Method and system for caculating driving readiness score

Abstract

The present disclosure provides a system for calculating a driving readiness score of a user to drive a vehicle using real-time biometric and behavioural data The system includes a wearable device worn by the user, and the vehicle or a user wearable device. The system is configured to receive real-time biometric data from the wearable device, including sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep. The system extracts one or more feature vectors from the biometric data and correlates them with predefined feature sets representing baseline physiological states. Based on the correlation, the system calculates a driving readiness score using a machine learning (ML) model. Upon evaluating the driving readiness score, the system generates an alert and displays it on a display unit or user wearable device to notify the user of their current readiness state before driving.

Description

METHOD AND SYSTEM FOR CACULATING DRIVING READINESS SCOREFIELD OF INVENTION

[0001] Present disclosure relates to driving readiness score, more specifically, the present invention relates to a method and system for calculating driving readiness score.BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.

[0003] Traditionally, evaluating a user's readiness to operate a vehicle has relied heavily on subjective self-assessment or generic guidelines that may not accurately reflect an individual's current physiological and psychological state. Such methods often overlook personalized factors crucial to determine alertness, such as sleep quality, stress levels, and physical fatigue. Moreover, existing technologies typically focus on discrete metrics without integrating comprehensive biometric and behavioural data.

[0004] The limitations of traditional methods contribute significantly to road accidents caused by user’s fatigue and lack of alertness. Self-assessment alone can be unreliable, as individuals may underestimate their fatigue or overestimate their readiness to drive. Generic guidelines also fail to account for the variability in user’s physiological responses to fatigue and stress, influencing user’s driving performance. Furthermore, existing state of art often lack real-time monitoring capabilities and fail to provide actionable insights that can proactively address user’s fatigue.

[0005] Thus, there is a need for a methodology of calculating driving readiness score that can address the above-mentioned shortcomings.OBJECT OF THE INVENTION

[0006] A general objective of the present disclosure is to provide an accurate and individualized measure of a user's alertness and readiness to drive by integrating biometric data (such as sleep quality, heart rate variability, and movement patterns) with behavioural patterns.

[0007] Another objective of the present disclosure is to improve road safety by offering a real-time assessment that helps users to evaluate current state of readiness before driving, thereby reducing the risk of accidents caused by fatigue, stress, or inattentiveness.

[0008] Yet another objective of the present disclosure is to encourage users to maintain optimal sleep, stress, and activity levels by offering feedback on how the factors can impact driving readiness, thereby improving the overall well-being.

[0009] Yet another objective of the present disclosure is to support fleet management by providing fleet operators with a tool for monitoring and managing user’s readiness before driving, allowing for better oversight and management of user’s health and performance.SUMMARY OF THE INVENTION

[0010] The summary is provided to introduce aspects related to a method and system for calculating driving readiness score of a user to drive a vehicle. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.

[0011] According to an embodiment of the present disclosure, a method for calculating a driving readiness score of a user to drive a vehicle is disclosed. The method includes receiving, via a wearable device, real-time biometric data of the user. The real-time biometric data includes sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep. The method further includes extracting, one or more feature vectors from the real-time biometric data to determine one or more physiological parameters and behavioural indicators of the user. The method further includes correlating, the extracted one or more feature vectors with predefined feature vectors of the real-time biometric data using a machine learning (ML) model. The method further includes calculating, a driving readiness score based on the correlation between the extracted one or more feature vectors with predefined feature vectors. The driving readiness score includes a sleep score, an HRV score, a movement pattern score, a temperature score, and a heart rate drop score. The method furtherincludes generating, an alert based on the calculation of the driving readiness score for the user. The method further includes displaying, the alert on a display unit of the vehicle or a user wearable device.

[0012] In another aspect, the sleep quality metrics comprise total sleep duration, sleep efficiency, sleep stage distribution including light and deep sleep, sleep interruptions, and sleep pattern consistency.

[0013] In another aspect, the heart rate variability (HRV) is used to determine the user's autonomic nervous system balance and stress levels prior to driving.

[0014] In another aspect, the movement patterns are analysed to assess the user's physical fatigue and activity consistency throughout the day.

[0015] In another aspect, the wearable device is a ring embedded with one or more biometric sensors configured to collect and transmit the real-time biometric data.

[0016] In another aspect, the ML model comprises supervised and unsupervised learning algorithms configured to identify correlations and dependencies among the physiological parameters and the behavioural indicators.

[0017] In another aspect, the driving readiness score is normalized on a scale of 0 to 100 and includes individual component scores selected from the sleep score, the HRV score, the movement pattern score, the temperature score, and the heart rate drop score.

[0018] In another aspect, the alert generated is indicative of one or more physiological states of the user, at least one of optimal readiness, moderate fatigue, or critical fatigue.

[0019] According to an embodiment of the present disclosure, a system for calculating a driving readiness score of a user to drive a vehicle is disclosed. The system comprises a wearable device configured to monitor and transmit real-time biometric data of a user, and the vehicle or a user wearable device. The vehicle or the user wearable device comprises, a display unit, one or more processors, a memory, and one or more programs stored in the memory. The one or more programs when executed by the one or more processors cause the one or more processors to receive, via the wearable device, the real-time biometric data of the user, whereinthe real-time biometric data comprises sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep. The one or more processors are further configured to extract one or more feature vectors from the real-time biometric data to determine one or more physiological parameters and behavioural indicators of the user. The one or more processors are further configured to correlate the extracted one or more feature vectors with predefined feature vectors of the real-time biometric data using a machine learning (ML) model. The one or more processors are further configured to calculate a driving readiness score based on the correlation between the extracted one or more feature vectors with predefined feature vectors, wherein the driving readiness score includes a sleep score, an HRV score, a movement pattern score, a temperature score, and a heart rate drop score. The one or more processors are further configured to generate an alert based on the calculation of the driving readiness score for the user. The one or more processors are further configured to display the alert on the display unit of the vehicle or the user wearable device.

[0020] Other aspects and advantages of the invention will become apparent from the following description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings constitute a part of the description and are used to provide further understanding of the present invention. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0022] FIG. 1 illustrates an example environment for calculating a driving readiness score of a user to drive a vehicle, in accordance with an embodiment of the present invention;

[0023] FIG. 2 illustrates a block diagram of a system for calculating a driving readiness score of a user to drive a vehicle, according to an embodiment of the present disclosure;

[0024] FIG. 3 illustrates a functional block diagram of modules for calculating a driving readiness score of a user to drive the vehicle, according to an embodiment of the present disclosure; and

[0025] FIG. 4 illustrates a flow chart for a method of calculating a driving readiness score of a user to drive the vehicle, according to an embodiment of the present disclosure.DESCRIPTION OF THE INVENTION

[0026] The description set forth below in connection with the appended drawings is intended as a description of various embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. Each embodiment described in this invention is provided merely as an example or illustration of the present invention, and should not necessarily be construed as preferred or advantageous over other embodiments. The description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. Further, the reference numerals for similar components, modules, units, and operation steps have been kept same for the ease of understanding.

[0027] Some embodiments of the present disclosure now will be described with reference to the accompanying drawings, in which some, but not all, embodiments of the disclosure are shown. Indeed, embodiments of the disclosure may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements.

[0028] The present disclosure, includes a method and system for evaluating user’s readiness level before driving by integrating biometric data from a wearable device with behavioural pattern. Through sophisticated data integration and machine learning algorithms, the present disclosure calculates a driving readiness score, offering personalized insights into an individual's alertness levels before driving. Such an approach addresses the inadequacies of subjective assessments and generic guidelines, aiming to mitigate accidents stemming from user’s fatigue and diminished alertness. By empowering users with real-time physiological and behavioral data, the invention promotes proactive measures to enhance road safety.

[0029] FIG. 1 illustrates an example environment for calculating a driving readiness score of a user to drive a vehicle, in accordance with an embodiment of the present invention.According to an exemplary embodiment, the environment may include a system 100 for calculating a driving readiness score to drive the vehicle. The system 100 includes a smart ring 102 and a display unit 104 integrated within a vehicle 108. The smart ring 102 is corresponds to a wearable device which is worn by a user 106 and is configured to collect real-time biometric and behavioral data indicative of the user’s physiological state. The collected realtime biometric data is processed to calculate a driving readiness score, which is then presented to the user 106 via the display unit 104.

[0030] In an embodiment, the smart ring 102 includes a plurality of biometric sensors, configured to continuously monitor physiological parameters such as sleep quality, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep. These parameters reflect the user’s mental and physical readiness for safe driving.

[0031] The smart ring 102 transmits the collected biometric data to a processing unit (not shown), which applies machine learning algorithms to extract feature vectors and identify patterns that correlate with user fatigue, stress, and alertness levels. Based on these correlations, the system 100 calculates the driving readiness score, comprising sub-scores such as a sleep score, HRV score, movement score, temperature score, and HR drop score.

[0032] The system 100 updates the driving readiness score based on real-time inputs and contextual decay factors such as time since waking, proximity to habitual sleep time, and cumulative driving duration. The driving readiness score is then communicated to the user via the display unit 104 of the vehicle 108, which may present a visual alert or recommendation such as “Safe to Drive,” “Monitor Alertness,” or “Consider Rest.”

[0033] In an exemplary embodiment, the system 100 also provides audible alerts or haptic feedback through the wearable device or within the vehicle 108, prompting the user 106 to take corrective actions such as delaying the trip or taking rest breaks. The integration of biometric monitoring with in-vehicle feedback improves driver safety by providing proactive decisionmaking and reducing the possibility of fatigue-related incidents.

[0034] FIG. 2 illustrates a block diagram of a system for calculating a driving readiness score of a user to drive a vehicle, according to an embodiment of the present disclosure. The system 100 may include the smart ring 102 and the vehicle 108 or a user wearable device which may be communicatively coupled with each other. In an embodiment, the vehicle may includea processor 202, a memory 204, a module 206 and the display unit 104 are operably connected with each other. In another embodiment, the processor 202, the memory 204, the module 206 and the display unit 104 may be present in the user wearable device. The user wearable device may include a smartwatch, a smart wristband, a smart specs, a virtual reality (VR) glasses and further include a smartphone, a tablet and alike.

[0035] For an example, the processor(s) 202 may be a single processing unit or a number of units, all of which could include multiple computing units. The processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logical processors, virtual processors, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) 202 is configured to fetch and execute computer- readable instructions and data stored in the memory 204.

[0036] The memory 204 may include any non-transitory computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and / or non-volatile memory, such as read-only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes.

[0037] In an example, the module(s), engine(s), and / or unit(s) 206 may include a program, a subroutine, a portion of a program, a software component, or a hardware component capable of performing a stated task or function. As used herein, the module(s), engine(s), and / or unit(s) may be implemented on a hardware component such as a server independently of other modules, or a module can exist with other modules on the same server, or within the same program. The module (s), engine(s), and / or unit(s) 206 may be implemented on a hardware component such as processor one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. The module (s), engine(s), and / or unit(s) 206 when executed by the processor(s) 1201 may be configured to perform any of the described functionalities.

[0038] The modules / engines / units 206 may be implemented with an Al module that may include a plurality of neural network layers. Examples of neural networks include, but are notlimited to, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), and a Restricted Boltzmann Machine (RBM). The learning technique is a method for training a predetermined target device using a plurality of learning data to cause, allow, or control the target device to make a determination or prediction. Examples of the learning techniques include, but are not limited to, supervised learning, unsupervised learning, semi- supervised learning, or reinforcement learning. At least one of a plurality of CNN, DNN, RNN, RMB models and the like may be implemented to thereby achieve execution of the present subject matter's mechanism through an Al model. A function associated with the Al model may be performed through the non-volatile memory, the volatile memory, and the processor. The processor may include one or a plurality of processors. At this time, one or a plurality of processors may be a general-purpose processor, such as a central processing unit (CPU), an application processor (AP), or the like, a graphics-only processing unit such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an Al-dedicated processor such as a neural processing unit (NPU). The one or a plurality of processors control the processing of the input data in accordance with a predefined operating rule or the artificial intelligence (Al) model stored in the non-volatile memory and the volatile memory. The predefined operating rule or artificial intelligence model is provided through training or learning.

[0039] As an example, the display unit 104 includes a computer monitor, a touch screen, an output device capable of displaying the graphics, and the like. The display unit 104 is configured to display visual output in desktops, laptops, and workstations. The display unit 104 may come in different sizes, resolutions, and types (such as LCD, LED, or OLED).

[0040] In an embodiment, the smart ring 102 may include biometric sensors 200 for monitoring the user 106 health parameters. The biometric sensors may include, but not limited to, a photoplethysmography (PPG) sensor for measuring heart rate and heart rate variability (HRV), an electrodermal activity (EDA) sensor for detecting stress-related changes in skin conductance, and a skin temperature sensor for monitoring fluctuations in peripheral body temperature. Additionally, a 3 -axis accelerometer and a gyroscope may be included to detect user movement patterns, physical activity, posture, and orientation, which are indicative of fatigue and behavioral consistency. The biometric sensor 200 may further include a pressure sensor may configured to detect microvascular changes or blood volume pulse characteristics, and a blood oxygen saturation (SpO?) sensor to assess oxygen levels in the bloodstream duringsleep and wakefulness, as well as dedicated sleep monitoring sensors for identifying sleep stages such as light sleep, deep sleep, and REM, along with detecting sleep interruptions.

[0041] The biometric sensor 200 continuously collect real-time biometric data such as the heart rate variability (HRV), the sleep quality metrics, the movement patterns, the body temperature, and the heart rate drop during sleep. The real-time biometric data collected forms the foundational input for evaluating the user’s physical and cognitive readiness to drive.

[0042] According to one embodiment, the biometric data from the smart ring 102 is wirelessly transmitted to the vehicle 108 or the user’s wearable / mobile device which hosts the computational components of the system 100. The processor 202 is configured to execute instructions that enable the analysis of the incoming biometric data. The memory 204 stores the extracted feature vectors, baseline user profiles, historical readiness scores, and machine learning models used to evaluate the user’s readiness score.

[0043] In one embodiment, the module 206 includes machine learning algorithms that correlate extracted feature vectors from the biometric data with predefined readiness models. These algorithms process the data to generate component scores such as a sleep score, HRV score, movement score, temperature score, and heart rate drop score. The module 206 synthesizes these into a normalized driving readiness score on a scale from 0 to 100.

[0044] The driving readiness score is updated based on real-time inputs and contextual modifiers such as time since the user last slept, cumulative driving time, and proximity to habitual sleep time. The system 100 may also factor in personalized baselines, enabling tailored assessment across varying user demographics. The display unit 104, integrated within the vehicle dashboard or user device interface, serves as the primary output interface. It is configured to visually present the user’s current readiness score and generate qualitative alerts or recommendations. These alerts may include statements such as “Safe to Drive,” “Monitor Alertness,” or “Consider Taking a Break,” depending on the score range. In some embodiments, the alert may also be accompanied by haptic or audible feedback via the wearable device.

[0045] FIG. 3 illustrates a functional block diagram of modules for calculating a driving readiness score of a user to drive the vehicle, according to an embodiment of the present disclosure. According to an embodiment, the system 100 includes a receiving module 300, aextraction module 302, an evaluation module 304, and a displaying module 306 coupled with each other. In an embodiment, the receiving module 300, the extraction module 302, the evaluation module 304, and the displaying module 306 are uniquely designed hardware or software that are integrated within the system 100. According to some embodiment, the functions of the receiving module 300, the extraction module 302, the evaluation module 304, and the displaying module 306 can be performed by one or more processors. According to some embodiments, the receiving module 300, the extraction module 302, the evaluation module 304, and the displaying module 306 may be a part of an Al framework of the application to develop Al-powered solutions for specific tasks. An explanation will be made by referring to the modules depicted in FIG. 3. Furthermore, the labels depicted in the representative drawings are kept the same for similar components and operations throughout the disclosure for ease of understanding. The detailed functioning of each module will be explained in the following paragraphs.

[0046] The receiving module 300 is configured to continuously receive the real-time biometric data from the wearable device, such as the smart ring 102 worn by the user 106. The receiving module 300 acts as the first point of interface for capturing the user’s physiological and behavioral indicators, which are essential for evaluating driving readiness. The biometric data collected by the biometric sensor 200 in the smart ring 102 is transmitted wirelessly to the receiving module 300 for further processing.

[0047] The extraction module 302 is configured to extract one or more feature vectors from the real-time biometric data received from the smart ring 102, in order to determine one or more physiological parameters and behavioral indicators of the user 106. The extracted features represent condensed, structured representations of raw sensor inputs, enabling efficient analysis by downstream machine learning algorithms. The biometric data inputs may include the heart rate variability (HRV), the sleep quality metrics, the body temperature, the movement patterns, and the heart rate drop during sleep.

[0048] The extraction module 302 processes these inputs using signal processing and statistical modeling techniques to derive features such as time-domain and frequency-domain HRV measures (e.g., RMSSD, LF / HF ratio), sleep duration consistency, sleep efficiency percentage, and sleep stage transitions. From movement patterns, the module may extract metrics like activity intensity, restlessness index, and sedentary intervals. For temperature, themodule evaluates thermal stability, deviations from baseline, and circadian alignment. Similarly, the heart rate drop feature is extracted by measuring the delta between pre-sleep and deep sleep HR values over time.

[0049] The extraction module 302 further correlates the extracted feature vectors with predefined feature vectors stored in the system using a machine learning (ML) model. These predefined feature vectors represent normative or baseline biometric patterns that correspond to known states of alertness, fatigue, stress, or optimal driving readiness. The correlation process involves comparing the user’s current feature vectors against these predefined templates to determine how closely the user’s current physiological state aligns with ideal or suboptimal conditions.

[0050] In one embodiment, the ML model may include a trained neural network, decision tree, or ensemble classifier capable of recognizing patterns and deviations from standard biometric baselines. The model identifies complex, non-linear relationships between feature sets and learns to classify or score readiness levels based on these relationships.

[0051] The evaluation module 304 is configured to calculate a driving readiness score for the user 106 based on the correlation results between the extracted feature vectors and predefined biometric feature templates. The evaluation module 304 receives correlated data from the extraction module 302, which includes real-time physiological and behavioral indicators derived from biometric inputs such as sleep quality, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep.

[0052] Upon receiving the correlated feature data, the evaluation module 304 calculates individual component scores representing key dimensions of the user’s readiness state. The evaluation module 304 assigns weights to each component score based on predefined heuristics or model-learned importance factors. These weighted scores are then synthesized into a composite driving readiness score, normalized on a scale from 0 to 100. A higher score reflects an optimal physiological and cognitive state favorable for driving, while a lower score may indicate fatigue, stress, or compromised alertness, suggesting the user should rest or delay driving.

[0053] In an embodiment, the evaluation module 304 may adjusts the final readiness score by factoring in contextual modifiers such as time since wake-up, proximity to habitual bedtime,and accumulated drive time. These modifiers ensure the score reflects not just static biometric states but also real-time fatigue risk due to circadian misalignment or extended wakefulness.

[0054] The final driving readiness score, along with its component sub-scores, is evaluated and generates an alert based on the calculation by the evaluation module 304. In some implementations, the evaluation module 304 also maps the readiness score to qualitative alerts such as “Safe to Drive,” “Monitor Alertness,” or “Not Ready to Drive,” thereby enabling actionable, personalized feedback for safety-critical decision-making.

[0055] The displaying module 306 is configured to display the calculated driving readiness score and corresponding alerts or recommendations on the display unit 104 located within the vehicle 108 or on the user’s wearable or mobile device. After receiving the readiness score and its associated component scores from the evaluation module 304, the displaying module 306 generates a user-facing interface that may include the overall readiness score, sub-scores such as sleep score, HRV score, movement pattern score, temperature score, and heart rate drop score, along with a contextual alert. The alerts may be visually presented using text, icons, or color-coded indicators such as green for “Safe to Drive,” yellow for “Monitor Alertness,” and red for “Consider Rest” to help the user interpret their current physiological and behavioral state. The display unit 104 may be implemented as an in-vehicle dashboard screen, infotainment panel, heads-up display (HUD), or alternatively on a smartphone or smartwatch linked to the smart ring 102. In certain cases, if the readiness score falls below a critical threshold, the displaying module 306 may also trigger a haptic or audible alert to prompt user attention. Additionally, the module may provide historical trends or wellness insights over time, helping the user understand patterns in their readiness data. In fleet-based use cases, the displaying module 306 may also transmit readiness alerts to a central monitoring system for real-time fleet management and safety intervention. Overall, the displaying module 306 serves as the final output layer of the system 100, enabling real-time, user-friendly communication of readiness status and supporting informed, safety-conscious decisions before driving.

[0056] FIG. 4 illustrates a flow chart for a method of calculating a driving readiness score of a user to drive the vehicle, according to an embodiment of the present disclosure. The method 400 is implemented in the system 100 of FIGs. 1, and 2. Further, steps of the method 400 are explained in detail through FIGs 2 and 3, therefore for the sake of brevity, the detailed explanation has been omitted here.

[0057] In an embodiment, the system 100 for evaluating the user's readiness level before driving the vehicle. At first, the real-time biometric data is collected and transmitted by the biometric sensors 200.

[0058] According to an embodiment, the method 400, at step 402 includes receiving, via the wearable device, real-time biometric data of the user, wherein the real-time biometric data comprises sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep. The receiving module 300 is configured to continuously acquire biometric data generated by one or more biometric sensors 200 embedded within the smart ring 102 worn by the user 106. The biometric data includes multiple physiological and behavioral parameters including, but not limited to, sleep quality metrics (such as sleep duration, sleep efficiency, sleep stage distribution, and sleep efficiency), heart rate variability (HRV) (indicating autonomic nervous system balance), movement patterns (reflecting activity level and physical fatigue), body temperature (for assessing thermoregulation and metabolic state), and heart rate drop during sleep (used to evaluate sleep recovery quality). The data acquisition is performed in real time, enabling the system 100 to establish an accurate, up-to-date physiological profile of the user 106 prior to vehicle operation.

[0059] According to an embodiment, the method 400, at step 404, includes extracting one or more feature vectors from the real-time biometric data to determine one or more physiological parameters and behavioral indicators of the user 106. In an embodiment, this step is performed by the extraction module 302 of the system 100. The extraction module 302 is configured to process the raw biometric data received from the receiving module 300 and convert it into structured, high-level features that represent the current health and behavioral state of the user. These feature vectors are extracted using signal processing, statistical analysis, and temporal modeling techniques, and may include metrics such as sleep efficiency, REM cycle duration, movement variance, skin temperature stability, and heart rate recovery curves during sleep.

[0060] For example, the extraction module 302 may compute HRV features like RMSSD and LF / HF ratio to assess stress and autonomic balance, while sleep features may include total sleep time and frequency of disturbances. Movement data is analyzed to determine patterns of activity or fatigue, and temperature readings are compared against circadian baselines to inferthermoregulation status. The extracted feature vectors collectively form a detailed profile of the user’s physiological readiness and behavioral condition, enabling personalized and dynamic evaluation of their capability to operate the vehicle safely.

[0061] According to an embodiment, the method 400, at step 406, includes correlating the extracted one or more feature vectors with predefined feature vectors of the real-time biometric data using a machine learning (ML) model. In an embodiment, this step is performed by the extraction module 302 in conjunction with the evaluation module 304 of the system 100. The extracted feature vectors, representing the user's current physiological parameters and behavioral indicators, are input into the ML model which has been previously trained on labeled datasets comprising biometric profiles associated with various levels of alertness, fatigue, stress, and readiness.

[0062] The predefined feature vectors stored in the system represent standard or baseline biometric patterns known to correspond with optimal and suboptimal readiness states. The ML model compares the user’s current data against these baseline patterns to detect correlations, deviations, or similarities. For instance, the model may identify that the user's sleep quality vector and HRV indicators closely resemble patterns associated with high fatigue or stress. The machine learning algorithms may include decision trees, support vector machines, neural networks, or ensemble models, depending on implementation.

[0063] In an embodiment, the method 400, at step 408, includes calculating a driving readiness score based on the correlation between the extracted feature vectors and the predefined feature vectors. This step is performed by the evaluation module 304 of the system 100. The evaluation module 304 processes the correlation results provided by the machine learning model and computes a personalized driving readiness score that reflects the user’s current physiological and behavioral state in the context of safe driving. The readiness score is composed of multiple weighted sub-scores, including a sleep score (based on sleep duration, sleep efficiency, and sleep stage distribution), an HRV score (based on measures like RMSSD and LF / HF ratio), a movement pattern score (derived from activity levels, movement consistency, and restlessness), a temperature score (based on circadian-aligned skin temperature trends), and a heart rate drop score (reflecting the degree and quality of cardiovascular recovery during sleep). Each of these component scores is normalized and combined using predefined weightings to generate a final readiness score on a scale from 0 to100, where higher scores indicate better preparedness to drive. In some cases, the evaluation module 304 also adjusts the final score by factoring in contextual modifiers such as time since waking, accumulated drive time, or deviation from typical sleep cycles. This ensures that the readiness score not only reflects raw biometric data but also accounts for real-world fatigue risks, enabling a more accurate, timely, and context-aware assessment of the user's driving readiness.

[0064] According to an embodiment, the method 400, at step 410, includes generating an alert based on the calculation of the driving readiness score for the user. In an embodiment, this step is performed by the evaluation module 304 or a dedicated alert generation unit within the system 100. Once the driving readiness score has been calculated — based on a combination of biometric sub-scores such as sleep, HRV, movement, temperature, and heart rate drop — the system evaluates whether the score falls within specific predefined thresholds that correspond to various levels of alertness or fatigue.

[0065] Depending on the readiness score, the system may generate one of several types of alerts. For example, if the score is above a high threshold (e.g., >80), an alert may indicate “Safe to Drive,” signaling optimal physiological state. If the score falls within a moderate range (e.g., 50-80), the system may generate a “Monitor Alertness” warning, suggesting that the user may be entering a borderline state of fatigue or stress. If the score is low (e.g., < 50), the system may trigger a critical alert such as “Consider Rest” or “Not Ready to Drive,” indicating a heightened risk of impaired driving performance due to fatigue, poor sleep recovery, or stress- related physiological markers.

[0066] The alert may be generated in multiple formats, including visual notifications (text or color-coded icons), audible prompts, or haptic feedback on the wearable device. These alerts serve as real-time decision support for the user, encouraging proactive behavior such as delaying a trip, taking a break, or adjusting sleep / activity patterns.

[0067] According to an embodiment, the method 400, at step 412, includes displaying the alert on the display unit 104 of the vehicle or the user wearable device. In an embodiment, this step is performed by the displaying module 306 of the system 100, which is operatively connected to the vehicle’s display unit 104 and / or a companion device interface such as a smartphone, smartwatch, or the smart ring 102 itself. Upon generation of the alert based on theuser’s calculated driving readiness score, the displaying module 306 is configured to deliver a real-time visual or auditory notification to the user 106.

[0068] The alert may be displayed on the vehicle’s dashboard screen, infotainment system, or a heads-up display (HUD), depending on the vehicle configuration. Alternatively, or in parallel, the same alert may appear on a mobile app or wearable device interface for portability and continuity outside the vehicle. The alert includes key information such as the current readiness score, a corresponding status indicator (e.g., “Safe to Drive,” “Monitor Alertness,” or “Consider Rest”), and, in some embodiments, a breakdown of component scores (e.g., sleep, HRV, movement, etc.). In addition to text-based messages, the displaying module 306 may use color codes (e.g., green for safe, yellow for caution, red for warning), icons, or graphs to visually convey the user's physiological state. For critical alerts, the system 100 may also activate audible tones or vibration feedback through the wearable device to ensure the user is immediately aware of their readiness status. This real-time, multi-modal feedback mechanism enables the user to make informed decisions regarding their fitness to drive and helps prevent driving under suboptimal physiological conditions.Technical Advancement

[0069] The present disclosure represents a significant technical advancement in the field of driver safety and biometric monitoring by introducing a system that seamlessly integrates advanced wearable sensors with machine learning algorithms to deliver real-time, individualized assessments of a user's readiness to drive. Unlike conventional methods that rely on subjective self-assessment or generic metrics, this system uses multi-parameter biometric data including heart rate variability, sleep quality, body temperature, and movement patterns to calculate a dynamic driving readiness score. The system's ability to correlate extracted biometric features with predefined behavioral profiles, adjust scores in real time based on contextual factors, and deliver personalized alerts through in-vehicle or wearable interfaces reflects a substantial leap in precision, automation, and applicability. Furthermore, the system’s scalability, adaptability to environmental variations, and support for fleet-level monitoring make it a robust solution not only for individual users but also for commercial and public safety applications.

[0070] The disclosed technique, provides an improved method of predicting anomalies in the airflow condition over the wing surface of the aircraft. This helps in securely deploying orexecuting an external software framework in end devices like edge devices, cloud-based systems, organizational devices, and the like.

[0071] The figures of the disclosure are provided to illustrate some examples of the invention described. The figures are not to limit the scope of the depicted embodiments of the appended claims. Aspects of the disclosure are described herein with reference to the invention to example embodiments for illustration. It should be understood that specific details, relationships, and methods are set forth to provide a full understanding of the example embodiments. One of the ordinary skills in the art recognize the example embodiments that can be practiced without one or more specific details and / or with other methods.

[0072] Aspects of the present disclosure may be implemented as computer program products that comprise articles of manufacture. Such computer program products may include one or more software components including, for example, applications, software objects, methods, data structure, and / or the like. In some embodiments, a software component may be stored on one or more non-transitory computer-readable media, which computer program product may comprise the computer-readable media with software component, comprising computer executable instructions, included thereon. The various control and operational systems described herein may incorporate one or more of such computer program products and / or software components for causing the various conveyors and components thereof to operate in accordance with the functionalities described herein.

[0073] It is to be understood that the disclosure is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation, unless described otherwise.

Claims

WE CLAIM:

1. A method for calculating a driving readiness score of a user to drive a vehicle, the method comprising: receiving, via a wearable device, real-time biometric data of the user, wherein the realtime biometric data comprises sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep; extracting one or more feature vectors from the real-time biometric data to determine one or more physiological parameters and behavioural indicators of the user; correlating the extracted one or more feature vectors with predefined feature vectors of the real-time biometric data using a machine learning (ML) model; calculating a driving readiness score based on the correlation between the extracted one or more feature vectors with the predefined feature vectors, wherein the driving readiness score comprises a sleep score, an HRV score, a movement pattern score, a temperature score, and a heart rate drop score; generating an alert based on the calculation of the driving readiness score for the user; and displaying the alert on a display unit of the vehicle or a user wearable device.

2. The method as claimed in claim 1, wherein the sleep quality metrics comprise total sleep duration, sleep efficiency, sleep stage distribution including light and deep sleep, sleep interruptions, and sleep pattern consistency.

3. The method as claimed in claim 1, wherein the heart rate variability (HRV) is used to determine autonomic nervous system balance and stress levels of the user prior to driving.

4. The method as claimed in claim 1, wherein the movement patterns are analysed to assess physical fatigue and activity consistency of the user throughout the day.

5. The method as claimed in claim 1, wherein the wearable device is a smart ring embedded with one or more biometric sensors configured to collect and transmit the real-time biometric data.

6. The method as claimed in claim 1, wherein the ML model comprises supervised and unsupervised learning algorithms configured to identify correlations and dependencies among the physiological parameters and the behavioural indicators.

7. The method as claimed in claim 1, wherein the driving readiness score is normalized on a scale of 0 to 100 and includes individual component scores selected from the sleep score, the HRV score, the movement pattern score, the temperature score, and the heart rate drop score.

8. The method as claimed in claim 1, wherein the alert generated is indicative of one or more physiological states of the user, at least one of optimal readiness, moderate fatigue, or critical fatigue.

9. A system 100, comprises: a wearable device configured to monitor and transmit real-time biometric data of a user; and a vehicle 108 or a user wearable device comprises: a display unit 104; one or more processors 202; a memory 204; and one or more programs stored in the memory 204, the one or more programs when executed by the one or more processors 202 cause the one or more processors 202 to: receive, via the wearable device, the real-time biometric data of the user, wherein the real-time biometric data comprises sleep quality metrics, heart rate variability (HRV), movement patterns, body temperature, and heart rate drop during sleep; extract one or more feature vectors from the real-time biometric data to determine one or more physiological parameters and behavioural indicators of the user;correlate the extracted one or more feature vectors with predefined feature vectors of the real-time biometric data using a machine learning (ML) model; calculate a driving readiness score based on the correlation between the extracted one or more feature vectors with predefined feature vectors, wherein the driving readiness score comprises a sleep score, an HRV score, a movement pattern score, a temperature score, and a heart rate drop score; generate an alert based on the calculation of the driving readiness score for the user; and display the alert on the display unit of the vehicle or the user wearable device.

10. The system 100 as claimed in claim 9, wherein the sleep quality metrics comprise total sleep duration, sleep efficiency, sleep stage distribution including light and deep sleep, sleep interruptions, and sleep pattern consistency.

11. The system 100 as claimed in claim 9, wherein the heart rate variability (HRV) is used to determine the user's autonomic nervous system balance and stress levels prior to driving.

12. The system 100 as claimed in claim 9, wherein the movement patterns are analysed to assess the user's physical fatigue and activity consistency throughout the day.

13. The system 100 as claimed in claim 9, wherein the wearable device is a smart ring 102 embedded with one or more biometric sensors 200 configured to collect and transmit the realtime biometric data.

14. The system 100 as claimed in claim 9, wherein the ML model comprises supervised and unsupervised learning algorithms configured to identify correlations and dependencies among the physiological parameters and the behavioural indicators.

15. The system 100 as claimed in claim 9, wherein the driving readiness score is normalized on a scale of 0 to 100 and includes individual component scores selected from the sleep score, the HRV score, the movement pattern score, the temperature score, and the heart rate drop score.

16. The system 100 as claimed in claim 9, wherein the alert generated is indicative of one or more physiological states of the user, at least one of optimal readiness, moderate fatigue, or critical fatigue.

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