Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to health monitoring and predictive analysis, more specifically, relates to automated sleep disorder detection, monitoring, and prediction system and method thereof.
BACKGROUND OF THE DISCLOSURE
[0002] The system is integrating data from multiple sources such as wearable devices, sleep tracking applications, pulse oximeters, and actigraphy to provide a holistic view of an individual's sleep health. It is incorporating manual inputs including bedtime, wake-up time, and number of awakenings to enhance accuracy. By continuously analyzing sleep patterns, it is ensuring precise identification of irregularities and potential sleep disorders, thereby facilitating early intervention and preventive measures.
[0003] The system is eliminating the need for expensive and time-consuming clinical studies such as polysmnography by offering a non-invasive and scalable solution for continuous sleep monitoring. It is leveraging machine learning algorithms to process and analyze sleep data in real time, making sleep health tracking more accessible to a wider population. Unlike traditional methods that are relying on clinical visits, the system is enabling users to monitor their sleep quality in their own environment, ensuring convenience and affordability.
[0004] The system is employing machine learning algorithms to predict sleep quality and detect potential medical or mental health concerns in advance. It is analyzing sleep-related data along with lifestyle factors to provide personalized recommendations for improving sleep patterns. If necessary, it is recommending medical consultation for early diagnosis of underlying health conditions. Additionally, it is offering periodic reviews to help individuals maintain long-term sleep health and well-being.
[0005] Existing inventions for sleep monitoring are relying on standalone devices that are analyzing only a single data source, such as heart rate or movement patterns, without integrating multiple physiological and behavioral parameters. These systems are failing to provide a comprehensive analysis of sleep quality since they are not considering external factors like lifestyle habits, environmental conditions, or medical history. As a result, they are often generating inaccurate or incomplete insights, leading to ineffective sleep health recommendations.
[0006] Conventional sleep diagnostic methods such as polysomnography are requiring individuals to visit specialized sleep laboratories, where they are undergoing extensive monitoring under clinical supervision. These procedures are being expensive, time-consuming, and physically inconvenient, making them inaccessible for a large segment of the population. Many individuals are avoiding necessary sleep evaluations due to the high cost, which is leading to delayed diagnosis and untreated sleep disorders.
[0007] Existing sleep monitoring systems are focusing primarily on tracking past sleep patterns without utilizing advanced analytical techniques for future sleep health prediction. These systems are not employing machine learning algorithms to detect early signs of potential medical or mental health issues. Additionally, they are not providing customized recommendations based on individual sleep behaviors, which is resulting in generic feedback that is failing to address specific user needs for sleep improvement and overall well-being.
[0008] Thus, in light of the above-stated discussion, there exists a need for an automated sleep disorder detection, monitoring, and prediction system and method thereof.
SUMMARY OF THE DISCLOSURE
[0009] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0010] According to illustrative embodiments, the present disclosure focuses on an automated disease detection and health monitoring system and method thereof which overcomes the above-mentioned disadvantages or provides the users with a useful or commercial choice.
[0011] An objective of the present disclosure is to develop a sleep analysis system that is integrating data from multiple sources, including wearable devices, sleep tracking applications, pulse oximeters, and actigraphy, to provide a comprehensive assessment of sleep quality.
[0012] An objective of the present disclosure is to implement machine learning algorithms for analyzing sleep patterns, identifying potential sleep disorders, and predicting future sleep health risks based on collected data.
[0013] Another objective of the present disclosure is to provide real-time monitoring of sleep-related physiological parameters such as heart rate, oxygen levels, movement patterns, and breathing irregularities to enhance sleep quality assessment.
[0014] Another objective of the present disclosure is to ensure a cost-effective and accessible sleep monitoring solution that is reducing the dependency on traditional polysmnography-based sleep studies, which are expensive and time-consuming.
[0015] Another objective of the present disclosure is to incorporate manual inputs such as bedtime, wake-up time, and the number of awakenings to enhance the accuracy of sleep health analysis and provide personalized insights.
[0016] Another objective of the present disclosure is to generate personalized sleep improvement recommendations based on individual lifestyle factors, environmental conditions, and medical history to promote better sleep habits.
[0017] Another objective of the present disclosure is to provide periodic reviews and continuous health monitoring to track long-term sleep patterns and ensure proactive intervention in case of potential health concerns.
[0018] Another objective of the present disclosure is to facilitate early detection of medical and mental health conditions linked to poor sleep quality, such as sleep apnea, insomnia, and stress-related disorders, through advanced data analysis techniques.
[0019] Yet another objective of the present disclosure is to develop a user-friendly interface that is allowing individuals to access sleep reports, health predictions, and personalized recommendations in an easily understandable format.
[0020] Yet another objective of the present disclosure is to improve overall sleep health management by integrating real-time data, predictive analytics, and automated alerts to encourage timely medical consultation when necessary.
[0021] In light of the above, in one aspect of the present disclosure, an automated sleep disorder detection, monitoring, and prediction system is disclosed herein. The system comprises a wearable device, the wearable device configured to continuously collect physiological and movement data of a user, the wearable device comprising. The system includes a pulse oximeter, operatively connected to the wearable device and configured to measure and transmit oxygen saturation levels, detecting possible sleep apnea events or respiratory distress. The system also includes an accelerometer, operatively connected to the wearable device and configured to detect motion patterns, sleep posture, and frequency of nocturnal awakenings. The system also includes a communication network, operatively connected to the wearable device, the communication network configured to facilitate bi-directional data exchange using Bluetooth, Wi-Fi, and cloud-based synchronization protocols. The system also includes a plurality of sensors, operatively connected to the communication network and configured to detect and transmit external environmental conditions affecting sleep quality, the plurality of sensors comprising. The system also includes a heart rate sensor, configured to monitor and transmit real-time heart rate variability data to detect irregularities in cardiovascular activity during sleep. The system also includes a temperature sensor, configured to track and transmit body temperature variations associated with sleep disturbances. The system also includes a light sensor, configured to detect brightness levels in the sleeping environment, ensuring assessment of light-induced sleep disruptions. The system also includes a sound sensor, configured to detect ambient noise levels, identifying disturbances caused by external sounds. The system also includes a humidity sensor, configured to track and transmit variations in air moisture content affecting respiratory comfort during sleep. The system also includes a processing unit, operatively connected to the communication network, the processing unit configured to analyze collected physiological, environmental, and behavioral data using machine learning algorithms to predict sleep quality, identify sleep disorders, and generate personalized recommendations, the processing unit comprising. The system also includes a data aggregation module, operatively connected to the processing unit and configured to preprocess and standardize raw data received from the wearable device and external sensors. The system also includes a machine learning module, operatively connected to the data aggregation module and configured to analyze historical and real-time sleep data using predictive analytics models, identifying potential sleep disorders, including insomnia, obstructive sleep apnea, and circadian rhythm disruptions. The system also includes a sleep health prediction module, operatively connected to the machine learning module and configured to generate risk assessments, determine long-term sleep quality trends, and recommend medical consultation based on detected anomalies in sleep patterns. The system also includes an alert generation module, operatively connected to the sleep health prediction module and configured to transmit critical alerts. The system also includes a user interface, operatively connected to the processing unit and configured to display real-time sleep analysis reports, sleep trend graphs, and predictive health insights, wherein the user interface is further configured to provide personalized sleep hygiene recommendations and medical consultation alerts via a mobile computing device including a smart phone or tablet. The system also includes a storage unit, operatively connected to the processing unit and configured to securely store collected and processed sleep data in an encrypted format, wherein the storage unit is configured to facilitate periodic retrieval of historical sleep records for comparative analysis and long-term sleep health monitoring.
[0022] In one embodiment, the pulse oximeter in the wearable device is configured to continuously measure oxygen saturation levels at predefined intervals, detect sudden drops indicative of respiratory distress, and transmit real-time alerts to the processing unit for sleep apnea detection.
[0023] In one embodiment, the accelerometer in the wearable device is configured to analyze sleep posture and nocturnal movements to classify sleep stages, detect restless leg syndrome, and provide movement frequency trends through the user interface.
[0024] In one embodiment, the machine learning module in the processing unit is configured to apply deep learning algorithms for recognizing complex sleep patterns, predicting potential sleep disorders based on multi-sensor data fusion, and refining analysis accuracy through adaptive learning.
[0025] In one embodiment, the heart rate sensor in the plurality of sensors is configured to detect variations in heart rate variability and correlate findings with sleep stress levels, autonomic nervous system activity, and early indicators of cardiovascular abnormalities.
[0026] In one embodiment, the communication network is configured to automatically synchronize collected physiological and environmental data with a cloud-based storage unit, enabling secure remote access and long-term trend analysis for sleep health monitoring.
[0027] In one embodiment, the alert generation module in the processing unit is configured to transmit personalized sleep improvement recommendations via push notifications to the user interface, wherein the recommendations include optimal sleep duration, bedtime adjustments, and lifestyle modifications.
[0028] In one embodiment, the user interface is configured to generate interactive visual reports with sleep score assessments, comparative sleep efficiency graphs, and AI-driven insights for guiding users toward healthier sleep patterns based on historical trends.
[0029] In light of the above, in one aspect of the present disclosure, a method for automated sleep disorder detection, monitoring, and prediction is disclosed herein. The method comprises collecting, by the wearable device, physiological and movement data. The method includes transmitting, by the communication network enables real-time bi-directional data exchange using Bluetooth, Wi-Fi, and cloud protocols. The method also includes receiving, by the processing unit, environmental data from the plurality of sensors. The method also includes analyzing, by a machine learning module, real-time and historical sleep data. The method also includes generating, by a sleep health prediction module, personalized sleep assessments and medical consultation recommendations based on detected anomalies. The method also includes transmitting, by an alert generation module, sleep reports and improvement recommendations to a user interface on a smart phone or tablet. The method also includes storing, by a storage unit, encrypted sleep data for periodic retrieval and long-term trend analysis.
[0030] In one embodiment, the machine learning module, comprises classifying sleep stages into light sleep, deep sleep, and rapid eye movement (REM) sleep, detecting sleep disruptions based on variations in heart rate, oxygen saturation, and motion data, and predicting potential sleep disorders including insomnia, obstructive sleep apnea, and circadian rhythm disruptions by comparing real-time sleep patterns with historical sleep data.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of a automated sleep disorder detection, monitoring, and prediction system, in accordance with an exemplary embodiment of the present disclosure;
[0037] FIG. 2 illustrates a flow chart of a method for automated sleep disorder detection, monitoring, and prediction, in accordance with an exemplary embodiment of the present disclosure;
[0038] FIG. 3 illustrates an architectural flow diagram of intelligent sleep analysis system for smart sleep monitoring & health prediction, in accordance with an exemplary embodiment of the present disclosure.
[0039] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0040] The automated sleep disorder detection, monitoring, and prediction system and method thereof is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0041] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0042] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0043] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0044] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0045] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0046] Referring now to FIG. 1 to FIG. 3 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an automated sleep disorder detection, monitoring, and prediction system, in accordance with an exemplary embodiment of the present disclosure.
[0047] The system 100 includes a wearable device 102, the wearable device 102 configured to continuously collect physiological and movement data of a user, the wearable device 102 comprising, a pulse oximeter 104, operatively connected to the wearable device 102 and configured to measure and transmit oxygen saturation levels, detecting possible sleep apnea events or respiratory distress, an accelerometer 106, operatively connected to the wearable device 102 and configured to detect motion patterns, sleep posture, and frequency of nocturnal awakenings, a communication network 108, operatively connected to the wearable device 102, the communication network 108 configured to facilitate bi-directional data exchange using Bluetooth, Wi-Fi, and cloud-based synchronization protocols, a plurality of sensors 110, operatively connected the communication network 108 and configured to detect and transmit external environmental conditions affecting sleep quality, the plurality of sensors 110 comprising, a heart rate sensor 112, configured to monitor and transmit real-time heart rate variability data to detect irregularities in cardiovascular activity during sleep, a temperature sensor 114, configured to track and transmit body temperature variations associated with sleep disturbances, an light sensor 116, configured to detect brightness levels in the sleeping environment, ensuring assessment of light-induced sleep disruptions, a sound sensor 118, configured to detect ambient noise levels, identifying disturbances caused by external sounds, a humidity sensor 120, configured to track and transmit variations in air moisture content affecting respiratory comfort during sleep, a processing unit 122, operatively connected to the communication network 108, the processing unit 122 configured to analyze collected physiological, environmental, and behavioral data using machine learning algorithms to predict sleep quality, identify sleep disorders, and generate personalized recommendations, the processing unit 122 comprising, a data aggregation module 124, operatively connected to the processing unit 122 and configured to preprocess and standardize raw data received from the wearable device 102 and external sensors, a machine learning module 126, operatively connected to the data aggregation module 124 and configured to analyze historical and real-time sleep data using predictive analytics models, identifying potential sleep disorders, a sleep health prediction module 128, operatively connected to the machine learning module 126 and configured to generate risk assessments, determine long-term sleep quality trends, and recommend medical consultation based on detected anomalies in sleep patterns, an alert generation module 130, operatively connected to the sleep health prediction module 128 and configured to transmit critical alerts, a user interface 132, operatively connected to the processing unit 122 and configured to display real-time sleep analysis reports, sleep trend graphs, and predictive health insights, wherein the user interface 132 is further configured to provide personalized sleep hygiene recommendations and medical consultation alerts via a mobile computing device including a smart phone or tablet, a storage unit 134, operatively connected to the processing unit 122 and configured to securely store collected and processed sleep data in an encrypted format, wherein the storage unit 134 is configured to facilitate periodic retrieval.
[0048] The pulse oximeter 104 in the wearable device 102 is configured to continuously measure oxygen saturation levels at predefined intervals, detect sudden drops indicative of respiratory distress, and transmit real-time alerts to the processing unit 122 for sleep apnea detection.
[0049] The accelerometer 106 in the wearable device 102 is configured to analyze sleep posture and nocturnal movements to classify sleep stages, detect restless leg syndrome, and provide movement frequency trends through the user interface 132.
[0050] The machine learning module 126 in the processing unit 122 is configured to apply deep learning algorithms for recognizing complex sleep patterns, predicting potential sleep disorders based on multi-sensor data fusion, and refining analysis accuracy through adaptive learning.
[0051] The heart rate sensor 112 in the plurality of sensors 110 is configured to detect variations in heart rate variability and correlate findings with sleep stress levels, autonomic nervous system activity, and early indicators of cardiovascular abnormalities.
[0052] The communication network 108 is configured to automatically synchronize collected physiological and environmental data with a cloud-based storage unit 134, enabling secure remote access and long-term trend analysis for sleep health monitoring.
[0053] The alert generation module 130 in the processing unit 122 is configured to transmit personalized sleep improvement recommendations via push notifications to the user interface 132, wherein the recommendations include optimal sleep duration, bedtime adjustments, and lifestyle modifications.
[0054] The user interface 132 is configured to generate interactive visual reports with sleep score assessments, comparative sleep efficiency graphs, and AI driven insights for guiding users toward healthier sleep patterns based on historical trends.
[0055] The method 100 may include collecting, by the wearable device 102, physiological and movement data, transmitting, by the communication network 108 enables real-time bi-directional data exchange using Bluetooth, Wi-Fi, and cloud protocols, receiving, by the processing unit 122, environmental data from the plurality of sensors 110, analyzing, by a machine learning module 126, real-time and historical sleep data, generating, by a sleep health prediction module 128, personalized sleep assessments and medical consultation recommendations based on detected anomalies, transmitting, by an alert generation module 130, sleep reports and improvement recommendations to a user interface 132 on a smart phone or tablet, storing, by a storage unit 134, encrypted sleep data for periodic retrieval and long-term trend analysis.
[0056] The machine learning module 126 comprises classifying sleep stages into light sleep, deep sleep, and rapid eye movement REM sleep, detecting sleep disruptions based on variations in heart rate, oxygen saturation, and motion data, and predicting potential sleep disorders including insomnia, obstructive sleep apnea, and circadian rhythm disruptions by comparing real-time sleep patterns with historical sleep data.
[0057] The wearable device 102 is configured to continuously collect physiological and movement data of a user to facilitate sleep analysis and health prediction. The wearable device 102 is operatively connected to the communication network 108 to ensure seamless data transmission. The wearable device 102 comprises the pulse oximeter 104 and the accelerometer 106, both of which are essential for gathering real-time biometric and movement data. The wearable device 102 is designed to be worn on the user’s body, ensuring continuous monitoring without disrupting the user’s sleep. The wearable device 102 is equipped with low-power sensors that optimize battery usage while maintaining high accuracy in data collection. The wearable device 102 integrates advanced signal processing algorithms to enhance the accuracy of physiological data measurement, ensuring that any deviations in normal patterns are detected promptly. The wearable device 102 maintains secure communication with the communication network 108 to facilitate encrypted data transfer, ensuring that user information remains protected during transmission.
[0058] The pulse oximeter 104 is operatively connected to the wearable device 102 and is configured to measure and transmit oxygen saturation levels to detect potential respiratory distress or sleep apnea events. The pulse oximeter 104 uses photoplethysmography technology to analyze the absorption of light by blood vessels and determine blood oxygen levels. The pulse oximeter 104 continuously monitors variations in oxygen saturation to identify periods of low oxygenation that may indicate obstructive sleep apnea. The pulse oximeter 104 is calibrated to minimize errors caused by motion artifacts, ensuring reliable readings during sleep. The pulse oximeter 104 transmits collected data to the processing unit 122 via the communication network 108 for further analysis. The pulse oximeter 104 plays a crucial role in identifying irregular breathing patterns that may signal serious sleep-related health issues. The pulse oximeter 104 works in conjunction with the heart rate sensor 112 to provide a comprehensive understanding of cardiovascular activity during sleep.
[0059] The accelerometer 106 is operatively connected to the wearable device 102 and is configured to detect motion patterns, sleep posture, and the frequency of nocturnal awakenings. The accelerometer 106 measures movement intensity and direction to classify sleep stages based on user activity levels. The accelerometer 106 differentiates between deep sleep, light sleep, and wakefulness by analyzing variations in motion signals. The accelerometer 106 is highly sensitive to minor movements, ensuring that even subtle body shifts during sleep are accurately recorded. The accelerometer 106 transmits movement data to the processing unit 122 via the communication network 108 to contribute to sleep stage classification. The accelerometer 106 operates with low power consumption to ensure prolonged usage without frequent recharging. The accelerometer 106 enhances sleep tracking accuracy by correlating motion data with physiological parameters collected by other sensors. The accelerometer 106 detects instances of restlessness, which may indicate discomfort or underlying sleep disorders.
[0060] The communication network 108 is operatively connected to the wearable device 102 and is configured to facilitate bi-directional data exchange using Bluetooth, Wi-Fi, and cloud-based synchronization protocols. The communication network 108 ensures seamless connectivity between the wearable device 102 and the processing unit 122, allowing real-time data transmission. The communication network 108 encrypts transmitted data to maintain user privacy and prevent unauthorized access. The communication network 108 optimizes bandwidth usage to ensure efficient data transfer without latency. The communication network 108 enables cloud synchronization, allowing users to access their sleep data across multiple devices. The communication network 108 integrates with mobile applications to provide real-time updates and insights regarding sleep patterns. The communication network 108 maintains stable connections with multiple sensors to ensure uninterrupted data flow for comprehensive sleep analysis.
[0061] The plurality of sensors 110 is operatively connected to the communication network 108 and is configured to detect and transmit external environmental conditions affecting sleep quality. The plurality of sensors 110 comprises the heart rate sensor 112, temperature sensor 114, light sensor 116, sound sensor 118, and humidity sensor 120, each of which plays a crucial role in analyzing sleep environments. The plurality of sensors 110 provides real-time updates on environmental conditions to determine external factors influencing sleep disturbances. The plurality of sensors 110 transmits collected data to the processing unit 122, ensuring comprehensive sleep analysis. The plurality of sensors 110 is calibrated to maintain high accuracy in varying environmental conditions. The plurality of sensors 110 enhances the predictive capabilities of the system by correlating physiological responses with environmental factors. The plurality of sensors 110 enables real-time adjustments and recommendations based on detected changes in the sleeping environment.
[0062] The heart rate sensor 112 is configured to monitor and transmit real-time heart rate variability data to detect irregularities in cardiovascular activity during sleep. The heart rate sensor 112 provides continuous tracking of heart rate fluctuations to assess autonomic nervous system activity. The heart rate sensor 112 identifies instances of bradycardia or tachycardia, which may indicate underlying health issues. The heart rate sensor 112 works in conjunction with the pulse oximeter 104 to analyze correlations between oxygen saturation levels and heart rate patterns. The heart rate sensor 112 transmits collected data to the processing unit 122 for further analysis. The heart rate sensor 112 enhances the system’s capability to detect stress-induced sleep disturbances. The heart rate sensor 112 maintains high accuracy under varying sleep conditions to ensure reliable health assessments.
[0063] The temperature sensor 114 is configured to track and transmit body temperature variations associated with sleep disturbances. The temperature sensor 114 provides continuous temperature readings to detect deviations from normal sleeping patterns. The temperature sensor 114 helps in identifying fever-induced sleep interruptions or circadian rhythm disruptions. The temperature sensor 114 works in conjunction with the humidity sensor 120 to assess thermal comfort during sleep. The temperature sensor 114 transmits collected data to the processing unit 122 via the communication network 108. The temperature sensor 114 enables predictive analysis of temperature-related sleep disturbances. The temperature sensor 114 is optimized to function efficiently under diverse sleeping conditions. The temperature sensor 114 ensures high precision in temperature monitoring for accurate sleep assessment.
[0064] The light sensor 116 is configured to detect brightness levels in the sleeping environment, ensuring assessment of light-induced sleep disruptions. The light sensor 116 continuously measures variations in ambient light to determine exposure levels. The light sensor 116 detects sudden changes in illumination that may affect sleep continuity. The light sensor 116 transmits collected data to the processing unit 122 for real-time analysis. The light sensor 116 integrates with the alert generation module 130 to provide recommendations for optimal sleeping conditions. The light sensor 116 ensures accurate assessment of artificial and natural light exposure. The light sensor 116 enables adaptive recommendations based on light intensity trends. The light sensor 116 works in conjunction with other environmental sensors to provide comprehensive sleep quality analysis.
[0065] The sound sensor 118 is configured to detect ambient noise levels, identifying disturbances caused by external sounds. The sound sensor 118 continuously monitors variations in noise levels to assess potential sleep disruptions. The sound sensor 118 transmits collected data to the processing unit 122 via the communication network 108. The sound sensor 118 enables early detection of snoring patterns, assisting in sleep disorder analysis. The sound sensor 118 integrates with the alert generation module 130 to provide noise-related sleep improvement recommendations. The sound sensor 118 ensures high precision in sound intensity detection. The sound sensor 118 operates efficiently under diverse acoustic conditions for reliable sleep assessment.
[0066] The humidity sensor 120 is configured to track and transmit variations in air moisture content affecting respiratory comfort during sleep. The humidity sensor 120 provides real-time assessment of humidity levels to determine optimal sleeping conditions. The humidity sensor 120 transmits collected data to the processing unit 122 for analysis. The humidity sensor 120 integrates with other environmental sensors to provide holistic sleep quality assessments. The humidity sensor 120 enables predictive analysis of humidity-induced sleep discomfort. The humidity sensor 120 ensures high accuracy in air moisture detection. The humidity sensor 120 supports personalized sleep recommendations based on humidity trends.
[0067] The processing unit 122 is operatively connected to the communication network 108 and is configured to analyze collected physiological, environmental, and behavioral data using machine learning algorithms to predict sleep quality, identify sleep disorders, and generate personalized recommendations. The processing unit 122 receives real-time data from the wearable device 102, the plurality of sensors 110, and the communication network 108. The processing unit 122 integrates multi-source data to ensure comprehensive sleep analysis and health prediction. The processing unit 122 performs complex computations to detect anomalies in sleep patterns and provide risk assessments. The processing unit 122 is optimized for high-speed processing to ensure minimal latency in data analysis. The processing unit 122 transmits analyzed data to the user interface 132 to provide real-time insights. The processing unit 122 encrypts all processed information before sending it to the storage unit 134 for secure record-keeping.
[0068] The data aggregation module 124 is operatively connected to the processing unit 122 and is configured to preprocess and standardize raw data received from the wearable device 102 and external sensors. The data aggregation module 124 filters out noise and inconsistencies in the collected data to ensure accuracy. The data aggregation module 124 normalizes physiological and environmental parameters for effective machine learning analysis. The data aggregation module 124 integrates data from multiple sources, including the pulse oximeter 104, accelerometer 106, heart rate sensor 112, temperature sensor 114, light sensor 116, sound sensor 118, and humidity sensor 120. The data aggregation module 124 ensures the reliability of processed information by eliminating erroneous values. The data aggregation module 124 structures data in a format suitable for predictive analysis. The data aggregation module 124 transmits preprocessed data to the machine learning module 126 for further evaluation. The data aggregation module 124 enhances the overall accuracy of sleep quality assessment by ensuring data consistency.
[0069] The machine learning module 126 is operatively connected to the data aggregation module 124 and is configured to analyze historical and real-time sleep data using predictive analytics models, identifying potential sleep disorders. The machine learning module 126 applies advanced algorithms to detect abnormalities in sleep patterns. The machine learning module 126 continuously learns from historical sleep data to improve prediction accuracy. The machine learning module 126 correlates physiological and environmental data to identify factors affecting sleep quality. The machine learning module 126 enhances early detection of sleep disorders such as insomnia, obstructive sleep apnea, and circadian rhythm disruptions. The machine learning module 126 refines its models based on user-specific sleep trends to provide personalized assessments. The machine learning module 126 transmits predictive insights to the sleep health prediction module 128 for further evaluation. The machine learning module 126 ensures adaptive learning capabilities for continuous improvement in sleep disorder detection.
[0070] The sleep health prediction module 128 is operatively connected to the machine learning module 126 and is configured to generate risk assessments, determine long-term sleep quality trends, and recommend medical consultation based on detected anomalies in sleep patterns. The sleep health prediction module 128 evaluates deviations in sleep metrics to detect early signs of potential health risks. The sleep health prediction module 128 integrates findings from the machine learning module 126 to assess overall sleep health. The sleep health prediction module 128 provides a long-term analysis of sleep patterns by comparing current data with historical trends. The sleep health prediction module 128 categorizes sleep disturbances based on severity to prioritize medical recommendations. The sleep health prediction module 128 personalizes sleep improvement suggestions to optimize rest quality. The sleep health prediction module 128 transmits analyzed reports to the alert generation module 130 for user notification. The sleep health prediction module 128 ensures accurate health predictions through continuous monitoring and analysis.
[0071] The alert generation module 130 is operatively connected to the sleep health prediction module 128 and is configured to transmit critical alerts and sleep improvement recommendations to a user interface 132. The alert generation module 130 processes detected anomalies in sleep behavior and converts them into actionable insights. The alert generation module 130 prioritizes alerts based on the severity of detected sleep irregularities. The alert generation module 130 delivers real-time notifications to inform users of potential health concerns. The alert generation module 130 provides personalized sleep hygiene recommendations based on identified sleep issues. The alert generation module 130 ensures timely medical consultation alerts for cases requiring expert intervention. The alert generation module 130 transmits sleep improvement suggestions directly to the user interface 132 for user review. The alert generation module 130 enhances user awareness of sleep quality trends through instant notifications.
[0072] The user interface 132 is operatively connected to the processing unit 122 and is configured to display real-time sleep analysis reports, sleep trend graphs, and predictive health insights. The user interface 132 provides an interactive platform for users to monitor their sleep data. The user interface 132 ensures accessibility across multiple devices, including smartphones and tablets. The user interface 132 visualizes sleep patterns using graphs and reports for easy interpretation. The user interface 132 provides real-time updates on sleep health based on data received from the alert generation module 130. The user interface 132 allows users to review historical sleep data for long-term analysis. The user interface 132 integrates with mobile applications to provide remote access to sleep monitoring insights. The user interface 132 ensures a user-friendly experience by simplifying complex sleep data into understandable metrics.
[0073] The storage unit 134 is operatively connected to the processing unit 122 and is configured to securely store collected and processed sleep data in an encrypted format. The storage unit 134 ensures secure long-term retention of sleep records for future reference. The storage unit 134 facilitates periodic retrieval of historical sleep records for comparative analysis. The storage unit 134 encrypts stored data to maintain user privacy and data security. The storage unit 134 allows access to past sleep assessments for tracking sleep health trends. The storage unit 134 optimizes storage space by efficiently organizing recorded sleep data. The storage unit 134 integrates with cloud-based systems to provide remote data backup and synchronization. The storage unit 134 ensures seamless retrieval of sleep insights for long-term sleep health monitoring.
[0074] FIG. 2 illustrates a flow chart of a method for automated sleep disorder detection, monitoring, and prediction, in accordance with an exemplary embodiment of the present disclosure.
[0075] At 202, collect, by the wearable device, physiological and movement data.
[0076] At 204, transmit, by the communication network enables real-time bi-directional data exchange using Bluetooth, Wi-Fi, and cloud protocols.
[0077] At 206, receive, by the processing unit, environmental data from the plurality of sensors.
[0078] At 208, analyzing, by a machine learning module, real-time and historical sleep data.
[0079] At 210, generate, by a sleep health prediction module, personalized sleep assessments and medical consultation recommendations based on detected anomalies.
[0080] At 212, transmit, by an alert generation module, sleep reports and improvement recommendations to a user interface on a smart phone or tablet.
[0081] At 214, store, by a storage unit, encrypted sleep data for periodic retrieval and long-term trend analysis.
[0082] FIG. 3 illustrates an architectural flow diagram of intelligent sleep analysis system for smart sleep monitoring & health prediction, in accordance with an exemplary embodiment of the present disclosure.
[0083] The data source 302 is configured to collect and integrate multiple sleep-related data streams to ensure comprehensive monitoring and analysis of sleep patterns and potential sleep disorders. The data source 302 connects various hardware and software components to extract relevant physiological, environmental, and behavioral information for sleep quality assessment. The data source 302 ensures seamless communication between different input sources such as wearable devices 304, sleep tracking apps 306, actigraphy device 308, and manual inputs 310. The data source 302 processes information in real time and transmits it to the data pre-processing 312 stage for further refinement.
[0084] The wearable devices 304 are configured to continuously collect physiological and movement data using advanced biosensors. The wearable devices 304 include fitness trackers and smart rings such as Fit bit and Oura Ring to monitor real-time parameters including heart rate variability, blood oxygen levels, motion patterns, and body temperature. The wearable devices 304 track nocturnal awakenings, sleep posture, and physical restlessness, ensuring accurate sleep disorder detection. The wearable devices 304 transmit raw physiological data to the data source 302 via wireless communication protocols. The wearable devices 304 enhance sleep tracking accuracy by providing real-time insights into physiological changes during different sleep stages. The wearable devices 304 continuously update sleep-related metrics for comprehensive health assessment.
[0085] The sleep tracking apps 306 are configured to monitor and record sleep duration sleep cycles, and snoring patterns using mobile applications such as Sleep Cycle and Snore Lab. The sleep tracking apps 306 utilize smart phone sensors and microphones to detect sleep disturbances and environmental factors affecting sleep quality. The sleep tracking apps 306 integrate data from wearable devices 304 to improve analysis accuracy. The sleep tracking apps 306 enable users to log lifestyle habits and bedtime routines, ensuring a personalized sleep assessment. The sleep tracking apps 306 identify irregularities such as fragmented sleep, early awakenings, and variations in sleep consistency. The sleep tracking apps 306 transmit recorded sleep metrics to the data source 302 for centralized processing. The sleep tracking apps 306 provide essential inputs for sleep disorder prediction and personalized recommendations.
[0086] The actigraphy device 308 is configured to measure sleep quality and monitor sleep cycles using motion-detecting sensors. The actigraphy device 308 tracks activity levels and rest periods to determine sleep efficiency. The actigraphy device 308 records sleep-wake patterns and identifies potential disruptions in circadian rhythm. The actigraphy device 308 provides detailed insights into sleep architecture, distinguishing between light sleep, deep sleep, and rapid eye movement sleep. The actigraphy device 308 operates continuously to collect long-term sleep behavior data. The actigraphy device 308 integrates with wearable devices 304 and sleep tracking apps 306 to ensure multi-source validation of sleep quality analysis. The actigraphy device 308 transmits recorded data to the data source 302 for further evaluation.
[0087] The manual inputs 310 are configured to capture user-provided information related to bedtime, wake-up time, and number of awakenings. The manual inputs 310 ensure the inclusion of subjective sleep experience and lifestyle habits in sleep quality assessment. The manual inputs 310 allow users to record perceived sleep disturbances, daytime fatigue, and external factors such as caffeine intake, medication use, and stress levels. The manual inputs 310 contribute to refining sleep disorder predictions by providing behavioral context to automated data collection methods. The manual inputs 310 are securely stored and analyzed alongside physiological and environmental data. The manual inputs 310 enhance the adaptability of the sleep monitoring system by incorporating user-reported experiences. The manual inputs 310 ensure a holistic approach to sleep assessment by integrating both objective and subjective data.
[0088] The data pre-processing 312 is configured to refine and standardize collected data from multiple sources for machine learning analysis. The data pre-processing 312 filters out noise and artefacts from physiological, environmental, and behavioral inputs. The data pre-processing 312 normalizes recorded sleep metrics to ensure consistency across different data collection devices. The data pre-processing 312 integrates structured and unstructured data to optimize predictive model accuracy. The data pre-processing 312 transmits cleaned and formatted data to the prediction machine learning model 314 for further analysis. The data pre-processing 312 ensures high-quality data input to improve sleep disorder detection and recommendation accuracy.
[0089] The prediction machine learning model 314 is configured to analyze historical and real-time sleep data using advanced predictive analytics. The prediction machine learning model 314 identifies deviations in sleep behavior patterns that indicate the presence of potential sleep disorders. The prediction machine learning model 314 correlates physiological, environmental and behavioral data to generate sleep quality assessments. The prediction machine learning model 314 adapts to individual sleep trends to improve personalized recommendations. The prediction machine learning model 314 continuously updates its learning algorithms based on new sleep data. The prediction machine learning model 314 transmits predicted outcomes to the evaluate the prediction of sleep disorder 316 stage for further assessment.
[0090] The evaluate the prediction of sleep disorder 316 is configured to determine whether the identified sleep patterns indicate a potential disorder. The evaluate the prediction of sleep disorder 316 cross-verifies results from multiple data sources to ensure prediction accuracy. The evaluate the prediction of sleep disorder 316 classifies detected anomalies based on severity and risk factors. The evaluate the prediction of sleep disorder 316 provides a conclusive assessment of whether the detected sleep irregularities require medical intervention. The evaluate the prediction of sleep disorder 316 ensures reliable identification of sleep disorders for timely intervention.
[0091] The affected or not 318 is configured to categorize users based on the presence or absence of sleep disorders. The affected or not 318 directs users with normal sleep patterns to continuous evaluation 322 for periodic sleep health monitoring. The affected or not 318 identifies affected users who require medical attention and directs them to the visit the doctor healthcare provider 320 stage. The affected or not 318 ensures efficient decision-making for timely intervention and health management.
[0092] The visit the doctor healthcare provider 320 is configured to generate alerts recommending medical consultation for users identified as affected. The visit the doctor healthcare provider 320 provides users with detailed reports on detected sleep disorders for clinical assessment. The visit the doctor healthcare provider 320 enables early medical intervention to prevent worsening sleep-related health issues. The visit the doctor healthcare provider 320 ensures that affected individuals receive expert medical guidance for treatment and management.
[0093] The continuously evaluated periodical review 322 is configured to monitor users classified as not affected to ensure ongoing sleep health. The continuously evaluated periodical review 322 periodically reassesses sleep patterns to detect emerging anomalies. The continuously evaluated periodical review 322 provides routine feedback and sleep hygiene recommendations to maintain optimal sleep quality. The continuously evaluated periodical review 322 integrates continuous monitoring strategies to ensure proactive health management. The continuously evaluated periodical review 322 ensures long-term well-being by tracking evolving sleep trends and updating predictive models accordingly.
[0094] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0095] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0096] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0097] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0098] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A automated sleep disorder detection, monitoring, and prediction system (100), the system (100) comprising:
a wearable device (102), the wearable device (102) configured to continuously collect physiological and movement data of a user, the wearable device (102) comprising:
a pulse oximeter (104), operatively connected to the wearable device (102) and configured to measure and transmit oxygen saturation levels, detecting possible sleep apnea events or respiratory distress;
an accelerometer (106), operatively connected to the wearable device (102) and configured to detect motion patterns, sleep posture, and frequency of nocturnal awakenings;
a communication network (108), operatively connected to the wearable device (102), the communication network (108) configured to facilitate bi-directional data exchange using Bluetooth, Wi-Fi, and cloud-based synchronization protocols;
a plurality of sensors (110), operatively connected the communication network (108) and configured to detect and transmit external environmental conditions affecting sleep quality, the plurality of sensors (110) comprising:
a heart rate sensor (112), configured to monitor and transmit real-time heart rate variability data to detect irregularities in cardiovascular activity during sleep;
a temperature sensor (114), configured to track and transmit body temperature variations associated with sleep disturbances;
an light sensor (116), configured to detect brightness levels in the sleeping environment, ensuring assessment of light-induced sleep disruptions;
a sound sensor (118), configured to detect ambient noise levels, identifying disturbances caused by external sounds;
a humidity sensor (120), configured to track and transmit variations in air moisture content affecting respiratory comfort during sleep;
a processing unit (122), operatively connected to the communication network (108), the processing unit (122) configured to analyze collected physiological, environmental, and behavioral data using machine learning algorithms to predict sleep quality, identify sleep disorders, and generate personalized recommendations, the processing unit (122) comprising;
a data aggregation module (124), operatively connected to the processing unit (122) and configured to preprocess and standardize raw data received from the wearable device (102) and external sensors;

a machine learning module (126), operatively connected to the data aggregation module (124) and configured to analyze historical and real-time sleep data using predictive analytics models, identifying potential sleep disorders;

a sleep health prediction module (128), operatively connected to the machine learning module (126) and configured to generate risk assessments, determine long-term sleep quality trends, and recommend medical consultation based on detected anomalies in sleep patterns;

an alert generation module (130), operatively connected to the sleep health prediction module (128) and configured to transmit critical alerts;
a user interface (132), operatively connected to the processing unit (122) and configured to display real-time sleep analysis reports, sleep trend graphs, and predictive health insights, wherein the user interface (132) is further configured to provide personalized sleep hygiene recommendations and medical consultation alerts via a mobile computing device including a smart phone or tablet;
a storage unit (134), operatively connected to the processing unit (122) and configured to securely store collected and processed sleep data in an encrypted format, wherein the storage unit (134) is configured to facilitate periodic retrieval.
2. The system (100) as claimed in claim 1, wherein the pulse oximeter (104) in the wearable device (102) is configured to continuously measure oxygen saturation levels at predefined intervals, detect sudden drops indicative of respiratory distress, and transmit real-time alerts to the processing unit (122) for sleep apnea detection.
3. The system (100) as claimed in claim 1, wherein the accelerometer (106) in the wearable device (102) is configured to analyze sleep posture and nocturnal movements to classify sleep stages, detect restless leg syndrome, and provide movement frequency trends through the user interface (132).
4. The system (100) as claimed in claim 1, wherein the machine learning module (126) in the processing unit (122) is configured to apply deep learning algorithms for recognizing complex sleep patterns, predicting potential sleep disorders based on multi-sensor data fusion, and refining analysis accuracy through adaptive learning.
5. The system (100) as claimed in claim 1, wherein the heart rate sensor (112) in the plurality of sensors (110) is configured to detect variations in heart rate variability and correlate findings with sleep stress levels, autonomic nervous system activity, and early indicators of cardiovascular abnormalities.
6. The system (100) as claimed in claim 1, wherein the communication network (108) is configured to automatically synchronize collected physiological and environmental data with a cloud-based storage unit (134), enabling secure remote access and long-term trend analysis for sleep health monitoring.
7. The system (100) as claimed in claim 1, wherein the alert generation module (130) in the processing unit (122) is configured to transmit personalized sleep improvement recommendations via push notifications to the user interface (132), wherein the recommendations include optimal sleep duration, bedtime adjustments, and lifestyle modifications.
8. The system (100) as claimed in claim 1, wherein the user interface (132) is configured to generate interactive visual reports with sleep score assessments, comparative sleep efficiency graphs, and AI-driven insights for guiding users toward healthier sleep patterns based on historical trends.
9. A method for automated sleep disorder detection, monitoring, and prediction, the method (100) comprising:
collecting, by the wearable device (102), physiological and movement data;
transmitting, by the communication network (108) enables real-time bi-directional data exchange using Bluetooth, Wi-Fi, and cloud protocols;
receiving, by the processing unit (122), environmental data from the plurality of sensors (110);
analyzing, by a machine learning module (126), real-time and historical sleep data;
generating, by a sleep health prediction module (128), personalized sleep assessments and medical consultation recommendations based on detected anomalies;
transmitting, by an alert generation module (130), sleep reports and improvement recommendations to a user interface (132) on a smart phone or tablet;
storing, by a storage unit (134), encrypted sleep data for periodic retrieval and long-term trend analysis.
10. The method (100) as claimed in claim 9, wherein the analyzing, by the machine learning module (126), comprises classifying sleep stages into light sleep, deep sleep, and rapid eye movement (REM) sleep, detecting sleep disruptions based on variations in heart rate, oxygen saturation, and motion data, and predicting potential sleep disorders including insomnia, obstructive sleep apnea, and circadian rhythm disruptions by comparing real-time sleep patterns with historical sleep data.