedance variance in real time and initiates recalibration when contact quality falls below a predefined threshold for more than a specified duration.

[0067] Another function of the sensor data processing module 16 (SID) is to perform edgelevel preprocessing which comprises adaptive filtering, motion-artifact labeling, and compression of validated feature vectors before transmission to the sensor fusion module. The sensor data processor module 16 can also perform on-device noise characterization using embedded accelerometer and thermistor data to discriminate between physiological change and external environmental disturbances to improve the signal to noise ratio. The sensor data processor module 16 can also comprise redundant sensing channels and be configured to perform cross-validation between channels to identify sensor dropout or signal corruption, such as using dynamic fallback logic which substitutes redundant signals for missing or degraded channels to maintain data continuity. The sensor data processor module 16 can store sensor integrity metadata such as sampling timestamp, contact quality index, and artifact probability score. The sensor data processor module 16 can include context specific sampling logic that can prioritize certain signals when the sensor fusion module 18 and the SID module 20 detect irregularities. For example, the context specific sampling can increase the increase sampling frequency for physiological data signals such as heart rate variability and electrodermal activity when the deviations exceed a prespecified threshold are detected. Additional context can be obtained from user provided information 36.

[0068] The sensor data processor module 16 transmits data to the sensor fusion module 18 (RID) and other modules downstream in the ADPT system and can have a data on demand interface for downstream modules to request supplemental inputs. Prior to transmission of structured data, edge preprocessing can be used for artifact filtering and time-weighted normalization. In an example physiological data can be sampled at frequencies between 0.5 and 4 Hz and aggregated into rolling 30 second windows for transfer to the sensor fusion module 18. The sensor data processor module 16 acts as a signal processing device to process data from the physiological sensors and environmental sensors to provide temporal synchronization across signal. The operations of the sensor data processor module 16 can occur locally to reduce cloud dependency and support mobile or field deployment.

[0069] The sensor fusion module 18 (RID) receives structured physiological and environmental data from the sensor data processor module 16 (AID) and acts as an individualized baseline engine to generate temporal emotion patterns and create an individualized physiological baseline model. The temporal emotion patterns quantify the regularity, intensity, and duration of deviation from personalized baselines. The sensor fusion module 18 translates emotion theory as described by James-Lange and LeDoux into continuous signal analysis for the detection of subclinical dysregulation. From the plurality of physiological data signals collected, a plurality of features for each figure can be determined. For example, from ECG signals, the heart rate in beats per minutes, and heart rate variability metrics such as root mean square of successive differences (RMSSD), standard deviation of normal-to-normal intervals (SDNN), and percentage of NN intervals greater than a specific time duration, for example 50ms (pNN) can be extracted. For each feature of each physiological data signal, a rolling mean (p) and a standard deviation (G) can also be computed. Each feature can be analyzed in multi-window temporal sequences that range in scale from minutes to weeks. Using the multi-window temporal sequences of the extracted physiological features, an emotion pattern profile can be generated and used for contextual analysis.

[0070] To generate the emotion pattern profile, for each physiological or behavioral feature, a temporal stability vector can be computed. In some implementations, a temporal stability vector is computed per temporal window for each physiological feature or behavioral feature. Multiple temporal stability vectors corresponding to different time windows are aggregated to form the emotion pattern profile, thereby capturing short-term, intermediate, and extended temporal characteristics of emotional variability. The temporal stability vector represents the frequency amplitude, and persistence of deviations across multiple time windows. The time windows selected can include but are not limited to a minute, a number of minutes, an hour, an a number of hours, a day, a number of days, a week, a number of weeks, a month, a number of months, a year and a number of years. In some examples the time window can be 5 minutes, 1 hours, 24 hours, and 7 days. Within a sliding 24-hour window, the sensor fusion module 18 can calculate a regularity index (Ri) by determining the mean recurrence interval of deviation events that exceed one standard deviation. The sensor fusion module 18 also determines an intensity coefficient (li) by averaging the peak deviation magnitudes (|A|) normalized to each feature’s baseline standard deviation (G) to quantify a physiological arousal intensity. Physiological arousal intensity refersto the normalized magnitude of deviation of a physiological feature from its individualized baseline, reflecting the strength of autonomic activation associated with an emotional response. Higher arousal intensity corresponds to greater sympathetic nervous system engagement or reduced parasympathetic modulation, as inferred from relative deviation amplitudes.

[0071] The sensor fusion module 18 computes a duration parameter (Di) by measuring the continuous time during which a deviation magnitude remains above a baseline standard deviation. The duration parameter classifies deviations as transient, intermediate, or sustained. The regularity index, intensity coefficient, and duration parameter are integrated into a composite emotion pattern profile which is expressed as a three-dimensional vector that captures the frequency, amplitude, and persistence of emotional change. To improve baseline precision and reduce false positive detection, the sensor fusion module 18 can apply time-weighted averaging to assign higher weights to data captured during a user’s identified circadian peak hours. The sensor fusion 18 module can continuously update the baselines which comprise the rolling mean and the standard deviation using exponentially weighted moving averages with a decay factor (1), such as between 0.05 and 0.25. The decay factor (1) determines the relative weighting applied to newly acquired data versus historical data when updating the rolling mean and standard deviation used to define individualized physiological baselines. Lower values of emphasize long-term stability by reducing sensitivity to short-term fluctuations, while higher values of increase responsiveness to recent physiological changes, and can result in faster adaptation to evolving conditions.

[0072] In some embodiments, 1 may be selected within a range between approximately 0.05 and 0.25 to balance noise suppression with adaptive responsiveness for autonomic and behavioral signals that exhibit both short-term variability and longer-term trends. This range has been found suitable for preserving baseline integrity while allowing gradual recalibration in response to sustained lifestyle, environmental, or contextual changes.

[0073] The decay factor is not fixed and may be dynamically adjusted based on signal type, data quality, circadian phase, or user-specific characteristics. In alternative embodiments, 1 may be selected outside the stated range or determined algorithmically without an explicit numerical bound. The continuous updating of the baselines allows gradual adaptation to lifestyle or environmental changes.

[0074] The sensor fusion module 18 can also be used to generate a resilience indicator (Rs) which is the recovery half-time between the onset and resolution of a deviation. The resilience indicator can be used to adjust threshold placement within the DIP module 22. The sensor fusion module 18 can also be used to generate a resilience indicator (Rs), which represents the recovery half-time between the onset of a deviation and its resolution toward an individualized baseline.

[0075] The resilience indicator is used to adjust threshold placement within the detection intervention prevention (DIP) module 22 because recovery dynamics provide a user-specific measure of regulatory capacity. Users exhibiting faster recovery following deviations can tolerate tighter thresholds without increased false positive alerts, whereas users with slower recovery benefit from more conservative threshold placement to prevent excessive intervention, alert fatigue, or premature escalation. In this manner, threshold placement is adaptively personalized based on demonstrated physiological regulation rather than static population assumptions. The sensor fusion module 18 can further flag outlier episodes and anomalies exhibiting excessive duration, such as deviations persisting beyond a predefined temporal limit, or excessive magnitude, such as deviations exceeding a multiple of a baseline standard deviation. Flagged outlier episodes may automatically trigger the DIP module 22 to initiate clinician review or elevated monitoring.

[0076] The stimulus identification module (SID) 20 maps flagged deviations and anomaly events to contextual stimuli by temporally correlating deviation onset, intensity, and duration with environmental sensor signals and user-provided contextual inputs. Candidate stimuli are scored based on temporal proximity, co-occurrence frequency, and historical association strength between stimulus features and prior deviations. The SID module 20 further refines stimulus attribution by weighting contextual features using prior intervention outcomes and feedback received from the DIP module 22, thereby iteratively improving causal inference between stimuli and emotional responses. The sensor fusion module 18 can flag outlier episodes and anomalies that have excessive durations such as those exceeding 12 hours or that have deviations with high magnitudes such as those exceeding 3 times the standard deviation. The flagging of outlier episodes can automatically trigger the DIP module 22 to initiate clinician review. The SID module 20 maps sensor fusion module 18 flagged anomalies and outlier episodes to contextual triggers provided by environmental sensors 14 and user provided information 36, and scores deviations and profiles triggers to establish causal links between stimuli and emotionalresponses. The determinations of the SID module 20 are continuously updated through feedback from the detection intervention prevention module 22.

[0077] The DIP module 22 uses the insight into emotional state gained from the SID module 20 to determine appropriate real-time actions and monitors the effects through adaptive feedback loops. To do this, the DIP module 22 compares the magnitude of deviations in the individualized physiological baseline to personality calibrated low 0i and high 02 thresholds. If one or both of the personality calibrated thresholds is exceeded, the DIP module 22 can select, using a multimodal intervention engine, from behavioral, cognitive, nutritional, physiological, or environmental interventions based on contextual triggers, deviation magnitude, and user personality profiles or preference history. Examples of behavioral interventions can include but are not limited to guided breathing routines, movement prompts, and haptic pacing cues, a dietary recommendation, a recommended mindfulness practice, a recommended physical activity to restore emotional stability, clinician notifications, and / or an adaptive coping strategy recommended to a user based on user-specific historical data. Suggested behavioral interventions can be delivered through a user electronic device, or an electronic device connected to the sensor data processor module 16, and the intensity of the interventions prescribed can be scaled relative to the magnitude of deviation. Examples of cognitive interventions can include but are not limited real-time reframing prompts, reflective journaling cues, psychoeducational messages. Cognitive interventions can be prescribed when deviation patterns suggest cognitive overload or maladaptive thought loops. Environmental interventions can include but are not limited to light modulation, sound masking, and micro-break scheduling which may be prescribed based on environmental sensor data that indicates overstimulation or poor recovery conditions.

[0078] The DIP module 22 operates under a phase-based trauma support protocol that differentiates between pre-traumatic, peri-traumatic, and post-traumatic states by analyzing deviation persistence, event density, and physiological load. The differentiation of deviations across pre-, peri, and post-traumatic phases means that the system can be used as for supportive intervention and for the optimization of resilience and reduction of emotional load. During pre-traumatic states, the DIP module 22 can deliver preventative micro-interventions such as mindfulness prompts or breathing guidance when certain conditions are met such as when cumulative deviation frequency exceeds 3 occurrences within 72 hours. During peri-traumatic states, the DIP module 22 can activate support that reduces cognitive demand by simplifyinginterface prompts and extending the intervention duration to preserve autonomic stability.Specifically, extending the intervention duration preserves autonomic stability by providing sufficient time for the user’s autonomic response to transition from sympathetic activation toward parasympathetic recovery and to remain within a regulated range long enough to prevent rapid rebound. In some embodiments, the DIP module 22 maintains the intervention (e.g., paced breathing cadence, haptic pacing, or simplified prompts) until a recovery criterion is satisfied, such as (i) a reduction in arousal-related features, (ii) stabilization of variability-based features toward the individualized baseline band, or (iii) a minimum continuous stabilization interval. This reduces oscillatory re-triggering and supports closed-loop regulation rather than short, intermittent prompts that may be insufficient to resolve the deviation.

[0079] In the post-traumatic phase, the DIP module 22 monitors the recovery profile and can, for example, modulate threshold recalibration speed based on improvements in a resilience indicator (Rs) The resilience indicator (Rs) corresponds to the recovery half-time for a deviation event and is used by the DIP module 22 to personalize threshold recalibration speed and intervention intensity within bounded safety limits. The effects of the behavioral, cognitive, nutritional, or environmental actions prescribed by the DIP module 22 can be monitored by changes in physiological data as collected by the sensor data processor module 16 and analyzed by the sensor fusion module 18. The feedback collected can be used by the sensor fusion module 18 to tune thresholds enabling personalized preventative mental health support and allowing the inclusion of clinical oversight. The DIP module 22 can also compute a feedback-efficacy score (Fe) which is derived from metrics such as post-intervention recovery (tr), adherence (A), and residual deviation (AR). In some embodiments, post-intervention recovery time (tr) is determined as the elapsed time from intervention initiation (or deviation peak) to the earliest time at which one or more monitored features return within a recovery band relative to the individualized baseline model and remain within the band for at least a stabilization interval. The recovery band may be defined, for example, as within ±kc of baseline for the feature(s), where k is configurable. Adherence (A) is determined based on whether the user completed the prescribed intervention protocol and / or the degree of compliance with intervention parameters. Nonlimiting examples include (i) completion percentage of an intervention session, (ii) time-in-session relative to prescribed duration, (iii) adherence to a paced breathing cadence measured via sensor signals, or (iv) confirmation inputs indicating completion. Residual deviation (AR) isdetermined as the remaining normalized deviation magnitude after completion of the intervention, computed relative to the individualized baseline model. In some embodiments, AR is calculated as the difference between a post-intervention feature value and its baseline mean, normalized by baseline variability, and optionally aggregated across multiple features using a weighted combination. Lower residual deviation indicates a more effective intervention response.

[0080] The feedback-efficacy score (Fe) may be computed as a function that increases with lower trand lower AR and increases with higher adherence A, thereby prioritizing interventions that empirically resolve deviations more quickly and more completely for the user. The feedback efficacy score, for example, can be used to prioritize future interventions with higher empirical success for the individual user.

[0081] The sensor data processor module (AID) 16, the sensor fusion module (RID) 18, the stimulus identification module (SID) 20, and the detection intervention prevention (DIP) module 22 that comprise the ADPT system operate in a closed loop. Specifically, the sensor data processor module 16 captures data signals from a plurality of physiological 12 and environmental sensors 14. The sensor data processor module 16 validates and preprocesses the data captured in addition to collecting user provided information 36. The data and information collected by the sensor data processor module 16 is transmitted to the sensor fusion module 18. The sensor fusion module 18, with the SID module 20 analyzes patterns in the data collected and uses the information obtained to map triggers and quantify risk. The DIP module 22 prescribes interventions and monitors the outcomes of the prescribed interventions. The physiological effects of the interventions prescribed by the DIP module 22 are then captured by the physiological sensors 12 and / or environmental sensors 14 and received by the sensor data processor module 16, completing the cycle. Each cycle runs continuously with parameter refresh intervals of approximately one minute to one hour, depending on signal type and context. This ensures temporal resolution sufficient for emotion regulation without excess energy use.

[0082] In an example of the closed loop structure of the ADPT system, during a high-stress work interval of a user, the sensor data processor module 16 detected a 24% increase in electrodermal activity and a heart rate variability decrease of -1.4 c for a user. The sensor data processor module 16 also detected a noise level of +10 dB from a noise sensor. The sensor fusion module 18 interpreted the data collected by the sensor data processor module 16 and detected apattern of elevated arousal, and the SID module 20 associated the pattern of elevated arousal with environmental (noise) overload. Based on the causal association, the DIP module 22 initiated a guided breathing intervention that resulted in a heart rate variability that returned to 0.3 c within six minutes. The threshold 0i was automatically relaxed by 0.1 G to minimize false alerts. The threshold 0i can also be automatically relaxed following the successful intervention because the observed physiological recovery demonstrated effective regulation in response to the identified stimulus. Adaptive threshold relaxation reduces sensitivity to transient, contextspecific stressors that have been shown to resolve reliably with intervention, thereby minimizing repetitive triggering and false alerts in similar conditions. This adjustment preserves responsiveness to novel or sustained deviations while preventing unnecessary intervention escalation in environments where adaptive regulation has been empirically confirmed for the user.

[0083] Figure 2 shows a method for multimodal emotion detection and adaptive intervention. A plurality of physiological sensors can be used to collect initial physiological sensor data from a user 202. The data collected using the plurality of physiological sensors can be preprocessed and aggregated by a sensor data processor module which can also acquire and process behavioral and environmental data in addition to physiological data. The physiological, behavioral, and environmental data can include but is not limited to heart rate variability, electrodermal activity, skin temperature, sleep efficiency, movement, ambient noise, and light exposure. Each data signal is preferably validated with automatic recalibration performed with the detection of issues such as the presence of motion artifacts, sensor detachment, and environmental interference. Based on each sensor feature analyzed by the sensor data preprocessor module, an individualized physiological baseline for a particular user can be established 204. To compute the individualized physiological baseline, a mean (p) and a standard deviation (G) can be determined for each monitored physiological feature over one or more defined temporal window. Real time physiological data is regularly collected using the plurality of physiological sensors 206 and used to update the individualized physiological baseline.

[0084] Deviations in the individualized physiological baseline can be quantified 208 by the sensor fusion module which determines the normalized differences between incoming data and the established individualized physiological baseline to establish if the deviation exceeds a calibrated threshold 210. The deviation magnitude A can be expressed as: <where x is the value of the incoming data, p is the rolling mean value, and G is the standard deviation.

[0085] Each deviation can be mapped to a temporal context window that includes concurrent environmental and behavioral metadata to identify potential stimuli to the user. Causal strength scores that link each deviation to one or more internal or external stimuli can be assigned to generate a trigger profile. In addition, each deviation magnitude can be compared against a first threshold (0i) and a second threshold (62) to determine whether intervention is required. The deviation threshold from the user’s physiological baseline model can be dynamically determined from the baseline standard deviation (G) and can optionally incorporate a personality coefficient (K) which is derived from a stored personality profile for the user. If the deviation magnitude as calculated by the detection intervention prevention module exceeds both the first and second thresholds for a sustained duration, it is determined that clinician attention is required 212 and a clinician is contacted 218. An encrypted clinician review packet can be created containing deviation metrics, triggers and prior interventions. It the deviation exceeds the first threshold but does not exceed the second threshold, an appropriate self-regulation intervention can be selected from one of a plurality of interventions 214. The self-regulation intervention is determined by taking into account environmental data and user provided data. The interventions can include but are not limited to breathing guidance, posture adjustment, haptic cues, and environmental modification. The intervention recommended by the DIP module can be transmitted to the user using an electronic device or any other appropriate mechanism 216.

[0086] The effect of recommended interventions can be observed from changes in physiological data collected by the plurality of physiological sensors that results in changes in the physiological response relative to the physiological baseline model. The post-intervention recovery back to the physiological baseline model for the user can also be measured by calculating the metrics recovery time (tr), adherence (A), and residual deviation (AR) and transmitting these to a feedback controller of stimulus identification module to compare the magnitudes of deviations in the individualized baseline model to the operating threshold(s). The metrics can be used to update the first and second thresholds to produce personalized adaptation while maintaining safety bounds Safety bounds define constrained limits within which adaptivethreshold updates may occur to ensure physiological relevance, user safety, and regulatory robustness. In some embodiments, the safety bounds are expressed relative to an individualized baseline standard deviation and may be set, for example, between approximately ±1.5o and ±3. Os. The lower bound limits excessive sensitivity that could result in frequent false alerts or unnecessary intervention, while the upper bound prevents desensitization that could delay detection of meaningful emotional dysregulation. These bounds reflect empirically observed ranges of autonomic variability within which emotional responses remain physiologically interpretable across diverse users and conditions. The safety bounds act as guardrails that constrain adaptive personalization while preserving responsiveness to sustained or novel deviations. In alternative embodiments, the bounds may be configured per feature type, user profile, application context, or regulatory requirement, and are not limited to the example ranges described herein and to personalize the DIP module’s intervention library for the particular user. The process of multimodal emotion detection and adaptive intervention is a cycle that involves measuring an emotional state, managing the emotional state and adapting the ADPT system to changes. The cycle operates continuously and autonomously to maintain emotional stability and resilience and transforms emotional regulation from a reactive process into a measurable, preventative, and continuously optimized health parameter.

[0087] Due to its cyclic nature, the ADPT system which detects, interprets, manages, and stabilizes emotional state, can be said to operate as an emotion cycle framework (ECF). Unlike linear assessment tools that capture single-time emotional states, the ADPT An emotion cycle framework links sensing, regulation, feedback, and autonomy to provide a fulsome understanding of a user’s emotional state based on measured physiological and environmental data. The emotion cycle framework as set out herein comprises seven interconnected stages detailed in Table 2:

[0088] Table 2: Emotion cycle framework&

[0089] During the emotion patterns stage (1) of the emotion cycle framework, the sensor data processor module and the sensor fusion module jointly identify repeating physiological or behavioral rhythms that are indicative of emotional state change. The emotion recognition stage (2) involves the detection of deviation from baseline regularity by the sensor fusion module by comparing the deviation magnitude to the first and second thresholds. The emotion regulation stage (3) involves the determination of appropriate interventions by the DIP module in proportion to deviation magnitude and contextual trigger type. During the emotional quotient (EQ) assessment stage (4), the sensor fusion module and the DIP module compute a resilience indicator (Rs) which is based on recovery half time (tr). The resilience indicator is used as an indicator of future intervention sensitivity. The behavior assessment stage (5) involves analysis of behavioral changes such as changes in sleep efficiency, social engagement, and activity regularity by the sensor fusion module to detect the early trends that precede symptomatic onset. During the feedback loop (6), the DIP module evaluates intervention efficacy and user adherence using feedback weight coefficients. During the feedback loop (6), the detection interventionprevention (DIP) module evaluates intervention efficacy and user adherence using feedback weight coefficients. The feedback weight coefficients represent adjustable weighting factors assigned to post-intervention metrics, including recovery time (tr), adherence (A), and residual deviation (AR), to quantify the relative contribution of each metric to overall intervention effectiveness.

[0090] In some embodiments, higher weight may be assigned to metrics demonstrating faster recovery or lower residual deviation, while reduced weight may be assigned when adherence is incomplete or recovery is delayed. The feedback weight coefficients may be individualized per user and per intervention type, and may be updated iteratively based on empirical intervention outcomes observed over multiple events.

[0091] The feedback weight coefficients are used to bias subsequent intervention selection, threshold adjustment, and intervention timing, enabling the system to preferentially deploy interventions that have demonstrated higher effectiveness for the user while maintaining bounded adaptation to refine subsequent decisions. In the autonomy stage (7), the sensor fusion module and the DIP module reduce the frequence of intervention once user-initiated selfregulation success exceeds a predetermined level of adherence over a fixed period i.e. once selfsustained emotional stability is signaled.

[0092] In an operational example of the emotion cycle framework, which is provided for illustrative purposes and is not limiting, a university student used the ADPT system throughout a demanding exam period. In the first stage, during emotion pattern detection by the sensor data processor module and the sensor fusion module, a nightly rise of 18% in electrodermal activity after 11 pm was detected and a pattern was flagged. On day 10, the heart rate variability of the student dropped to 50 milliseconds (ms) (A = -1.3 c) and the sensor fusion module classified a stress onset event. As a result, during emotion regulation, the DIP module initiated a 2-minute guided breathing exercise and temporarily suppressed non-critical notifications to reduce cognitive load and improve intervention adherence.

[0093] During the emotional quotient (EQ) assessment stage, the system computed recovery half-time (tr) and updated a resilience indicator (Rs) derived from the recovery half-time. In addition, the recovery score was found to be 6 minutes and the EQ score or resilience indicator (Rs) The “EQ score” and the resilience indicator (Rs) are not necessarily the same value. In some embodiments, the resilience indicator (Rs) is a physiology-derived recovery metric (e.g., basedon recovery half-time tr), whereas the EQ score is a higher-level composite indicator that can incorporate resilience indicator inputs together with additional behavioral, contextual, or user-reported measures. In some implementations, the EQ score may include or weight Rs, but Rsremains a distinct technical parameter. EQ assessment outputs may include multiple indicators of regulation capacity, including but not limited to the resilience indicator (Rs); in some embodiments EQ is a composite construct while Rsis a specific quantitative recovery metric used within EQ assessment.

[0094] At the behavior assessment stage, the system detected a 15% decrease in sleep efficiency relative to the user’s baseline and reduced social activity. During the feedback loop stage, intervention adherence was measured at 95% and feedback policy weights were updated to increase the likelihood of earlier-in-evening micro-interventions (e.g., pre- 10 pm recovery breaks) under similar contextual conditions. After 10 days of system-guided interventions and feedback updates, the user self-initiated relaxation routines and system alert frequency decreased by 30%.

[0095] Figure 3 is a schematic diagram illustrating the development of an emotion pattern profile 26. A plurality of physiological sensors 12 (sensor 1, sensor 2, sensor 3) are used to collect physiological data from a user. The physiological data is received from the plurality of physiological sensors 12 and environmental sensors by the sensor data processor module 16 which performs various preprocessing actions on the sensor data collected. The preprocessing can include but is not limited to noise filtering, artifact labeling and time-weighted normalization of multimodal data, and the extraction of features from the multimodal data. For each of the physiological sensors 12, physiological feature data 30 such as heart rate variability (HRV) and electrodermal activity (EDA) can be extracted that is indicative of the emotional state of the user. The structured physiological feature data 30 preprocessed by the sensor data processor module 16 is transmitted to the sensor fusion module 18 which, for each extracted physiological feature, computes individualized baseline means (p) and standard deviations (c). Deviation magnitudes (A) for each extracted physiological feature are quantified as normalized differences from the individualized baselines. Using the quantified deviation magnitudes, an emotion pattern profile 26 is generated based on the regularity, intensity, and duration of deviations of multiple temporal windows that range from minutes to weeks.

[0096] In an example, the physiological feature data 30 is analysed for rolling mean, standard deviation, and other statistical measures In an example, the physiological feature data 30 is analysed for rolling mean, standard deviation, and other statistical measures including variance, median, interquartile range, coefficient of variation, skewness, kurtosis, trend slope, and rate-of-change metrics, to provide a statistical analysis of the physiological features which make up the emotion pattern profile to provide a statistical analysis of the physiological features which make up the emotion patten profile. The emotion pattern profile 26 of a user comprises a temporal stability vector which comprises a regularity index (Ri), an intensity coefficient (li), and a duration parameter (Di). The regularity index is a measure of frequency of the deviations from the individualized baseline and can be calculated by determining the mean recurrence interval of deviation events that exceed a pre-specified threshold. The intensity coefficient is average of the peak deviation magnitudes normalized to each feature’s baseline standard deviation (c). The intensity coefficient quantifies the intensity of physiological arousal. The duration parameter measures the continuous time during which a deviation magnitude remains above a specified amount above the baseline variance (for example, 0.75 times o). The emotion pattern profile 26 comprises the regularity index, intensity coefficient, and the duration parameter and takes the form of a three-dimensional vector that is also known as the temporal stability vector.

[0097] Figure 4 is a schematic diagram illustrating the generation of an individualized physiological baseline 32 with integration of a personality calibration engine 34. The generation of the individualized physiological baseline uses user provided information 36 and physiological data collected using a plurality of physiological sensors that has been preprocessed by a sensor data processor module 16. User provided information 36 is entered into a personality calibration engine 34 which computes a personality coefficient (K) which is derived from psychometric traits as determined from a psychological examination. In particular, user provided information 36 is entered into a psychological model 38 such as, for example, the five-factor model of personality, also referred to as the big five personality traits. Other validated assessments that can be used for calibration include but are not limited to the Big Five Inventory-2 (BFI-2) test, the Ten Item Personality Measure (TIPI), other clinician administered equivalents, and longitudinal inference from engagement behavior Other validated psychological or behavioral models that may be used for calibration include, but are not limited to, temperament-based models, attachment style models, affective style assessments, stress reactivity profiles, emotion regulation styleinventories, resilience or coping style assessments, and clinically validated trait or state questionnaires. In some embodiments, the personality calibration engine may also derive calibration parameters from longitudinal behavioral patterns, interaction dynamics, or engagement characteristics observed during system use, either independently or in combination with formal psychometric instruments to determine a dominant personality trait 40. The personality traits of the user modulate the personality coefficient (K).

[0098] In an example, the five-factor model of personality represents different traits of human personality (openness, conscientiousness, extraversion, agreeableness, and neuroticism) with each trait ranging in value from low to high. Determination of a dominant personality trait 40 can be used for the generation of a personality coefficient (K) that can be stored in the sensor fusion module 18 for threshold calibration. The personality coefficient (K) can be used to apply a calibration effect 42 to the operating threshold 44 and defines how tightly the operating threshold 44 should be positioned around the baseline band (p ± c). The effect of modulation of the individualized physiological baseline 32 based on of personality traits is illustrated in Table 3.

[0099] Table 3: Personality calibration

[0100] The operating threshold 44 can be adjusted by the calibration effect 42 according to the personality coefficient (K) such that higher K values which represent increased emotional sensitivity decrease the absolute magnitudes of the first and second threshold relative to the baseline c. The operating threshold 44 can also be modified based on situational context so that operating thresholds 44 can be increased during low-risk or low stress periods and decreased during high-load or high-stress intervals as detected by the sensor fusion module 18. Different mechanisms can be used to continuously update the monitoring of emotional states and ensure accuracy. In an example, the DIP module can be used to compute a dynamic tolerance band P which is defined by the following equation:where 0i is the first operational threshold, 02is the second operational threshold, and o is the baseline.

[0101] The dynamic tolerance band defines an adaptive range around an individualized physiological baseline within which fluctuations are considered non-actionable and outside of which deviations are classified as emotionally significant. The band is bounded by a first operational threshold (0i) and a second operational threshold (©2), representing escalating levels of deviation severity. The dynamic tolerance band is continuously adjusted to balance sensitivity and specificity by maintaining a target false-positive rate over a rolling observation period.Adjustment of the tolerance band allows the system to remain responsive to meaningful emotional deviations while preventing excessive alerts caused by normal physiological variability. For example, the target false positive rate may be below 10% across at least one week of monitoring. In another example, threshold adaptation could follow an incremental adjustment rule that allows gradual personalization with an abrupt sensitivity change. Updated first operating threshold (0i') and second operating threshold (02') can be measured by taken a previously determined first operating threshold (0i) and second operating threshold (02) and following an incremental adjustment rule, and can be calculated using the following equations:0 = 0i + a(A0i)and02' = 02+ a(A02)where a is a learning-rate parameter, 0i is the first operational threshold, 02is the second operational threshold

[0102] Threshold adaptation occurs through incremental updates that apply a learning-rate parameter (a) to observed deviation trends, enabling gradual personalization while preserving stability. The learning-rate parameter governs the speed at which thresholds adapt to sustained changes in baseline physiology without reacting to transient noise. Updated thresholds are computed as:0i' = 9i + a(A6i)62' = 62 + a(A02)where 0i and 02 are previously established operational thresholds, A0i and A02 represent adjustment terms derived from deviation statistics, and a constrains adaptation to prevent abrupt threshold deviation.

[0103] The learning-rate parameter (a) is a scaling factor that controls how quickly the ADPT system updates personalized parameters (including thresholds and coefficients) in response to new evidence. In particular, a governs the proportion of a computed adjustment term (A0) that is applied at each update step, thereby constraining adaptation so that thresholds evolve gradually rather than reacting abruptly to transient noise, artifacts, or short-lived context changes. In an example, a can be selected within a range such as 0.05 to 0.20, where lower values produce slower, more conservative personalization and higher values produce faster adaptation. This example range is not limiting, and a can be selected based on desired stability, sampling frequency, and signal quality. If a is set too high, thresholds may adapt too quickly, increasing the likelihood of instability (e.g., oscillatory threshold behavior) and susceptibility to false adjustments driven by short-term variability. If a is set too low, adaptation may lag behind sustained changes in user physiology, delaying personalization. Accordingly, the system may bound a and / or condition updates on data quality and context stability to maintain safe and stable operation. Personalized adaptations made by the ADPT system can be recorded in a personality adaptation log that records the personality coefficient (K), operational thresholds (0i, 02), and corresponding interventions. The contents of the log can be made available to a user electronic device, or to a clinician in the event that escalation or professional intervention is required.

[0104] In an example, the learning rate parameter can range between 0.05 and 0.2 The personalized adaptations made by the ADPT system can be recorded in a personality adaptation log that records coefficient (K), thresholds (0i, ©2), and corresponding interventions. The contents of the log can be made available to a user electronic device, or to a clinician in the event that escalation or professional intervention is required.

[0105] The incorporation of personality driven thresholds enables the individualized calibration of emotional state boundaries and reflect a user’s sensitivity, resilience and recovery characteristic. In an operational example of the incorporation of personality driven thresholds, when a deviation is detected, the sensor fusion module 18 computes the deviation magnitude Ai. The deviation magnitude is compared against the first and second thresholds with a low-alert intervention initiated when the deviation magnitude is above the first threshold and below thesecond threshold. When the deviation magnitude is below the second threshold for a sustained duration, a clinician-alert protocol is initiated. After each resolved episode, the system can update the thresholds using the weighted adaptation coefficient learning rate parameter a which measures the recovery efficiency and frequency of event. After each resolved episode, the system can update the thresholds using a weighted adaptation coefficient learning-rate parameter (a) that reflects both recovery efficiency and event recurrence.

[0106] In an example, a can be computed as a bounded function of recovery half-time (tr) and event frequency (fe), such that:a = clamp ( ao • (to / tr) (1 / (1 + fe)), amm, Umax )where:tris the measured recovery half-time of a deviation,to is a reference recovery time,feis the frequency of deviation events within a predefined observation window, ao is a nominal learning-rate constant, andclamp() constrains a between minimum and maximum allowable values.

[0107] In this manner, faster and more consistent recovery results in a higher effective a and gradual outward threshold adjustment, while slower recovery or frequent relapse reduces a and limits threshold expansion. The adjustments to the thresholds remain within predefined physiological safety bounds (±1.5 o < 62 < ±3.0 o) to maintain clinically conservative operation. These bounds are selected to remain within statistically significant but non-pathological ranges of individualized baseline variability, ensuring that adaptive personalization does not mask clinically relevant deviations or permit unsafe desensitization. Over time, thresholds gradually shift outward, indicating increased tolerance when recovery is consistency and rapid, or inward when relapse frequency increases. The adjustments to the thresholds remain within predefined safety bounds (±1.5 o < 62 < ±3.0 c) to maintain safety-constrained adaptive bounds derived from physiological baseline variance.

[0108] In an example of the ADPT system operation using personality adaptive thresholds, a user with moderate neuroticism and high conscientiousness used ADPT continuously for six weeks. Initially a baseline is established with a heart rate variability rolling mean p of 64 ms and a standard deviation c of 9 ms. Based on the personality calibration engine 34, the first thresholdwas set at -0.8 c or -7.2 ms and the second threshold was set at -1.6 c or -14.4 ms. On day 10, a low-alert event occurred that crossed the first threshold. The heart rate variability for this event was 57 ms meaning that the deviation magnitude was -0.8 c. Additionally, a 12 percent increase in electrodermal activity was detected. As a result of these deviations the DIP module initiated a 1 -minute breathing sequence which resulted in the heart rate variability decreasing to 62 ms within 5 minutes. For this event, no clinician alert was issued, and the system logged the event as resolved. On day 22, a high-alert event occurred with the heart rate variability decreased to 49 ms or a deviation magnitude of A = -1.7 c. Additionally, a 26% increase in electrodermal activity and a 15% decrease in sleep efficiency was observed. The deviation persisted for greater than 10 minutes resulting in the initiation of a clinician alert which was accompanied by an encrypted summary that was transmitted to a clinician dashboard. Due to adaptive updating, after 14 days of consistent recovery where the mean recovery trwas 6 minutes and relapse frequency decreased by 40%, the first and second thresholds were relaxed to -1.0 c and -1.8 c respectively. The relaxing of the thresholds resulted in the user experiencing fewer false alerts and maintaining sensitivity to meaningful deviations.

[0109] The example provided provides a quantitative example of the personality driven closed loop threshold ADPT system which balances early detection with personalization and clinical safety. The personalization at initialization results in thresholds that reflect each user’s psychophysiological tolerance and the dynamic adaptation provided by continuous feedback prevents alert fatigue and adjusts for resilience growth. Clinical safety is integrated through the mechanism of automatic escalation and ISO 14971 risk logging which enables regulatory traceability. Federated synchronization can be used to avoid sharing raw biometric data. The ADPT system supports early self-regulation and clinician collaboration without clinical diagnosis functions.

[0110] Figure 5 is a schematic diagram illustrating an embodiment of the adaptive digital prevention technology (ADPT) system. A plurality of physiological sensors 12 are used to collect the physiological and behavioral data of a user. The physiological sensors 12 can include but are not limited to ECG sensors, PPG sensors, thermistors, and electrodermal activity sensors. Environmental sensors 14 are used to collect information about the surroundings of a user.Sensors such noise and light sensors, thermometer sensors, humidity sensors can be used to collect data about a user’s environment. Additional information about a user can be gathered toprovide additional context to the physiological and environmental data collected. The user provided information 36 can include but is not limited to previous medical history, personality profiles, personal calendars, workplace information. The user provided information 36 can be inputted through a variety of mechanisms including manually by the user, through electronic transfer, by querying the user, and through an authorized clinician. The data collected through the physiological sensors 12 and the environmental sensors 14 is collected by the sensor data processor module 16 which validates and preprocesses the data. Preprocessing by the sensor data processor module 16 can include motion-artifact labelling and adaptive filtering. One of the functions of the sensor data processor module 16 is to extract features from the physiological data which can be used as indicators of emotional state. The structured data that results from the preprocessing done by the sensor data processor module 16 is transferred to the sensor fusion module 18.

[0111] The sensor fusion module 18 calculates a rolling mean (p) and standard deviation (G) for each physiological feature in multiple temporal windows ranging in scale from minutes to weeks to create an individualized baseline 30. Baseline deviations 28 which represent changes in the individualized baseline are quantified and used by the sensor fusion module 18 to generate an emotion pattern profile with different emotion pattern profiles associated with different emotional states. The stimulus identification module 20 maps emotional states flagged by the sensor fusion module 18 onto contextual triggers extracted from environmental data gathered by environmental sensors 14 and user provided information 36. The SID module 20 correlates deviations with time-locked contextual variables such as environmental noise, illumination, and behavioral activity. A causal strength score can be assigned to each correlated stimulus to produce a dynamic trigger profile that can identify the causes of emotional deviation. The determinations of the SID module 20 are used inform the interventions prescribed by the DIP module 22. The detection intervention prevention module 22 compares the magnitudes of deviations in the individualized baseline 30 model to personality calibrated low and high thresholds. The thresholds are defined relative to the user’s individual baseline standard deviation (G) and personality coefficient (K). During low-alert conditions, the magnitudes of the baseline deviations 28 exceed the low threshold but do not exceed the high threshold. Low-alert conditions trigger the prescription of self-regulated micro-interventions based on contextual triggers, deviation magnitude, and user personality profiles. The micro-interventions can includebut are not limited to breathing guidance, posture cue, and sensory modulation. When the baseline deviations 28 exceed both the low and high thresholds, there is a high-alert condition.

[0112] When a high-alert condition persist beyond a specified time or occur more than a predetermined number of times within a review window, a clinician 46 escalation can be initiated. The clinician 46 escalation can include the transfer of a summary packet. The summary packet can comprise a series time-stamped deviation magnitude values for key signals such as heart rate variability, electrodermal activity, and sleep efficiency. The summary packet can also comprise the details regarding the interventions attempted and the success metrics, contextual triggers as identified by the stimulus identification module 20, and the personality modulated thresholds active at each deviation event. The case summary can be transmitted to a clinician 46 dashboard interface that displays for example deviation patterns, adherence metrics, and recovery profiles in real time. Such a clinician 46 dashboard can ensure that all personally identifiable data is maintained in encrypted form. The clinician 46 dashboard can incorporate clinician feedback by enabling authorized clinicians 46 to append interpretive notes or recommendations that can be used for adaptive recalibration. The transfer of the summary packet may involve encryption, and the transfer can take place through various mechanisms including through a federated synchronization protocol. The escalation can act as a supportive preventative measure to facilitate timely care coordination within stepped care frameworks. A regulatory audit package can be included which contains event logs, threshold updates, and clinician interactions with each entry time-stamped, hashed, and digitally signed.

[0113] The ADPT system can also determine a risk priority index (RPI) for each logged event. The risk priority index is a function of the severity, probability, and detectability of each event and based on the risk priority index, the ADPT system alert threshold can be automatically adjusted when cumulative RPI exceeds a predefined safety boundary The risk priority index (RPI) is a composite risk score computed for each logged event as a function of severity (S), probability (P), and detectability (D), where:Severity (S) represents the magnitude and persistence of the detected deviation relative to individualized thresholds,Probability (P) represents the likelihood of recurrence based on recent event frequency and recovery dynamics, andDetectability (D) represents the system’s confidence in reliable detection based on signal quality and cross-modal agreement.

[0114] In an example, the risk priority index can be computed as:RPI = S x P x Dwhere each component is normalized within a bounded range to ensure comparability across users and signal modalities.

[0115] The cumulative RPI is calculated over a predefined review window by aggregating individual event RPIs. When the cumulative RPI exceeds a predefined safety boundary, the ADPT system automatically modifies alert handling behavior, including one or more of• elevating the alert tier or reducing alert suppression,• inhibiting further downward threshold relaxation,• triggering clinician escalation workflows, and• prioritizing clinician review within the dashboard interface.

[0116] In this manner, exceeding the safety boundary acts as a protective control that shifts the system from autonomous adaptation toward supervised oversight, ensuring that personalization does not obscure clinically relevant risk patterns. The ADPT system thereby performs continuous closed loop monitoring and eliminates the need for static assessments. At each phase, pre-, peri-, and post- traumatic, phase specific intervention logic is available.

[0117] Figure 6 illustrates a computing system 100 in accordance with some embodiments described herein. The computing system 100 may be configured to implement the ADPT systems and methods described in accordance with some embodiments herein. The computing system 100 comprises a processor 100, a memory 120, a storage device 130, input / output devices 140, and a bus 150. The processor 100, the memory 120, the storage device 130, and the input / output devices 140 may be connected via the bus 150. The memory 120 may store instructions which, when executed by the processor 100, cause operations that comprise the calibration of an intervention responsive to an emotional state, as described herein. System 100 may further include an operating system, a hypervisor, and / or other resources, to provide virtualize physical resources (e.g., via virtual machines). In some implementations, the processor 110 may be asingle-threaded processor. In alternate implementations, the processor 110 may be a multithreaded processor.

[0118] The processor 110 may be further configured to process instructions stored in the memory 120 or on the storage device 130, including receiving or sending information through the input / output device 140. The memory 120 may store information within the system 100. In some implementations, the memory 120 may be a computer-readable medium. In alternate implementations, the memory 120 may be a volatile memory unit. In yet some implementations, the memory 120 may be a non-volatile memory unit. The storage device 130 may be capable of providing mass storage for the system 100. In some implementations, the storage device 130 may be a computer-readable medium. In alternate implementations, the storage device 130 may be a floppy disk device, a hard disk device, an optical disk device, a tape device, non-volatile solid state memory, or any other type of storage device. The input / output device 140 may be configured to provide input / output operations for the system 100. In some implementations, the input / output device 140 may include a keyboard and / or pointing device. In alternate implementations, the input / output device 140 may include a display unit for displaying graphical user interfaces. In some implementations, the input / output device 140 may comprise a laptop, a tablet, a smartphone or a wearable.

[0119] The ADPT systems and methods described in accordance with some embodiments herein may comprise a modular architecture. For example, the ADPT systems and methods described herein may utilize models, modules, and / or applications to provide improved mental health treatment. The modular architecture ensures scalability, adaptability, and seamless integration with future advancements in Al, machine learning, and wearable technologies. The ADPT systems and methods described herein are configured to continuously monitor and adapt emotion recognition, emotion regulation, medication adherence, nutritional interventions, treatment efficacy, and personality-driven recommendations in real-time; and to optimize personalized interventions based on changes in the user's emotional state, personality, medication adherence, physiological responses, and nutritional intake, ensuring dynamic adjustments in both emotional regulation, therapy accuracy, and overall wellness outcomes, automatically adjusting interventions when preset personality-driven thresholds for emotional or physiological states are exceeded, thus enhancing treatment outcomes.

[0120] In some configurations, the processor 110 of the computing system 100 may comprise a plurality of modular components, including an autonomous intelligent device (AID) or sensor data processor module; a regularity, intensity, duration (RID) module; a stimulus identification and deviation (SID) module; and a detection, intervention, and prevention (DIP) module. The AID module may be configured to collect multimodal data (including ECG, EDA, PPG signals), behavioral data, and environmental inputs. In other words, the AID module may comprise a data hub. The multimodal data may further comprise heart rate variability (HRV), sleep patterns, activity levels, environmental conditions, personality data, and medication adherence data, and wherein the system tracks medication adherence, treatment efficacy, and personality traits over time. The AID module may also be configured to preprocess the collected multimodal data. In some implementations, the AID module may be configured to provide haptic feedback to a user, e.g., via the input / output devices 140. Such haptic feedback may be configured to create distractions during acute stress events and support real-time stabilization of a user’s emotional state. In this way, the ADPT systems and methods described herein use sensory interventions to regulate emotional states and enhance focus during high-stress scenarios. The AID module may be configured to include low-edge computing capabilities. Such capabilities may allow the AID module to process data locally. By processing data locally, the AID module is ensured to be able to function offline and in low-resource environments. This can help to enhance the privacy of a user in low resource environments. Further, by providing for offline functionality, access to the ADPT systems and methods described in accordance with embodiments herein can be made av for underserved or low-connectivity regions, ensuring equitable access.

[0121] The ADPT systems and methods described herein may acquire a plurality of multimodal data and may determined, based on the multimodal data, a first emotional state of a user of the system. The ADPT systems and methods described herein may further calibrate an intervention responsive to the emotional state based on the multimodal data. The multimodal data may comprise, for example, at least one of a physiological signal, a behavioral signal, and an environmental signal. The physiological signal may comprise, for example, at least one of an ECG signal, an EDA signal, and a PPG signal.

[0122] In certain implementations, the RID module may be configured to detect emotional patterns of a user and to establish individualized baselines for the user. In other words, the RID module may comprise a pattern recognition module. The RID module may be configured torecalibrate baselines dynamically based on longitudinal data (e.g., data collected over time). Longitudinal data may include, for example, HRV trends and emotional stability scores.Longitudinal data analysis predicts mental health challenges and prescribes tailored interventions before conditions escalate. The RID module may be configured to build baselines for emotion patterns and medication adherence, including tracking trends in regularity, intensity, and duration of emotional responses, and identifying deviations that may indicate a need for medication adjustments or changes in emotional state. The RID module may be further configured to identify emotion recognition and detect deviations from established emotional baselines and medication adherence, providing feedback on treatment efficacy of ongoing interventions, while incorporating personality traits to tailor emotional and medication interventions.

[0123] In certain implementations, SID module may be configured to map emotional triggers to actionable interventions. The SID module may be configured to account for overlapping or ambiguous triggers using contextual relevance and historical data, e.g., by mapping contextual data to emotional triggers. The SID module may further be configured to map emotional triggers to emotion response patterns and medication adherence data, and to infer emotional states and treatment efficacy based on the emotion response patterns. The SID module may also be configured to refine emotion patterns and medication responses based on detected deviations from external stimuli (e.g., stressful events) and user behavior, enabling dynamic adaptation of interventions and improving treatment efficacy over time.

[0124] In some implementations, the AID module is configured to respond to specific requests from the RID module and the SID module for targeted data or sensor insights. By responding to the specific requested from the RID and SID modules, the AID module can inform the RID and SID modules in their real-time emotional state inference processes. Further, the communication between the AID, RID, and SID modules enables the ADPT system to adjust dynamically collect multimodal data to meet evolving sensor strengths or limitations. The AID module may be further configured to annotate the multimodal data with sensor metadata including sensor type and quality. In some implementations, the DIP module may be configured to deliver real-time interventions and predictive alerts. The DIP module may comprise a personalized intervention module. The DIP module may be configured to deliver personalized and actionable interventions, predictive alerts, and professional guidance when thresholds are crossed. In some implementations, preprocessed data flows into the RID module and the SID module for analysis.Outputs from the RID module and from the SID module may be sent to the DIP module for tailored interventions. The DIP module may be configured to integrate GPS-based contextual data to correlate emotional triggers with location-specific stressors. By determining locationspecific stressors, the ADPT systems and methods described herein can help provide mental health treatment that is uniquely tailored to the users of the systems.

[0125] In some implementations, the DIP module is configured to provide real-time treatmentsupport prompts and / or clinician-configured suggestions, emotion regulation interventions, and nutrition guidance, based on continuous feedback from the AID module (e.g., the data hub), the RID module (e.g., the pattern recognition module), and the SID module (e.g., the stimulus identification module). In some implementations, the DIP module may be further configured to adapt therapy interventions, medication recommendations, and nutritional interventions in realtime, ensuring dynamic adjustment of emotional progression, therapy accuracy, behavior, and overall wellness (including through tailored nutritional recommendations). The ADPT systems and methods described herein may be configured to provide improved mental health treatment and recommendations based on a cyclical framework, as shown in Table 4 below.

[0126] Table 4: ADPT Treatment Cascade>>>>>> & <>>

[0127] The cyclical framework employed by the ADPT systems and methods described herein guides users from maladaptive responses to adaptive states through structured emotional progression. In some implementations, the cyclical framework may comprise the detection of an emotional pattern, recognition of an emotional state, regulation of an emotional state, determination of an emotional quotient, suggestion of a behavior, and implementation of a feedback loop. As users progress through emotional stages, resilience-building practices, such as adaptive cognitive exercises, guide them toward sustained autonomy. For example, a user in a maladaptive phase may receive immediate grounding prompts, such as guided breathing exercises. As the user transitions to resilience-building, tailored journaling exercises are suggested based on their historical emotional patterns. Users of the ADPT systems and methodsdescribed herein thus become able to independently manage their mental health with tools that build resilience and promote long-term emotional stability.

[0128] In some implementations, each stage of the emotion cycle framework is linked to at least one of the AID module (e.g., the data hub), the RID module (e.g., the pattern recognition module), the SID module (e.g., the stimulus identification module), and the DIP module (e.g., the personalized intervention module). In some implementations, the system tracks treatment efficacy, therapy accuracy, and adapts over time by monitoring changes in emotional stability, medication adherence, and user feedback, adjusting interventions when specific personality-driven thresholds for emotional state, medication adherence, and emotion regulation are exceeded. After implementing a feedback loop, the user of the system may be enabled to autonomously follow suggested behaviors or interventions, which translates patterns into actionable strategies. The feedback loop may comprise, for example, a series of steps including data collection, pattern recognition, intervention delivery, and user feedback.

[0129] ADPT ensures scalability through offline functionality and multilingual adaptability, supporting clinical and non-clinical settings. Real-world applications include workplace wellness, telemedicine integration, and resilience support in low-resource environments. The ADPT systems and methods described herein may be configured to determine an emotional state based on multimodal data by analyzing the multimodal data via a progression of stages. For example, the ADPT systems and methods described herein may determine an emotional state by acquiring and analyzing emotional patterns through screening. The ADPT systems and methods described herein may analyze recurring emotional patterns, focusing on their regularity, intensity, and duration. This analysis establishes a baseline for understanding a user’s emotional state. Screening at this stage detects early warning signs of mental health challenges, such as anxiety, depression, or PTSD. The continuous analysis ensures potential issues are identified during presymptomatic phases.

[0130] The ADPT systems and methods described herein may then leverage the multimodal data to recognize an emotion of the user. The system identifies specific emotions in real time. It maps these emotions to triggers, such as environmental factors (e.g., noise, light), behavioral shifts, or physiological changes. This step provides the foundation for understanding emotional imbalances and detecting stressors dynamically. After recognizing an emotion of the user, according to the emotion cycle framework, the ADPT systems and methods described hereinmay offer real-time coping strategies to help individuals regulate their emotional responses. These strategies may include mindfulness exercises, cognitive reframing techniques, or nutritional recommendations based on physiological data. By regulating a user’s emotion, emotional balance is maintained, and minor disruptions are prevented from escalating into significant challenges. After regulating an emotion of a user, the ADPT systems and methods described herein may be configured to assign or determine an emotional quotient (EQ) of the user. Emotional intelligence is assessed over time, measuring the individual’s ability to perceive, understand, and manage emotions effectively.

[0131] A higher EQ indicates better emotional health and resilience, while challenges in EQ signal the need for deeper intervention or additional support. After determining the EQ of a user, the ADPT systems and methods described herein may be configured to analyze or assess user behavior. At this stage, the system performs a comprehensive assessment using clinical benchmarks, such as PHQ-9, GAD-7, or PCL-5, to evaluate the presence or progression of mental health conditions. Emotional states influence behavior, such as sleep patterns, productivity, or social interactions. The system observes these behavioral impacts to deepen its understanding of the user’s mental health.After analyzing and assessing user behavior, the ADPT systems and methods described herein may be configured to apply a feedback loop. Insights from assessments are fed back into the system, refining its recommendations and interventions. This continuous learning via a feedback loop ensures that future support is more tailored and precise. Prevention strategies are developed based on evolving emotional patterns, aiming to mitigate future mental health crises.

[0132] The ADPT systems and methods described herein may be configured to perform such a progression of stages autonomously. The system functions independently, providing ongoing support with minimal human intervention. It detects emotional changes, screens for risks, intervenes with personalized strategies, and ensures long-term emotional balance. The intervention responsive to the emotional state that is recalibrated by The ADPT systems and methods described herein may be recalibrated using an iterative feedback loop. The iterative feedback loop may be informed by at least one of historical data and real-time data. The iterative feedback loop may comprise a machine learning model configured to predict an intervention strategy. The machine learning model of the iterative feedback loop may be configured to predict an intervention strategy based on at least one of user-specific behavioral patterns andphysiological markers. In some implementations, the iterative feedback loop uses the machine learning model to refine the predicted intervention strategy based on the user-specific behavioral patterns and / or the physiological markers. In further implementations, feedback loops dynamically adjust interventions using machine learning models trained on longitudinal data and user engagement metrics. The system’s real-time feedback loops allow for timely and stage-appropriate care. For instance, adaptive responses like resilience-building exercises are reinforced, while maladaptive responses trigger immediate stabilization techniques.

[0133] As shown in Table 5 below, the ADPT systems and methods described herein cure deficiencies in traditional emotional theories, which may provide valuable insights into the origins of emotions while lacking the tools to address how emotions are processed, regulated, and resolved in real time, particularly in dynamic, high-stress environments. This gap leaves individuals without the means to proactively manage their mental health and prevent emotional disturbances from escalating. The ADPT systems and methods described herein, via the use of the emotion cycle framework, bridges these gaps to provide effective mental health treatment.

[0134] Table 5: Traditional Emotional Theories

[0135] The ADPT systems and methods described herein may incorporate demographicspecific baselines and may recalibrate these baselines for habitual behavior tracking. By recalibrating these baselines, The ADPT systems and methods described herein may provide for population-level adaptability. The ADPT systems and methods described herein may be configured to compare a received input (e.g., multimodal data) to a threshold. Based on a result of the comparison between the input and the threshold, The ADPT systems and methods described herein may be configured to identify and / or recommend that a user of the system should seek professional consultation. Such a recommendation may be based on real-time data patterns and validated screening tools.

[0136] The ADPT systems and methods described in accordance with some embodiments herein may recognize emotion and propose an intervention to an emotion. The ADPT systems and methods herein may be configured to recognize or infer an emotion or an emotional state including, for example, valence, arousal, and dominance. The ADPT systems and methods described herein may be configured to recognize or infer an emotion or an emotional state using multimodal data inputs. The ADPT systems and methods described herein may be configured to correlate autonomic nervous system (ANS) markers such as HRV and EDA with emotional states. The ADPT systems and methods described herein may be configured to map neurotransmitter activity (e.g., serotonin and dopamine) using physiological data. By correlating physiological markers with neurotransmitter activity, the ADPT systems and methods described herein may be configured to deliver tailored interventions based on detected emotional states and inferred neurotransmitter imbalances. For example, dietary recommendations and mindfulness practices suggested by the ADPT systems and methods may be tailored to serotonin and dopamine levels. The ADPT systems and methods described herein may be configured to validate neurotransmitter activity inferences through user-reported outcomes and behavioral patterns.

[0137] The systems and methods described herein may integrate validated mental health screening tools with real-time emotional data. The ADPT systems and methods described herein may be configured to map user or user data to validated tools including the generalized anxiety disorder-7 (GAD-7) scale, the patient health questionnaire-9 (PHQ-9), and the posttraumatic stress disorder checklist-5 (PCL-5). By mapping user data to validated tools, the ADPT systemsimprove diagnostic accuracy. For example, elevated HRV and EDA markers mapped to PHQ-9 scores can help identify early signs of depression, enabling timely intervention.

[0138] The system identifies correlations between elevated stress markers and depressive symptoms, providing actionable insights to the clinician. Personalized recommendations, such as lifestyle adjustments and therapy options, are shared with the user. A significant drop in HRV combined with elevated EDA levels may indicate a transition from acute stress to resiliencebuilding phases of trauma response. By monitoring this transition, the ADPT systems may be configured to prompt a user with guided mindful exercises or clinician alerts and in this way enable timely intervention as the emotional state of a user changes. As another example, the ADPT systems and methods described herein may flag deviations linked to depressive episodes during a virtual consultation. Responsive to these deviations, the ADPT systems and methods described herein may suggests evidence-based interventions, such as CBT exercises or mindfulness strategies.

[0139] In some implementations, an algorithm may be used to improve the sensitivity and specificity of screening tools through the integration of real-time physiological data. In certain implementations, The ADPT systems and methods described herein may be configured to generate automated clinician alerts for high-risk emotional states. The automated clinician alerts may be enriched with contextual information to guide decision-making. In further implementations, thresholds and patterns identified by the screening tools of The ADPT systems and methods herein may be analyzed to recommend professional consultations when emotional dysregulation is detected.

[0140] The ADPT systems and methods described herein may be configured to provide management of relapse across the mental health continuum. For example, The ADPT systems and methods described herein may be configured to identify early markers of emotional dysregulation using multimodal inputs. The multimodal inputs may comprise, for example, physiological markers such as elevated EDA and reduced HRV (e.g., over several hours or over several days), as well as erratic sleep patterns. In response to the detection of a marker of emotional dysregulation (e.g., a pattern in HRV or other multimodal data indicative of emotional dysregulation), the ADPT systems and methods described herein may suggest branching pathways for interventions. Such branching pathways may comprise, for example, grounding exercises, mindfulness recommendations, and clinician alerts. This may include guidedmindfulness exercises and personalized clinician recommendations. The ADPT systems and methods described herein may be configured to incorporate user feedback to improve future relapse predictions.

[0141] The ADPT systems and methods described herein may be further configured to deliver proactive interventions such as guided exercises, alerts, or clinical consultations. The ADPT systems and methods described herein may be configured to track user adherence to an intervention and user responses to an intervention in order to refine future relapse prevention strategies. The ADPT systems and methods described herein may be configured to manage relapse by incorporating real-time recommendations for clinical intervention based on recurring patterns of emotional dysregulation.

[0142] The ADPT systems and methods described in accordance with some embodiments herein may comprise a scalable mental health platform. The mental health platform may be configured to be compatible with a plurality of clients (e.g., input / output devices), including, for example, smartphones, wearables, and other internet-of-things (loT) devices. In some implementations, the scalable mental health platform may comprise offline functionality supported by edge computing capabilities. The scalable mental health platform provided by the ADPT systems and methods described herein may further comprise local data storage functionality. Such functionality ensures intervention delivery in low-resource settings and is supported by periodic synchronization when connectivity is restored. In certain implementations, the scalable mental health platform may comprise multilingual interfaces and culturally localized interventions. For example, culturally localized interventions may comprise localized relaxation techniques, tailored mindfulness exercises, and community-specific engagement strategies.

[0143] The ADPT systems and methods described in accordance with some embodiments herein may comprise a system for supporting pharmaceutical research. The system for supporting pharmaceutical research may be configured to, for example, aggregate anonymized data trends to analyze population-level responses to mental health treatments. The system for supporting pharmaceutical research may be configured to, for example, correlate emotional stability metrics with therapeutic efficacy.

[0144] The system for supporting pharmaceutical research may be configured to deliver insights for optimizing clinical trial protocols and drug development. For example, the ADPT systems and methods described herein may be configured to collect multimodal physiologicaland behavioral data during therapeutic interventions, providing pharmaceutical companies with actionable insights into drug efficacy and side effects. In particular, the ADPT systems and methods described herein may be configured to identify early indicators of therapeutic efficacy, enabling adaptive trial protocols, improving user stratification, and optimizing dosing strategies. Further, the ADPT systems and methods described herein may aggregate anonymized data to facilitate analysis of differential drug responses across demographics including age, gender, and socioeconomic status.

[0145] In some implementations, real-time emotional and physiological markers are used to validate dosage strategies and identify side-effect profiles because emotional stability metrics are correlated with medication efficacy. Thus, refining dosing strategies and identifying side effect profiles based on multimodal data can improve treatment. In certain implementations, feedback loops for pharmaceutical companies may be configured to query anonymized datasets and validate drug efficacy hypotheses.

[0146] The ADPT system described herein is configured to dynamically track and adapt to fluctuations across the continuum, ensuring personalized care. For instance, historical emotional patterns combined with real-time stress markers allow tailored interventions, including immediate grounding techniques for acute conditions, resilience-building exercises for long-term emotional stability (e.g., based on a cognitive behavioral therapy (CBT) approach), or clinician alerts when thresholds indicate the need for professional involvement. This integration enables the system to recalibrate feedback loops iteratively, providing precision in interventions and measurable outcomes, such as reduced emotional dysregulation, improved resilience scores, and timely professional care.

[0147] The ADPT systems described herein may be configured for continuous and adaptive monitoring of a user. For example, the systems may track emotional fluctuations across presymptomatic, mild, and acute stages using multimodal inputs. The systems described in accordance with some embodiments here may provide real-time interventions dynamically adjusted to the user’s emotional state and environment. The systems described herein may incorporate thresholds to determine when users may benefit from professional consultation, empowering timely and appropriate action. For example, early detection of elevated stress markers can trigger grounding exercises to be recommended by the systems, while chronic trends may prompt mindfulness practices or professional consultations based on validated screeningtools like PHQ-9 or GAD-7. For example, mild emotional dysregulation may trigger mindfulness prompts, while acute stress events activate stabilization techniques like guided breathing.

[0148] The systems may comprise multilingual and culturally adaptive frameworks to address global mental health challenges. The ADPT systems and methods may address global mental health strategies by, for example, suggesting or incorporating region-specific or populationspecific coping strategies. For example, cultural relevance may be integrated through regionspecific practices. In some implementations, yoga-based breathwork may be suggested for South Asian users, and guided forest therapy may be suggested for East Asian populations.

[0149] In an example, in a corporate environment, an employee experiences increasing stress due to tight deadlines. The ADPT systems and methods described herein may offer guided mindfulness sessions to urban professionals. The ADPT systems and methods described herein may be configured to identify workplace stress patterns and to subsequently offer resiliencebuilding exercises for workplace programs. The emotional cyclical framework employed by the ADPT systems and methods described herein guides the user from maladaptive stress responses to adaptive coping mechanisms, fostering long-term emotional resilience. For example, the ADPT system continuously monitors physiological markers, such as elevated EDA and decreased HRV, to detect early signs of emotional dysregulation. If emotional dysregulation is detected in an employee, the system delivers real-time insights, recommending mindfulness exercises and scheduling short breaks. Longitudinal data shows improved resilience scores and reduced emotional fluctuations, indicating the system’s effectiveness in managing presymptomatic mental health challenges. Workplace resilience programs as offered by the ADPT systems and methods described herein lead to a 30% improvement in productivity and a 15% reduction in absenteeism. Further, early identification and management of mental health conditions reduce reliance on acute care services and thus significantly reduce healthcare costs.

[0150] The ADPT systems and methods described herein may, in accordance with some embodiments, support telemedicine workflows and integration with electronic health records (EHRs) to facilitate clinician-driven interventions. For instance, the systems and methods described herein may be configured to provide real-time interventions during virtual consultations. Real-time emotional deviations can alert clinicians, providing contextually relevant data and comprehensive mental health profiles for rapid decision-making and actionable personalized care plans. In some implementations, The ADPT systems and methods describedherein offer threshold-based guidance for professional intervention. For example, The ADPT systems and methods described herein can define and monitor thresholds for when users should seek professional help, bridging the gap between self-management and clinical care. The ADPT systems and methods described herein may combine real-time data patterns with validated screening tools to help a user seek professional help. In response to real-time data (e.g., multimodal data) describing a user’s emotional state indicating that a user has crossed a threshold for when the user should seek professional help, the systems may provide an actionable alert to the user. Such an actionable alert may tell the user, for example, “You may benefit from speaking with a mental health professional. Here’s a list of recommended actions and nearby clinicians.” This real-time guidance bridges the gap between self-management and professional care, empowering users to take timely and informed actions.

[0151] In some implementations, The ADPT systems and methods described herein provide neuroadaptive insights to clinicians and patients (e.g., users). The ADPT systems and methods described herein may be configured to infer automatic nervous system (ANS) activity and neurotransmitter imbalances, such as serotonin and dopamine levels, from multimodal data (e.g., HRV, EDA data). The ADPT systems and methods described herein may, based on the inferred ANS activity and neurotransmitter imbalances, be configured to recognize an emotion of a patient. Based on the ANS activity, neurotransmitter imbalances, and recognized emotion, the ADPT systems and methods may be configured to recommend targeted interventions. For example, such targeted interventions may comprise suggested dietary adjustments, guided mindfulness sessions, and tailored physical activities. In some implementations, the targeted interventions may be validated through engagement metrics and physiological feedback.

[0152] The ADPT systems and methods described herein are configured to protect user data and offer privacy. To offer such privacy, the ADPT systems and methods described herein may be configured to implement encryption and anonymization of user data. The ADPT systems and methods described herein may be configured to be in compliance with global data privacy standards such as GDPR, HIPAA and the Personal Information Protection and Electronic Documents Act (PIPED A). By remaining in compliance with such global privacy standards, The ADPT systems and methods described herein protect user data and ensure ethical use. The ADPT systems and methods described herein may be configured to achieve user privacy by employing anonymization of user data, and decentralized processing techniques, such asfederated learning. Data anonymization protocols allow clinicians to access aggregated insights without compromising individual user privacy. The ADPT systems and methods described herein are configured to align with public health and policy goals. For example, the ADPT systems and methods described herein support public health goals by improving access to mental health care in underserved regions and reducing the burden on acute care systems. As another example, public health initiatives leverage ADPT to track emotional well-being trends across communities, enabling targeted interventions and improved population-level mental health outcomes. Aggregated data trends collected by the ADPT systems and methods described herein may inform public health policies by highlighting key emotional and behavioral markers linked to mental health challenges. Further, systems and methods described herein may be configured to align with regulatory standards and global mental health strategies, such as World Health Organization (WHO) guidelines, to ensure widespread applicability and compliance. Public

[0153] The ADPT systems and methods described herein demonstrate quantifiable benefits, such as a reduction in emotional crises by up to 20% and increased adherence to therapeutic interventions. Further, the systems and methods described herein offer a projected 30% reduction in relapse events and improved longitudinal resilience scores validate the system’s efficacy in clinical and non-clinical settings. As described, adaptive feedback loops dynamically refine intervention strategies, achieving up to a 15% improvement in identifying early signs of emotional dysregulation. The ADPT systems and methods described herein may provide a 25% improvement in adherence to therapeutic strategies.

[0154] In some implementations, the ADPT systems and methods described herein may be configured to leverage machine learning to improve emotion detection and subsequently suggested interventions and treatments. For example, in some implementations, the ADPT systems and methods described herein may comprise a neural network. The neural network may be configured to improve the accuracy of emotion detection offered by the ADPT systems and methods. In some implementations, the inclusion of a machine learning model, such as a neural network, may be configured to improve the accuracy of emotion detection by 15%. The ADPT systems and methods described herein may be configured to provide actionable insights to pharmaceutical companies for optimizing mental health treatments and clinical trial designs. As described, the ADPT systems and methods may be configured to collect multimodal physiological and behavioral data to analyze population-level responses to therapeuticinterventions. The ADPT systems and methods described herein may be configured to aggregate anonymized data trends, such as HRV and EDA responses during drug trials, helping pharmaceutical companies refine dosing strategies and identify differential demographic responses.

[0155] The ADPT systems and methods described herein may be configured to track longitudinal data (e.g., track data over time) and predict future risks based on the collected longitudinal data. Such longitudinal data may comprise, for example, HRV trends and emotional stability scores. The ADPT systems and methods described herein may be configured to graph or otherwise display longitudinal data points. From the graphs, the ADPT systems and methods described herein may be configured to annotate key insights, including predictive markers for emotional dysregulation. The key insights may be linked to proactive interventions that may be suggested to a user of the ADPT systems based on the longitudinal data.

[0156] Figure 7 illustrates a process flow for calibrating an intervention responsive to an emotional state in accordance with some embodiments described herein. The process shown may be implemented by a computing system and may be used to calibrate an intervention responsive to an emotional stage based on multimodal data. At 201 of the process, there is acquired a plurality of real-time multimodal data describing an emotional state. For example, there may be acquired at least one of an ECG signal, an EDA signal, and a PPG signal. The multimodal data may be acquired, for example, by an AID module, such as the AID module of the processor as herein described. At 202 of the process, there is determined a first emotional state based on the multimodal data. For example, based on fluctuations in an ECG signal, it may be determined that a user of the ADPT systems and methods described herein is experiencing an anxiety episode. At 203 of the process, an intervention responsive to the emotional state is calibrated based on the multimodal data. For example, based on the fluctuations in the ECG signal, the systems configured to perform the process may suggest that the user or patient having fluctuations in their ECG signal perform guided breathing exercises. As an example, the process may be implemented to suggest targeted mindfulness exercises to healthcare workers managing burnout or recovery from workplace trauma. At 201 of the process, the ADPT systems and methods described herein may be configured to monitor sleep patterns, physiological data, and emotional trends of the healthcare worker. At 202 of the process, the ADPT systems and methods described herein may be configured to identify early indicators of relapse based on the collected data. At203 of the process, the ADPT systems and methods described herein may be configured to provide proactive measures, including relaxation exercises, clinician-triggered support, and haptic reminders for mindfulness activities. Adherence by the healthcare worker to such mindfulness activities may lead to a 40% reduction in relapse episodes and improved emotional stability observed over a 12-month period.

[0157] As another example, the process may be implemented to suggest guided breathing exercises to a paramedic encountering a highly stressful situation during an emergency response. For example, at 201 of the process, the ADPT systems and methods may be configured to detect, via multimodal data, markers of acute stress in the paramedic. Such markers may include, for example, a spike in EDA and other behavioral cues. At 202, of the process, the ADPT systems and methods described in accordance with embodiments herein may be configured to determine that the paramedic is experiencing an episode of anxiety. At 203 of the process, the ADPT systems and methods described herein may be configured to propose immediate guided breathing exercises to the paramedic. Such exercises may be delivered through an AID module, such as the AID module of the processor as described herein, and may further comprise haptic feedback to help synchronize breathing. If stress persists, the system escalates by notifying a clinician or mental health professional. The real-time intervention prevents acute stress from progressing into a more severe condition, enabling the first responder to maintain optimal performance.

[0158] As another example, the process may be implemented to suggest guided breathing exercises to provide time-blocking strategies and relaxation prompts to educators in urban environments. In further implementations, the ADPT systems and methods described herein may be configured to suggest community-driven group activities aligned with local traditions to members of rural populations. In some implementations, the ADPT systems and methods described herein may be configured to suggest sensory grounding techniques to children with developmental disorders. In certain implementations, the ADPT systems and methods described herein may be configured to suggest or provide resilience-building exercises, such as mindfulness and biofeedback training, to military personnel preparing for deployment. For example, the ADPT systems and methods described herein may monitor the emotional states of military personnel preparing for high-stress deployment by analyzing HRV variability and behavioral patterns. The exercises offered by the ADPT systems and methods described hereinmay comprise haptic feedback that reminds users to engage in these exercises, ensuring adherence.

[0159] In some implementations, the ADPT systems and methods described herein may be configured to provide a suggested intervention, such as a guided breathing exercise, to a healthcare responder to a natural disaster. For example, the ADPT systems and methods described herein may detect a physiological deviation (e.g., elevated EDA, decreased HRV) in a healthcare responder. In such instances, real-time haptic feedback prompts the responder to initiate a guided breathing exercise. Calming auditory tones may be provided to stabilize emotional responses. Further, the ADPT systems and methods described herein may be configured to monitor and manage collective emotional responses of affected populations of natural disasters. For example, emergency response teams may use the ADPT systems and methods described herein to stabilize acute stress in disaster-affected individuals through realtime interventions like guided breathing exercises and grounding techniques. In certain implementations, the ADPT systems and methods described herein may be configured to provide emotional resilience programs for students in school -based interventions. For example, the ADPT systems and methods described herein may monitor student stress and provide adaptive interventions tailored to student needs. As another example, teachers may use insights gleaned from the ADPT systems and methods described herein to proactively address student well-being, leading to reduced anxiety levels and improved academic outcomes in high-stress environments.

[0160] These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural and / or object-oriented programming language, and / or in assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and / or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readablemedium can alternatively or additionally store such machine instructions in a transient manner, such as for example, as would a processor cache or other random access memory associated with one or more physical processor cores.

[0161] To provide for interaction with a user, one or more aspects or features of the subject matter described herein can be implemented on a computer having a display device, such as for example a cathode ray tube (CRT) or a liquid crystal display (LCD) or a light emitting diode (LED) monitor for displaying information to the user and a keyboard and a pointing device, such as for example a mouse or a trackball, by which the user may provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well. For example, feedback provided to the user can be any form of sensory feedback, such as for example visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input. Other possible input devices include touch screens or other touch-sensitive devices such as single or multi-point resistive or capacitive track pads, voice recognition hardware and software, optical scanners, optical pointers, digital image capture devices and associated interpretation software, and the like.

[0162] Figure 8 illustrates a table indicating the relationship between emotion categories and multimodal data in accordance with some embodiments described herein. As shown in Figure 8, different emotion categories, such as anxiety, depression, stress, PTSD, and general stress / overload may be associated with particular multimodal data values. The ADPT systems and methods described herein may use the multimodal data to determine the emotional category and range of a user.

[0163] Figure 9 illustrates a non-diagnostic adaptive inference and intervention loop and demonstrates system input / output and adaptation logic without mapping to diagnoses, severity scores, or clinical scales. The illustrated stages and mappings are provided for explanatory purposes only. The system generates regulation state estimates and confidence measures derived from computed features and does not diagnose, predict, or determine a clinical condition.Parameters, thresholds, state labels, and intervention selections may vary by user, context, and implementation. In some embodiments, the system generates a configured intervention output that includes one or more digital intervention parameters, behavioral and / or environmental adjustments, sensory or feedback outputs, and / or external system notifications. The configured intervention output is non-prescriptive and does not prescribe, order, or determine medicationdosing. After delivery of the configured intervention output, the system may acquire postintervention response signals and compute one or more efficacy indicators. Based on the measured response, the system may update one or more thresholds and / or intervention parameters and may recalibrate one or more confidence measures for subsequent monitoring windows.

[0164] In the descriptions above and in the claims, phrases such as “at least one of’ or “one or more of’ may occur followed by a conjunctive list of elements or features. The term “and / or” may also occur in a list of two or more elements or features. Unless otherwise implicitly or explicitly contradicted by the context in which it used, such a phrase is intended to mean any of the listed elements or features individually or any of the recited elements or features in combination with any of the other recited elements or features. For example, the phrases “at least one of A and B;” “one or more of A and B;” and “A and / or B” are each intended to mean “A alone, B alone, or A and B together.” A similar interpretation is also intended for lists including three or more items. For example, the phrases “at least one of A, B, and C;” “one or more of A, B, and C;” and “A, B, and / or C” are each intended to mean “A alone, B alone, C alone, A and B together, A and C together, B and C together, or A and B and C together.” Use of the term “based on,” above and in the claims is intended to mean, “based at least in part on,” such that an unrecited feature or element is also permissible.

[0165] The subject matter described herein can be embodied in systems, apparatus, methods, and / or articles depending on the desired configuration. The implementations set forth in the foregoing description do not represent all implementations consistent with the subject matter described herein. Instead, they are merely some examples consistent with aspects related to the described subject matter. Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and / or variations can be provided in addition to those set forth herein. For example, the implementations described above can be directed to various combinations and subcombinations of the disclosed features and / or combinations and subcombinations of several further features disclosed above. In addition, the logic flows depicted in the accompanying figures and / or described herein do not necessarily require the particular order shown, or sequential order, to achieve desirable results.

[0166] All publications, patents and patent applications mentioned in this specification are indicative of the level of skill of those skilled in the art to which this invention pertains and are herein incorporated by reference. The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that such prior art forms part of the common general knowledge.

[0167] The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims

1. CLAIMS1. A method for monitoring and regulating an emotional state comprising:generating an individualized physiological baseline by:collecting a plurality of physiological data signals from a user using a plurality of physiological sensors;collecting at least one environmental data signal using at least one environmental sensor;extracting a plurality of physiological features from the plurality of physiological data signals using a sensor data processor module; andcalculating a rolling mean and a standard deviation for each of the plurality of physiological features over a temporal window to generate the individualized physiological baseline;characterizing a deviation from the individualized physiological baseline by:collecting real-time physiological data for the user from the plurality of physiological sensors;determining a frequency of deviation, a magnitude of deviation, and a duration of deviation between the real-time physiological data and the individualized physiological baseline; andgenerating an emotion pattern profile expressed as a three-dimensional vector of a regularity index, an intensity coefficient, and a duration parameter of the deviation between the real-time physiological data and the individualized physiological baseline, the regularity index, intensity coefficient, and duration parameter calculated based on the magnitude of the deviation between the real-time physiological data and the individualized physiological baseline; andprescribing an intervention to the user if the deviation between the real-time physiological data and the individualized physiological baseline exceeds at least one operating threshold.

2. The method of claim 1, where the individualized physiological baseline further incorporates user provided information comprising one or more of previous medical history, user personality profile, personal calendar, workplace schedule, medication use, diagnosedconditions, prior episodes of stress, prior episodes of anxiety, sleep schedule, self-reported mood rating, self-reported stress rating, symptom reporting, lifestyle habits, caffeine intake, alcohol intake, drug intake, physical activity level, travel, time-zone changes, shift-work pattern, workplace schedule, and clinician-provided guidance.

3. The method of claim 1 or 2, wherein the temporal window is one or more of a minute, a number of minutes, an hour, an a number of hours, a day, a number of days, a week, a number of weeks, a month, a number of months, a year and a number of years.

4. The method of any one of claims 1-3, wherein the intervention is prescribed by providing an alert on an electronic device.

5. The method of any one of claims 1-4, further comprising preprocessing the physiological data and the environmental data using one or more of motion-artifact labelling, adaptive filtering, on-device noise characterization, cross-validation, varying sampling frequency, signal quality scoring, bandpass filtering, baseline wander removal, powerline interference suppression, outlier detection, missing-data imputation, sensor contact, impedance validation, timestamp alignment, resampling and interpolation, beat detection and artifact correction, normalization, and calibration deviation correction.

6. The method of any one of claims 1-5, wherein the at least one operating threshold comprises a first threshold and a second threshold.

7. The method of any one of claims 1-6, wherein the at least one operating threshold is determined based on one or more of a personality coefficient, historical behavioral patterns, prior intervention response history, and circadian indicators.

8. The method of claim 7, wherein the personality coefficient is derived from one or more of a stored personality profile, a clinician administered assessment, a psychometric questionnaire, a structured onboarding assessment, longitudinally from behavioral response patterns, and longitudinal inference from user engagement behavior.

9. The method of any one of claims 1-8, wherein the intervention prescribed by the detection intervention prevention module is a self-regulated intervention comprising one or more of breathing guidance, posture adjustment, haptic cues, paced respiration, guided grounding exercises, mindfulness prompts, progressive muscle relaxation, cognitive reframing prompts, micro-break scheduling, hydration prompts, sleep hygiene prompts, notification suppression, audio cues, visual prompts, and environmental modification.

10. The method of any one of claims 1-9, further comprising collecting post-intervention physiological data and updating the at least one operating threshold.

11. The method of claim 10, wherein updating the at least one operating threshold is based on a post-intervention recovery measured by calculating a recovery time, an adherence, and a residual deviation, and transmitting the recovery time, the adherence, and the residual deviation to a feedback controller.

12. The method of any one of claims 1-10, wherein the intervention prescribed is a clinician escalation protocol.

13. A system for monitoring and regulating an emotional state comprising:a plurality of physiological sensors;at least one environmental sensor;a sensor data processing module configured to:validate and preprocess a plurality of sensor data signals collected by the plurality of physiological sensors and the at least one environmental sensor; andextract a plurality of physiological features from the plurality of physiological data signals collected by the plurality of physiological sensors;a sensor fusion module configured to:construct an individualized physiological baseline for a user based on the plurality of physiological features;quantify deviation between the individualized physiological baseline and realtime physiological data collected by the plurality of physiological sensors; and generate an emotion pattern profile expressed as a three-dimensional vector of a regularity index, an intensity coefficient, and a duration parameter of the deviation between the real-time physiological data and the individualized physiological baseline; a stimulus identification and detection module configured to map the plurality of deviations onto a plurality of stimuli to establish a plurality of causal links; anda detection intervention prevention module configured to prescribe an intervention, deliver the appropriate intervention to a user electronic device, collect post-intervention physiological data, and update at least one operating threshold in the individualized physiological baseline.

14. The system of claim 13, wherein the plurality of physiological sensors comprise one or more of a electrocardiography (ECG) sensor, photoplethysmography (PPG) sensor, thermistor, electrodermal activity (EDA) sensor, accelerometer, gyroscope, respiration sensor, skin temperature sensor, blood oxygen saturation (SpCh) sensor, and electromyography (EMG) sensor.

15. The system of claim 13 or 14, wherein the at least one environmental sensor is one or more of a noise sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, an air quality sensor, and a location sensor.

16. The system of any one of claims 13-15, wherein the plurality of physiological features include one or more of the heart rate, root mean square of successive differences, standard deviation of normal-to-normal intervals, percentage of normal-to-normal intervals greater than a time duration, and electrodermal activity.

17. The system of any one of claims 13-16, further comprising a clinician alert protocol for alerting a clinician on a clinician electronic device, wherein the clinician alert protocol is initiated after persistence or recurrence of deviations exceeding a safety-bounded threshold.

18. The system of any one of claims 13-17, further comprising an adaptive feedback loop for evaluating an effectiveness of the intervention.

19. The system of any one of claims 13-18, further comprising a personality calibration engine for calibrating an operating threshold.

20. The system of any one of claims 13-19, wherein the intervention prescribed by the detection intervention prevention module is a self-regulated intervention comprising one or more of breathing guidance, posture adjustment, haptic cues, paced respiration, guided grounding exercises, mindfulness prompts, progressive muscle relaxation, cognitive reframing prompts, micro-break scheduling, hydration prompts, sleep hygiene prompts, notification suppression, audio cues, visual prompts, and environmental modification.

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