283 results about "Sleep Stages" patented technology

The invention relates to the field of sleep monitoring, in particular to a sleep monitoring visual analysis method and system based on a smart bracelet. The method comprises the following steps: extracting a sleep physiological signal of a wrist part of a user based on a smart bracelet; sliding time window analysis and multi-mode physiological state sensing are carried out, and a dynamic sleep state sensing map is constructed; performing transient mutation rhythm recognition on the dynamic sleep state sensing map, performing sleep stage evolution trend analysis, and constructing a sleep stage evolution model; the method comprises the following steps: collecting multi-dimensional parameters of a sleep environment, carrying out environment parameter change detection and environment disturbance-sleep stage change association identification, and obtaining an environment disturbance-sleep rhythm mutation association rule; environment disturbance intensity evaluation is carried out according to the association rule, and a real-time sleep state visual view is constructed. According to the method, accurate and high-readability sleep monitoring data visualization is realized, the overall sleep quality is improved, and the long-term health risk is reduced.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention relates to a multi-device cooperative control method and device based on a user state and an air conditioner. The method comprises: monitoring a sleep state of a user; and determining a sleep stage of the user based on the sleep state, and performing cooperative control on each device according to a control strategy matched with the sleep stage. The method and the device can adapt to the difference of dynamic sleep stages, and control the devices to operate cooperatively according to the control strategies matched with the different sleep stages, so that the self-adaptive adjustment and parameter adjustment precision of the devices is improved, and the problems of poor sleep mode adjustment effect and low adaptability in the prior art are solved.

The invention provides a system and method for assisting rehabilitation in sleep, and the system comprises an electroencephalogram monitoring assembly, a heart rate and breath monitoring assembly, a myoelectricity monitoring assembly and a body movement monitoring assembly which are used for obtaining physiological signals of an object; the intervention component is used for performing predetermined intervention operation when being activated; the control component is used for determining a sleep stage according to the physiological signal of the object; when the sleep stage is a deep sleep stage, determining an intervention priority score according to a day rehabilitation training target, a focus position and a stability score of the current deep sleep stage of the object; when the intervention priority score is larger than or equal to a preset threshold value, the object is in the deep sleep period at present, and the stability of the current deep sleep period meets a preset stability condition, a corresponding intervention assembly is activated. By means of the technical scheme, accurate intervention can be achieved in the sleep process, and the rehabilitation process of a patient is accelerated by effectively connecting intervention in the sleep stage with a rehabilitation training target in the daytime.

The invention belongs to the technical field of intelligent control equipment, and particularly relates to an intelligent room system for optimizing sleep and a working method thereof. The system comprises a data acquisition and monitoring module used for acquiring various physiological data and environmental parameters when a user moves; the machine learning prediction and evaluation module is used for analyzing, predicting and evaluating the collected data through a DNN technology; the environment control module is used for automatically adjusting environment parameters according to the current demand and the sleep stage of the user; the sleep feedback and adjustment module is used for matching the environment information with user perception and controlling the output of the terminal equipment; the central control and suggestion module is used for providing a sleep environment configuration suggestion for the user; and the data transmission module is used for performing wireless data transmission and communication with other modules. The intelligent sleep monitoring system has the advantages that the sleep condition of the user can be comprehensively monitored and analyzed, personalized sleep environments and suggestions are provided, and therefore the sleep quality and life quality of middle-aged and elderly people are improved.

Devices, systems, software, and methods are provided for treating sleep dysfunction in a subject using nighttime deep brain stimulation. Deep brain stimulation is performed with a neural recording device that records brain electrical signal data while the subject is sleeping. Machine learning computational models are used to detect and classify patterns of neural activity associated with different sleep features or sleep stages. An adaptive deep brain stimulation algorithm is provided that modulates stimulation parameters using intracranially classified sleep features or sleep stages to target sleep dysfunction. Methods and systems are also provided for performing closed-loop therapy with a deep brain stimulator that records brain electrical signals from subcortical or cortical neural activity associated with selected sleep features or stages and automatically adjusts deep brain stimulator settings and / or delivers deep brain electrical stimulation when pre-specified patterns of neural activity associated with a selected sleep feature or sleep stage are detected.

The invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention relates to the technical field of sleep environment regulation and control, and discloses a sleep environment self-adjustment system based on multi-source heterogeneous data. An environmental parameter acquisition module and a physiological feature acquisition module of the system respectively acquire sleep space environmental parameters and user physiological feature data in real time; after the master control system receives the two types of original data, a fusion calculation unit cleans original environment parameters to generate a standardized environment data stream, and extracts features from the original physiological feature data to generate a physiological feature time sequence; the dynamic evaluation module divides the two types of data into a plurality of sleep stage data segments according to a preset rule, and calculates an environmental parameter fluctuation index and a physiological feature deviation index; the label distribution mechanism combines the two types of indexes to generate a comprehensive comfort label of each sleep stage; an adjustment decision engine generates a global sleep environment adjustment strategy according to all labels, and an actuator control module drives environment adjustment equipment to execute; and the feedback learning unit receives the adjusted data and updates the calculation benchmark of the dynamic evaluation module.

The invention discloses a sleep classification method and system based on time-frequency combination, and belongs to the technical field of biomedical signal processing and artificial intelligence. In order to solve the problems that an existing method is high in manual dependence, insufficient in time-frequency feature fusion and low in long-time-sequence modeling efficiency, sleep stage classification is carried out mainly through automatic time-frequency feature extraction, a dynamic attention mechanism with memory enhancement and a lightweight multi-branch neural network structure. According to the method, efficient modeling and accurate classification of the multi-scale electroencephalogram signals can be achieved, the deep sleep recognition capability and the overall classification accuracy are improved, meanwhile, the model parameter quantity and the reasoning delay are remarkably reduced, and good real-time performance and clinical applicability are achieved.

The invention relates to the technical field of sleep stage classification, and particularly provides a sleep quality monitoring method fusing multi-scale features. The method comprises the following steps: preprocessing input signals, and inputting the input signals into a multi-mode sleep signal analysis network after ensuring that the signal amplitudes are consistent; capturing features of a plurality of time scales through a multi-granularity feature learning module, and carrying out feature fusion; integrating spatial information by using a spatial-temporal feature enhancement module, and mapping the three-dimensional features into a two-dimensional time sequence feature sequence; on the basis of a time context module of Mama, long-distance time dependence is modeled with linear complexity, features are averagely aggregated along the time dimension through a classification module, a classification result is output through a full connection layer and an activation function Softmax, and the method improves the accuracy of sleep stage classification while ensuring the high efficiency of sleep stage classification.

The invention discloses a single-channel electroencephalogram signal sleep stage classification method, and belongs to the technical field of deep learning, and the method comprises the steps: obtaining a single-channel electroencephalogram signal, carrying out the preprocessing of the single-channel electroencephalogram signal, and generating a signal segment with a fixed time length; performing multi-scale time-frequency feature extraction on the signal segment to generate a primary time-frequency feature, capturing a sleep stage conversion dependency relationship, and performing time sequence enhancement on the primary time-frequency feature to generate an enhanced time sequence feature; performing frequency spectrum statistical feature extraction on the signal segments to generate frequency spectrum statistical features; fusing the enhanced time sequence features and the frequency spectrum statistical features to generate a comprehensive feature vector; according to the comprehensive feature vector, probability distribution of different sleep stages is output through a main classifier, and a binary judgment result of the sleep stage with the minimum sample size is output through an auxiliary classifier. The method can solve the problems of class imbalance, signal complexity and calculation efficiency.

The invention relates to the technical field of integrated sleep management, in particular to a sleep data analysis management system based on artificial intelligence. The method comprises the following steps: segmenting a sleep cycle of a user based on brain wave data to obtain N sleep stages of the user; based on the body movement monitoring data, the pulse monitoring data and the respiration monitoring data, calculating to obtain a first sleep quality characteristic value of the sleep stage of the user, and based on the sleep posture category and a sleep quality weight parameter corresponding to the sleep posture category, determining a second sleep quality characteristic value of the sleep stage of the user; the sleep quality analysis index of the sleep cycle of the user is generated based on the sleep quality parameters of the sleep stage of the user, the sleep early warning signal is generated based on the sleep quality analysis index of the sleep cycle of the user, multi-dimensional data such as brain waves, body movement, pulse, breathing and sleeping postures can be combined, the sleep stage can be accurately divided, and the sleep early warning effect is improved. And sleep quality parameters are calculated through multi-modal data fusion, so that the accuracy and practicability of sleep monitoring are improved.

A method for treating a patient via high-frequency (HF) electrostimulation therapy at an external target body location comprises generating a tonic motor activation (TOMAC) electrostimulation waveform using a power source supplying less than 100 volts zero-to-peak voltage amplitude per phase. The waveform has a frequency of 1,000 Hz to 5,000 Hz, a zero-to-peak current amplitude of 10-50 mA, and a duty cycle greater than 25%. The waveform is configured to overcome skin capacitance, induce proprioceptive afferent activity, and induce sustained tonic motor activation of a muscle. The method involves delivering the waveform to the external target body location over a specified duration during the patient's sleep period and receiving at least one TOMAC efficacy indication. The efficacy indication may relate to sleep efficiency, sleep stage, sleep onset latency, daytime sleepiness, CPAP compliance, or patient physiological response indicating TOMAC.

The invention relates to an automatic mattress temperature adjusting method and system based on physiological data. According to the method, heart rate, body movement, respiratory rate and body temperature data of a user are collected in real time through a physiological parameter sensor in the intelligent mattress, and sleep stages of the user are identified on the basis of the data and comprise a falling asleep stage, a light sleep stage, a deep sleep stage and an awakening stage. And establishing an optimal sleep temperature database of the user, recording a corresponding relationship between physiological data of the user in different sleep stages and the mattress temperature, calculating a sleep quality score based on the body movement frequency and the deep sleep duration, and recording a compensation coefficient of the environment temperature and the mattress temperature at the same time. And determining a target adjusting temperature according to the current sleep stage of the user and the database information, and correcting the target adjusting temperature in combination with the environment temperature and the compensation coefficient to obtain a final adjusting temperature. The temperature of the mattress is dynamically adjusted to the corrected target adjusting temperature through a water circulation temperature control system arranged in the mattress, so that personalized sleep temperature control is provided.

The invention relates to an intelligent bedside instrument for relieving insomnia with assistance of sound waves. The intelligent bedside instrument comprises a sound wave adjusting module, a sound wave resonance relaxing system, a sleep state monitoring module and a personalized feedback system, the sound wave adjusting module sends specific sound waves to the sleep environment, and the sleep environment is automatically optimized based on sleep stage data monitored in real time by adjusting the frequency and intensity of the sound waves. The sound wave resonance relaxing system emits low-frequency sound waves with specific frequency into a bed head or a pillow according to the sleep state of a user, and the low-frequency sound waves are transmitted to the head and the body of the user through the resonance effect and used for relaxing tense muscles, stimulating deep nerves and helping the user to quickly relax and sleep. Through sound wave adjustment, resonance relaxation, accurate sleep state monitoring and a personalized feedback system, the sleep quality of a user is effectively improved, the user is helped to quickly enter deep sleep from a light sleep state, external interference is avoided, a sleep adjustment scheme is continuously optimized through intelligent analysis, and personalized requirements are met.

The invention provides an intelligent pillow control method and system, and belongs to the technical field of intelligent control. The method comprises the steps that an FPGA obtains a user sleeping posture data set collected by a pressure sensor array; and determining a current sleep stage based on the user sleep posture data set and a pre-trained sleep stage recognition model. According to the user sleeping posture data set, the historical sleeping posture data set and a preset cervical vertebra supporting curve, a first correction target parameter matrix corresponding to the current cervical vertebra curvature deviation value of the user is determined; wherein the first correction target parameter matrix comprises first correction target parameters corresponding to air bags in different areas of the intelligent pillow; the first correction target parameter is the airbag pressure adjustment amount dynamically constrained by the receptor dynamic frequency; and based on the current sleep stage and the first correction target parameter matrix, determining a corresponding airbag adjustment parameter group, operating an airbag driving module according to the airbag adjustment parameter group, performing airbag pressure adjustment, and updating the first correction target parameter matrix according to the real-time body movement frequency.

Methods, systems, and devices for determining a sleep age metric of a user are described. The system may receive photoplethysmogram (PPG) data throughout a time interval including a plurality of sleep intervals during which the user is asleep, input the PPG data into a first machine learning model, and classify the PPG data into a plurality of sleep stages. The system may input one or more sleep features into a second machine learning model, and the second machine learning model may output a sleep age metric associated with a sleep health of the user relative to a chronological age of the user. In some cases, the system may transmit, to a user device, an instruction to cause a graphical user interface (GUI) to display an indication of the sleep age metric and an insight for a value of the sleep age metric.

The invention relates to the technical field of smart homes, solves the problem that in the prior art, an intelligent mattress cannot help a user fall asleep quickly by adjusting music volume and massage strength in the sleeping stage of the user, and provides an intelligent mattress sleep aiding method and device, equipment and a medium. The method comprises the following steps: processing a target image of a target area in a sleep scene by utilizing a respiration detection algorithm, and determining respiration frequency information of a target object; the real-time sleep state of the target object is judged according to the human body sign data of the target object, the breathing frequency information and preset sleep state classification; if the real-time sleep state is a waking state or a preliminary sleep state, switching to a sleep-helping working mode, the sleep-helping working mode being used for adjusting sleep-helping volume and massage strength according to a preset adjustment level; and if the real-time sleep state is a shallow sleep state, closing the working mode of falling asleep and helping sleep. The sleeping efficiency of the user can be effectively improved.

The invention relates to the technical field of sleep medical diagnosis, and discloses a method for diagnosing obstructive sleep apnea of a patient. The method comprises the steps that nasal pressure airflow, thoracico-abdominal breathing effort and blood oxygen saturation waveform of all-night sleep are obtained, and standardized signal flow is generated through quality evaluation and compensation. And executing sleep stage perception respiratory event analysis, and outputting an event sequence with a time sequence and a type label. And inputting the sequence into an apnea association network for event chain mining and mode extraction, and generating an event association graph and a key pathological feature vector. And performing severity grading of sleep structure dependence, outputting severity indexes of sleep stages, and finally deducing a personalized treatment path. According to the method, the accuracy of event interpretation is improved by sensing the sleep stages, the dynamic mode between the events is mined by using the association network, and more refined diagnosis is realized.

The invention discloses a sleep stage classification method based on multi-scale feature contrast learning. The method comprises the steps that electroencephalogram signals to be classified are acquired; preprocessing the electroencephalogram signal to obtain an enhanced EEG signal; inputting the EEG signal into the trained multi-scale feature comparison representation learning sleep network model, and outputting a final classification result by using the model; the multi-scale feature comparative representation learning sleep network model comprises a comparative representation learning module, a pyramid time context learning module and a classification module which are connected in sequence; a difficult sample mining unit is arranged in the comparison characterization learning module and is used for mining boundary samples and samples with low classification accuracy from a training data set, introducing a sample weighting mechanism into a supervised comparison loss function, setting weights of the samples and limiting the range of the weights through a maximum weight limiting mechanism; and finally, optimizing network parameters in the contrast representation learning module through a supervised contrast loss function.

The invention discloses a sleep quality improving system based on big data analysis, and relates to the technical field of big data. According to the system, a multi-dimensional sleep state monitoring matrix is constructed, effective intervals of monitoring points are optimized in combination with dynamic adjustment factors and a deep learning algorithm, and redundant data interference is eliminated; generating a sleep intervention regulation factor based on the environmental influence function and the physiological rhythm constraint factor, and training the sleep intervention function through a genetic algorithm to dynamically adjust the intervention intensity; and generating an intervention instruction by utilizing the reinforcement learning model and combining the real-time physiological data of the user, the environmental parameters and the sleep stage prediction result, and controlling the starting sequence and parameter adjustment of the monitoring points until preset ideal sleep state parameters are met. According to the method, the problems of insufficient monitoring precision, poor environment adaptability, intervention lag and the like in a traditional method are solved, the accuracy and the real-time performance of sleep intervention are improved, and the method is suitable for the fields of smart home, medical health and the like.

The invention provides a sleep assisting system based on photoacoustic magnetic vibration wave resonance, and relates to the technical field of sleep assisting. The system comprises an acquisition unit, a processing unit, a stimulation unit and an evaluation feedback unit. Determining a sleep stage of the user, analyzing phases of multi-scale electroencephalogram rhythm components such as slow waves and spindle waves in a cross-scale manner, and extrapolating and predicting a phase value of a target electroencephalogram rhythm at a future moment in combination with a recent phase trajectory; taking the predicted phase as a target state, mapping a rising / falling slope of a slow wave form into an intensity change parameter of photoacoustic and magnetic vibration stimulation by constructing a mapping channel, and selecting an optimal stimulation combination for output through a decision model based on the intensity change parameter; calculating a phase locking effect index, and adjusting a phase prediction model parameter by using an optimization algorithm according to the index so as to optimize subsequent stimulation; and linkage regulation and control of an external environment are supported, and centralized monitoring of sleep states of multiple users can be realized. According to the invention, self-adaptive guidance of sleep brain rhythms is realized, and the sleep quality is effectively improved.

The invention provides a multi-mode adaptive wave simulation control system and method for a deep sleep physiotherapy cabin. Belongs to the technical field of fluid mechanics and health intervention. The method comprises the steps that multi-modal physiological parameter collection is conducted on a user on a deep sleep physiotherapy cabin, a real-time physiological data set is generated, the current sleep stage of the user is deduced through a time-frequency analysis and machine learning model on the basis of the data set, a sleep stage label is generated, a pre-trained wave mode generator is dynamically called according to the sleep stage label, and the deep sleep physiotherapy cabin is obtained. An initial set of wave parameters matching the current sleep stage is generated. By sensing the physiological state of the user in a non-contact mode, deducing the sleep stage and dynamically generating and adjusting the wave modes such as sleep guiding and deep sleep maintaining, water fluctuation accurately adapts to the physiological requirements of the user in different sleep stages, a traditional static wave mode is abandoned, highly-personalized sleep adjusting experience is provided for the user, and the sleep quality is effectively improved.

The invention discloses a sleep assistance composite audio generation method and system based on multi-frequency brain waves and a medium, and belongs to the technical field of digital audio processing, and the technical scheme is characterized by comprising the following steps: S1, collecting a brain wave signal of a user in real time through a brain wave monitoring module; s2, dynamically selecting a target frequency band combination according to the sleep stage and setting an amplitude change rule; s4, pink noise with the frequency spectrum range of 20-200 Hz is injected into the composite audio, the energy proportion of the pink noise is 5%-15%, and the amplitude is dynamically adjusted according to the environmental sound pressure level SPLenv. The method has the effect that the limitation of traditional sleep assisting audio is solved through the technologies of multi-frequency brain wave synchronization, personalized customization, dynamic adjustment, diversified audio combination and the like.

The invention provides a radar-based full-time in-bed state monitoring method, system and product, and the method comprises the steps: carrying out the signal preprocessing of a collected radar echo signal, and obtaining target existence information and target physiological information; performing feature extraction on the target existence information and the target physiological information, and adaptively dividing a daytime period and a night period based on a feature extraction result; performing multi-mode sleep state staging based on the target existence information and the target physiological information in the night time period; performing daytime activity state classification based on the target existence information and the target physiological information in the daytime period; and generating a full-time in-bed state monitoring report based on the sleep state staging result and the daytime activity state classification result. According to the method provided by the invention, both long-term bedridden people and non-bedridden people can be considered, the day and night time periods are automatically switched, and the function that single radar equipment synchronously supports night sleep staging and daytime activity classification is realized.

The invention discloses a sleep stage identification method and device based on a multi-mode signal, equipment and a medium. The method comprises the following steps: extracting a target lead signal comprising an electroencephalogram signal and an electro-oculogram signal from a polysleep monitoring signal of a target object; performing data preprocessing on the target lead signal to obtain a reference lead signal; inputting the reference lead signal into a sleep stage recognition model to obtain a sleep stage prediction result of the target object; wherein the sleep stage recognition model comprises a preliminary feature extraction unit, two deep feature extraction units, a dynamic gating fusion unit, a bidirectional long-short-term memory network unit, an attention unit and a classification unit, the deep feature extraction units are constructed based on a focus adjustment mechanism, and multi-scale one-dimensional deep convolution is adopted; and the two deep feature extraction units are used for performing deep feature extraction on the electroencephalogram signal and the electro-oculogram signal respectively. According to the scheme, the sleep stage recognition precision and robustness can be improved by using the sleep stage recognition model.

The present invention relates to the technical field of human sleep data analysis, and specifically discloses a human sleep data analysis method based on intelligent mattress monitoring, the method comprising: collecting sleeping state information, analyzing sleeping difficulty, analyzing sleeping environment comfort, confirming user sleep stages, analyzing sleep process quality and comprehensive sleep quality analysis; the present invention comprehensively analyzes whether a target user has sleep disorder by combining whether the target user has sleeping difficulty and whether the target user has sleep quality problems during sleep, thereby improving the comprehensiveness and accuracy of the target user's sleep problem assessment, providing the target user with personalized sleep advice, reducing the impact on the target user's physical and mental health, improving the timeliness of the discovery and treatment of sleeping difficulty, and for users with sleep disorders, helping doctors or therapists to formulate more effective treatment plans.

The invention discloses a sound-based sleep management method and device, equipment and a medium. The method is applied to an intelligent sleep management system and can be applied to the fields of medical health or financial insurance and the like. The method comprises the following steps: collecting a sleep audio signal, and carrying out signal preprocessing on the sleep audio signal to obtain a target audio signal; performing time-frequency analysis and feature extraction on the target audio signal to obtain a sleep feature, wherein the sleep feature comprises a sleep stage; performing event detection on the sleep features through a preset neural network to obtain a sleep event; acquiring sleep quality scores of the sleep events and the sleep stages through a preset evaluation model, and constructing a sleep management scheme according to the sleep quality scores. By implementing the method provided by the invention, the problems that the sleep problem cannot be comprehensively considered, a sleep management scheme is difficult to generate and the sleep quality of a user is difficult to improve in the prior art can be solved, so that the chronic disease onset risk is reduced, the customer health level is improved, the insurance claim rate is indirectly reduced, and a win-win situation is realized.

The present disclosure generally relates to systems and methods for monitoring and / or the sleep stage of an individual using one or more sensors, and methods of treating medical conditions related thereto (e.g., obstructive sleep apnea).

The present invention provides a sleep stage classification method based on zero-shot learning and contrastive learning, comprising the following steps: S1: acquiring and processing raw EEG, EMG, and EOG physiological signals; S2: acquiring semantic information related to sleep stage classification; S3: manually matching the physiological signals and semantic information; S4: data set partitioning: dividing the matched physiological signal and semantic information groups in S3 into a training set, a validation set, and a test set; S5: pre-training: inputting the training set obtained in S4 into a zero-shot learning and contrastive learning model for pre-training, and using contrastive learning to fine-tune the zero-shot learning and contrastive learning model; S6: predicting sleep stage classification. This invention addresses the problem of low sleep stage assessment accuracy in existing technologies.