52 results about "Sleep behavior" patented technology

A method of automated sleep behavior modification for insomnia and other sleep problems using a computerized treatment system including: determining a baseline sleep pattern of a user and storing in the system data indicative of the baseline sleep pattern; generating a sleep behavior regimen based on the stored sleep pattern data and a behavior modification algorithm, wherein the algorithm generates sleep inducement behavior prompts; prompting the user to perform a predetermined sleep inducement behavior at a time determined by the regimen; tracking the sleep behavior of the user and storing data indicative of the tracked sleep behavior; modifying the regimen based on the tracked sleep behavior, and prompting the user to perform the predetermined sleep inducement behavior at a time determined by the modified regimen.

A user monitoring device system includes a user monitoring device with a microphone and sensors to determine air quality, sound level/quality, light quality and ambient temperature near the user. A movement detection device detects a user's movement information. The movement detection device and the monitoring system assist to determine user sleep information and sleep behavior information. The microphone records user movement sounds detected by the movement detection device. The movement detection device is configured to cause the microphone to stop recording user movement sounds when the movement sounds are not directed to a sleep related parameter. In response to determining user sleep information or sleep behavior information the system is used for treatment of sleep or psychiatric disorders.

An E-healthy partitioned mattress capable of recognizing and recording sleep behavior without interference is characterized by being formed by combining an upper-layer mattress body and a lower-layer mattress body, wherein multiple logarithmic potentiometric sensors distributed in a reticular matrix shape are fixed in an upper surface layer fabric composite layer of the upper-layer mattress body and connected with an information collector, and human-bed interface dent amount information and logarithmic potentiometer output voltage signals synchronously changed with the human-bed interface dent amount information, which are continuously collected, are transmitted to a data processor through the information collector, so that human-bed interface dent shapes and sleeper behavior event occurrence signals are obtained simultaneously; the lower-layer mattress body is formed by combining five independent unit modules, namely, a head area, a shoulder area, a waist area, a hip area and a leg area. The mattress can recognize and record the human sleep behavior, provide technical support for healthy sleep monitoring and research as well as E-healthy sleep experience and quantitatively research the supporting relationship between a human and a bed.

A non-sleep-interference sleeping posture and sleeping behavior testing identification method includes the steps of extraction of human-bed interface compression shapes, classification and identification of sleeping postures, classification and identification of sleeping motion behaviors and analysis of sleeping behavior modes. A support vector machine is used for classification and identification of the sleeping postures, and the four sleeping postures of supination, left lateral decubitus, right lateral decubitus and prostration are identified on the basis of finite sample supervised training identification through classification of human-bed interface compression quantity matrixes; identification of the sleeping motion behaviors is achieved in the mode that logarithmic potential sensors synchronizing with human-bed interface compression quantity changes are used for outputting variation of voltage signals and obtaining signals generated when sleeper motion behavior accidents occur. According to the method, a technical support is provided for monitoring and evaluation of intellectualized design of a bed system or a mattress and healthy sleeping.

The invention relates to a sleep behavior detection method based on deep learning, and the method comprises the following steps: 1) obtaining a to-be-detected video stream, and carrying out the framecutting processing to obtain a discrete image; 2) inputting the discrete pictures into a trained convolutional neural network model in sequence, and detecting to obtain a sleep posture preliminary detection result and corresponding confidence; 3) screening all sleep posture preliminary detection results based on a multi-scale detection method to obtain a sleep posture final detection result; And 4) according to the final detection result of the sleeping posture, judging whether a sleeping behavior exists or not by adopting a target tracking algorithm based on position information. Compared with the prior art, the method has the advantages that the feature fusion is adopted in the target detection model to improve the accuracy rate, and meanwhile, a sleeping behavior decision algorithm withhigh accuracy rate is adopted to avoid misjudgment of the sleeping behavior.

The invention provides a sleep deprivation model system with simulation narrow and small spaces for pilots. The sleep deprivation model system comprises the closed spaces and a tested screening system. The tested screening system is arranged in the closed spaces and comprises a cognitive function assessment subsystem, a sleep quality assessment subsystem, an emotion assessment subsystem, a social function assessment subsystem and a sleep detection device; the cognitive function assessment subsystem is used for assessing cognitive behavior and cognitive functions of testees; the sleep quality assessment subsystem is used for assessing sleep behavior of the testees; the emotion assessment subsystem is used for assessing emotion indexes of the testees; the social function assessment subsystem is used for assessing social parameters of the testees; the sleep detection device is used for detecting sleep parameters of the testees. According to the technical scheme, the sleep deprivation model system has the advantage of accurate detection data.

The disclosure provides a sleep behavior information prediction method and system based on intelligent household equipment. The method comprises the following steps of monitoring running state of oneor more pieces of intelligent household equipment, and acquiring a first parameter related to the current sleep behavior when monitoring a running signal generated by triggering the intelligent household equipment; inputting the first parameter into a preset neural network model, and performing prediction based on the first parameter by using the neural network model so as to obtain a second parameter output after prediction, wherein the neural network model is a model obtained by training according to historical data related to sleep behaviors; and generating the prediction information of thesleep behavior according to the second parameter. Based on the technical scheme of the invention, the sleep behavior information of the user is predicted, the sleep quality of the user is improved, and the sleep experience of the user is enhanced.

The invention discloses a sleep mode setting method based on a user terminal, the user terminal and a computer readable storage medium, and the sleep mode setting method comprises the steps: obtaininghistorical sleep data of a user in a plurality of unit periods, and enabling the historical sleep data in each unit period to comprise a historical sleep start point and a historical sleep end point;predicting a sleep time period of the user in the current unit period according to each historical sleep starting point, each historical sleep terminal point and a preset weight corresponding to thehistorical sleep data in each unit period; and taking the predicted sleep time period of the user in the current unit period as a sleep mode starting time period of the user terminal. According to theinvention, the sleep behavior of the user is learned based on the historical data of the user, so that the sleep habit of the user is obtained, the relatively accurate sleep starting time and endingtime of the user are obtained, the sleep mode starting time period of the user terminal is adaptively adjusted, the user adjustment frequency is reduced, and the intelligent degree is high.

The invention discloses a rapid eye movement period sleep behavior disorder classification method based on sleep electroencephalogram. The method comprises the following steps: preprocessing an acquired sleep electroencephalogram signal of a patient, acquiring an electroencephalogram signal of an electroencephalogram interval in a sleep R period, cutting according to a preset signal length to acquire a plurality of sub-signal segments, layering an original electroencephalogram signal of each sub-signal segment, and enabling a frequency domain component after wavelet transform to approximatelycorrespond to different wave bands of original electroencephalogram according to the frequency of the electroencephalogram; carrying out empirical mode decomposition (EMD) to obtain a multilayer intrinsic mode function (IMF); performing feature extraction on the electroencephalogram signals of different wave bands and the multilayer intrinsic mode function, and performing dimensionality reductionon the feature set PCA to obtain a low-dimensional feature set; and carrying out classification and identification by using the feature set after dimension reduction, and classifying patients suffering from Parkinson's disease accompanied by rapid eye movement sleep disorder, Parkinson's disease not accompanied by rapid eye movement sleep disorder and idiopathic rapid eye movement period sleep behavior disorder. Complex PSG detection is simplified into electroencephalogram detection, a whole night does not need to be consumed, the number of times of measurement is reduced, an accurate recognition result is obtained, and then a very good auxiliary effect can be achieved on treatment of the illness state of a patient.

The invention discloses a movable air conditioner and an air conditioning control method thereof, and belongs to the technical field of air conditioners. The air conditioner comprises a semiconductortemperature regulator, a heat storage device, an air conditioning device, a detecting device and a controller, the detecting device is used for recognizing sleep behavior information of a user, and the controller is used for controlling the air conditioning device to conduct air conditioning operation based on the user sleep behavior information detected by the detecting device. The movable air conditioner has the beneficial effects that the semiconductor temperature regulator is adopted as a temperature regulating part, the situation that much noise is made in the temperature regulating process is avoided, and good using experience is brought to a user; and meanwhile, the air conditioning device is controlled to conduct air conditioning operation according to the user sleep behavior information, the running state of the air conditioner is regulated in time, the situation that the user catches a cold and is sick due to some sleep behavior of the user is avoided, and the sleep quality of the user is ensured.

The invention puts forwards a method and a device for prompting information to users, relating to the field of mobile health. The method comprises following steps: obtaining three-dimensional behavioral models of users; and determining current three-dimensional behavioral models and continuing time of users and generating relevant reminders by matching the pre-set rules based on users' behavior suggestions corresponding to the matching rules. The three-dimensional behavioral model comprises users' sleeping behaviors, users' motion behaviors and users' mobile terminal usage behaviors. The method for prompting information to users obtains users' mobile terminal usage behaviors, motion behaviors and sleeping behaviors for matching by collecting metadata of a mobile terminal, motion data and sleeping data and is capable of generating relevant reminders for users according to matching of current behaviors and pre-set rules.

The invention discloses a probiotic composition for treating rapid eye movement sleep behavior disorder (RBD) in patients with Parkinson disease (PD). The composition is prepared from bacillus licheniformis, bifidobacterium longum, lactobacillus acidophilus, enterococcus faecalis and pharmaceutically acceptable auxiliary materials, wherein the number of live bacteria of the bacillus licheniformisis not less than 10.0x10<8> CFU/g; the number of live bacteria of the bifidobacterium longum is not less than 4.0x10<8> CFU/g; the number of live bacteria of the lactobacillus acidophilus is not lessthan 1.0x10<8> CFU/g; and the number of live bacteria of the enterococcus faecalis is not less than 0.5x10<8> CFU/g. In the probiotic composition, the components have a synergistic effect, the RBD symptom in the PD patients can be effectively treated and improved, and the treatment effect is good in continuity and excellent in stability; the composition can effectively improve the motor symptom ofthe PD patients while having a treatment effect on the RBD symptom of the PD patients, and solves the problem that current drugs cannot treat the motor symptom and non-motor symptoms of the PD patients at the same time; and the composition can effectively reduce the dosage of levodopa in the PD patients and prevent the patients from generating resistance to levodopa prematurely, and has a betterprotection effect on the patients.

The invention discloses an insect motility activity rhythm and sleep behavior magnetic biological response research device, which comprises: an artificial magnetic field processing system used for providing magnetic field processing for the whole device; an environment factor control monitoring system used for providing a local light transmission space and monitoring space environment factors in real time and positioned in the artificial magnetic field processing system; a test insect flying motility activity rhythm measuring system used for positioning test insects and measuring the test insect flying motility activity rhythm and located in the environment factor control monitoring system; a test insect sleep behavior research system used for monitoring the sleep behavior of the test insect positioned by the test insect flight motility activity rhythm measurement system and positioned in the environment factor control monitoring system; an open source light source control system used for providing a required light source for the environment factor control monitoring system from the outside; and a control and data storage system used for controlling the whole device and collecting and analyzing data. The device is accurate in control, high in integration degree and high in adaptability.

The embodiment of the invention discloses a method and device for sleep deprivation, computer equipment and a storage medium. The method comprises the following steps of introducing set genes to set cells in a target animal body in advance, wherein the set cells are neuron cells participating in a waking-sleeping behavior adjusting and controlling mechanism, and the set genes comprise excited typeprotein genes and inhibitable type protein genes; acquiring a physiological signal of a target animal; and when the situation that the physiological signal is a sleep signal is determined, using a set light adjusting and controlling signal to perform light adjusting and controlling on the set cells, wherein the set light adjusting and controlling signal is a light beam having a set wave length, and is used for activating the set genes. Through the adoption of the method disclosed by the embodiment of the invention, a conventional sleep deprivation method is optimized, the reliability of a sleep deprivation test is improved, and the accuracy of test data is guaranteed.

A method, system and program product comprises instructing a computing device to receive a user inputted information at least identifying the user. The computing device is configured for home usage. A computer vision sensor in communication with the computing device is initialized. The computer vision sensor is configured for home usage. The initializing instructs the computer vision sensor to detect aspects of sleep deprivations of the user. Detected aspects of sleep deprivations of the user during a sleep cycle of the user are received. The computing device is instructed to transmit at least the detected aspects of sleep deprivations of the user to a computer system of a recipient.

The embodiment of the invention relates to the technical field of video monitoring, and discloses a sleep state monitoring method, electronic equipment and a storage medium. The sleep state monitoring method comprises the following steps: acquiring a continuous frame depth image containing a human body in a sleep area; identifying a specified body part of a human body in the depth image, and determining depth information of the specified body part in the depth image; and analyzing the depth information of the specified body part in the depth image, and determining the sleep state of the human body. By analyzing the continuous depth information, various sleep behaviors of the human body can be quickly determined, so that the sleep state of the human body is judged.

The invention relates to an anti-off-post and anti-sleep-on-post detection method and device, computer equipment and a storage medium. The method comprises the following steps: training a YOLOv5x model by using a human body region detection sample, and outputting a human body region frame; extracting coordinate positions of the human body key points from the human body region frame, and obtaining a sleep-on-post skeleton space-time diagram according to the coordinate positions of the human body key points of each image frame in the image frame sequence; training a behavior prediction model through the sleep-on-post skeleton space-time diagram to obtain a trained behavior prediction model; and carrying out human body off-post detection by utilizing the trained YOLOv5x model, and if the YOLOv5x model outputs a human body region frame, carrying out sleep behavior detection on the human body according to the trained behavior prediction model. By adopting the method, the off-post and sleep-on-post behaviors can be detected.

The invention discloses an infant sleep monitoring system, which comprises: at least one image acquisition module used for acquiring sleep image information of an infant in real time; and the central information processing module that is used for analyzing and judging sleep behavior information of the baby according to the sleep image information of the baby. When a baby sleeps alone, the infant sleep monitoring system provided by the invention can be used for monitoring the sleep of the baby. Once an abnormal sleep behavior is found, an alarm is sent to a guardian in time; for example, once the baby has behaviors such as face shielding and handrail climbing, relevant alarms are immediately sent to a guardian; a guardian can arrive immediately, so that accidents are avoided; a sleep curve of the baby can be obtained after the posture is monitored and recognized, and a sleep quality report is generated; the sleep condition of the baby can be known according to the sleep curve and the sleep quality report, and life arrangement is facilitated.

The invention relates to a method for inducing a rapid eye movement sleep behavior disorder (RBD) phenotype capable of transforming to a Parkinson phenotype in a non-human subject animal. The method comprises the following steps: 1) stereotactically injecting a substance capable of inducing a pathological change of synuclein into a unilateral or bilateral mesocephalon sublaterodorsal tegmental nucleus (SLD) of a non-human subject animal; 2) evaluating the induction of the RBD phenotype of the non-human tested animal; and 3) evaluating the induction of the Parkinson phenotype in the RBD animal. The invention also relates to a preparation method of a synuclein pathological RBD model. Prefabricated fibrous bodies (PFFs) capable of inducing synuclein pathology is stereodirectionally injected into an SLD nucleus, a mouse RBD model capable of transforming to a Parkinson phenotype is constructed by virtue of a pathology induction characteristic of "dissemination-nucleation" of the fibrous bodies, and the model is expected to provide an important animal model basis for clarification of pathogenesis and Parkinson's disease transforming of RBD and research and development of effective blocking drugs in a future research.

The invention relates to the technical field of sleep detection, and discloses a sleep detection method based on a front-end camera, which comprises the following steps: 1) before a system is started, adjusting and fixing the position, angle and imaging range of the front-end camera to ensure that the front-end camera is not shielded and a detection picture is clear, and setting a human body detection area according to the position of a human body, the detection area is divided into small areas for the front-end camera to perform local detection; and 2) because the monitored human body is in a lying static state under the normal condition, the monitored human body area is generally set according to the size of the lying human body. According to the sleep detection method and device based on the front-end camera, sleep and non-sleep videos are collected through the camera, sleep and non-sleep features in the videos are extracted, the model is trained to identify sleep behaviors, and the sleep behaviors and non-sleep behaviors are modeled, so that the accuracy of a detection result is ensured, and false detection caused by light change can be avoided.