384 results about "Wearable eeg" patented technology

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

Physiological monitoring can be provided through a lightweight wearable monitor that includes two components, a flexible extended wear electrode patch and a reusable monitor recorder that removably snaps into a receptacle on the electrode patch. The wearable monitor sits centrally on the patient's chest along the sternum oriented top-to-bottom. The placement of the wearable monitor in a location at the sternal midline, with its unique narrow “hourglass”-like shape, significantly improves the ability of the wearable monitor to cutaneously sense cardiac electrical potential signals, particularly the P-wave and the QRS interval signals indicating ventricular activity in the ECG waveforms. In particular, the ECG electrodes on the electrode patch are tailored to be positioned axially along the midline of the sternum for capturing action potential propagation in an orientation that corresponds to the aVF lead used in a conventional 12-lead ECG that is used to sense positive or upright P-waves.

The invention discloses a sleep monitoring and intervention method and system based on electroencephalogram signals, and belongs to the technical field of sleep monitoring and intervention. The method comprises the steps that electroencephalogram signals are collected through a dry electrode worn on the head of a user; the signals are preprocessed; performing automatic sleep staging on the pre-processed signal based on a layered space-time joint modeling network, and outputting a waking period label, an N1 period label, an N2 period label, an N3 period label and an REM period label; whether intervention conditions are met or not is judged based on the staging labels and the real-time electroencephalogram spectrum features; and if yes, generating and applying an alternating current stimulation signal for intervention. The system comprises a wearable electroencephalogram acquisition device, a signal analysis module and an intervention module. According to the method, the deep learning model integrating front-end self-adaptive processing, the graph neural network and Transform is adopted for sleep staging, closed-loop phase synchronous stimulation driven by the digital phase-locked loop is combined on the basis, connection from sleep monitoring to closed-loop intervention is achieved, and the sleep monitoring accuracy and the intervention effect are effectively improved.

The invention discloses a pilot mental load assessment system based on emotion calculation. The pilot mental load assessment system comprises an information acquisition and processing part and a psychological assessment and intervention report generation part. Specifically, a wearable physiological signal acquisition device is provided for a pilot in traditional simulated flight training, electroencephalogram, electrocardio and skin electrical signals of the pilot are acquired in the training process, and the emotion level of the pilot during training is judged through comprehensive processing of a common person emotion model and a data algorithm, so that the training accuracy of the pilot is improved. According to the judgment result, mental load and pressure level indexes of the pilot at a certain moment in training are obtained through comprehensive analysis, and the analyzed oscillogram of each index changing according to the time axis is taken as an output result; and finally generating physiological data, flight training data and an emotion evaluation result in training collected in training, and formulating a targeted training intervention scheme according to the individual emotion state of the pilot. According to the method, the psychological quality problem of the pilot can be well corrected, and finally an ideal training effect is achieved.

A method of providing an augmented reality (AR) content in a vehicle, and / or a wearable AR device and an electronic device for performing a method. The wearable AR device may include a processor and a memory storing instructions to be executed by the processor, and when the instructions are executed by the processor, the processor is configured to: determine whether the wearable AR device is in a space of the vehicle based on at least one of information received from the vehicle or a value measured using at least one sensor of the wearable AR device; when it is determined that the wearable AR device is not in the space of the vehicle, output an AR content corresponding to a space around the wearable AR device based on the value measured using the at least one sensor of the wearable AR device; when it is determined that the wearable AR device is in the space of the vehicle, determine whether there are anchor devices of the vehicle capable of communicating with the wearable AR device; and when it is determined that there are the anchor devices capable of communication, output an AR content corresponding to a space of the vehicle around the wearable AR device by communicating with the anchor devices.

The invention discloses a mathematics virtual-real fusion teaching system. A scene construction module is used for constructing a virtual teaching scene by using a virtual reality technology according to teaching contents and learning requirements of students; the animation simulation module is used for creating a mathematical model and an animation in the virtual teaching scene by utilizing a three-dimensional modeling technology and an animation production technology; the data acquisition module is used for acquiring learning behavior data and physiological data of students through the intelligent teaching terminal and the wearable sensor to obtain learning feedback data; analyzing the learning feedback data by using a machine learning algorithm to obtain learning state data of the student; and the teaching feedback module is used for adjusting a teaching mode and a teaching scene in the virtual teaching scene according to the learning state data. Through virtual reality and augmented reality technologies, in combination with a highly vivid virtual teaching scene and an innovative interaction mode, mathematics learning is more vivid and interesting, and the learning interest of students is greatly improved.

The invention provides a motion form identification method and system based on fusion vision and clothing AI. The method belongs to the technical field of artificial intelligence and computer vision, and comprises the following steps: carrying out real-time acquisition on video or image data in a human body movement process through a wearable visual sensor, carrying out clothing identification and clothing feature extraction on the acquired video or image data based on a clothing AI technology, and carrying out deep learning on the basis of a deep learning algorithm; human body postures are recognized, key posture features are extracted, time information is combined, human body movement tracks are analyzed, and dynamic features are extracted. By fusing the image data acquired by the visual sensor and the clothing AI technology and combining the deep learning algorithm to identify the human body posture, the key posture features and the dynamic features can be extracted more accurately, so that the accuracy of motion form identification is improved.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention is suitable for the technical field of wearable equipment, and provides a wearing comfort testing method of wearable equipment and electronic equipment, and the method comprises the steps: wearing the wearable equipment on a simulation human body assembly of a motion simulation device, and when the motion simulation device runs, testing the wearing comfort of the wearable equipment on the simulation human body assembly; acquiring reference position data corresponding to each reference motion capture mark point on the simulated human body assembly, target position data corresponding to each target motion capture mark point on the wearable device and pressure data acquired by each pressure sensor arranged at a wearing position of the wearable device at a preset acquisition frequency; determining a wearing stability index of the wearable device in a preset wearing scene according to each frame of target position data and the reference position data; and determining a wearing pressure index of the wearable device in a preset wearing scene according to each frame of pressure data. Therefore, the influence of subjective feeling on the test result is avoided, repeated verification can be realized, and the test precision of the wearing comfort is improved.

The invention relates to the technical field of medical equipment, in particular to a multi-modal respiratory signal acquisition system for realizing appearance and data dual individuation for multi-body-type respiratory patient crowds, which can generate an individualized patient respiratory data set through wearable waistcoat design and respiratory data weight calibration. The system mainly comprises a waistcoat body, an acquisition module, a communication module, a storage module and a personalized weight software module. The acquisition module comprises A-type ultrasound, an electroencephalogram sensor, a myoelectricity sensor, an inertial sensor, a flow meter and a body surface tension band. In the collecting process, firstly, a patient wears a waistcoat, the length of a bandage is adjusted, sensor collecting point positions are adjusted to medical reference positions, and then the sensors are synchronously triggered through the communication module to conduct real-time data collection. And a personalized weight software module is used for generating weight calibration for validity and significance of each modal data according to the basic medical record of the patient, and finally, the data and the weight are jointly stored in a storage module. According to the invention, the comprehensiveness and precision of data can be improved, and patient personalized acquisition which is widely applicable is realized.

The invention provides an air bag, a wrist strap and wearable electronic equipment. The air bag comprises a first bag body and a second bag body, the first bag body and the second bag body are stacked in the thickness direction of the air bag, the first bag body comprises a first main body layer and a second main body layer, the first main body layer and the second main body layer are connected and define a first cavity, and the second bag body comprises a third main body layer and a fourth main body layer; the third main body layer and the fourth main body layer are connected and define a second cavity, the first cavity is communicated with the second cavity, the second main body layer and the fourth main body layer are partially connected and form a connecting area, the area where the second main body layer and the fourth main body layer are not connected is a non-connecting area, reinforcing pieces are arranged in the non-connecting area in an overlapped mode and are adjacent to the connecting area, and the air bag is inflated. The first capsule body and the second capsule body layer expand in the cavity thickness direction, and the reinforcing piece drives the non-connection area of the second main body layer to be away from the fourth main body layer. The reinforcing piece can improve the strength of the first bag body, and the situation that the function of the first bag body is affected due to deviation during inflation is avoided.

The invention discloses a hand rehabilitation training device, which belongs to the technical field of medical rehabilitation, and comprises a wearable glove base body, a main body structure, a wrist structure, a multi-mode driving module, a nickel-titanium alloy wire driving unit, a pneumatic driving unit, a sensor detection module, a bending sensor, a myoelectricity sensor and a pressure sensor, the multi-mode driving module comprises a sensor detection module and a wrist structure, the electroencephalogram interaction module comprises a non-invasive EEG electrode and a head band, the self-adaptive control module comprises a control box arranged on the wrist structure, and the self-adaptive control module is electrically connected with the multi-mode driving module, the sensor detection module and the electroencephalogram interaction module through wires and communicates with terminal equipment through a built-in wireless module; according to the hand rehabilitation training device with the structure, flexible adaptation, multi-mode driving, electroencephalogram-sensing closed-loop feedback and intelligent regulation and control of hand rehabilitation training are achieved through cooperative work of multiple modules, and training accuracy and individuation are improved.

The invention relates to the technical field of sleep data processing, in particular to an intelligent intervention system for circadian rhythm disorder insomnia based on an APP. The system comprises a user day and night insomnia type marking and dividing module, a day and night insomnia user sleep parameter acquisition module, a day and night insomnia user disorder degree evaluation module and a day and night disorder insomnia user intelligent intervention execution module, and questionnaire survey and user day and night sleep parameter real-time acquisition can be performed through an APP on a wearable wristwatch. Obtaining a user day and night insomnia severity index and a user day and night sleep category measurement score, and performing day and night insomnia type virtual mark division to obtain various day and night insomnia types; the intelligent intervention work of the circadian rhythm disorder insomnia of the user is executed by monitoring the circadian sleep cycles and sleep quality scoring coefficients of the user corresponding to various users in real time and carrying out quantitative calculation of the rhythm disorder of the heart rate cycle and intelligent intervention pushing of the insomnia of the user. According to the invention, a customized insomnia intervention scheme can be provided.

The invention is suitable for the technical field of electronic equipment, and provides a safety prompting method of wearable equipment and the electronic equipment, and the method comprises the steps: when a safety prompting message corresponding to the wearable equipment associated with the current equipment is obtained, displaying a contact corresponding to the safety prompting message in a plurality of message prompting modes in the current equipment; and in response to a trigger operation on a target contact, processing the safety prompt message, the target contact being any one contact. Therefore, the safety prompt message corresponding to the wearable device is displayed at the same time in various message reminding modes, and the contact corresponding to the safety prompt message is provided at the same time, so that the safety prompt message can touch a user in time in various modes, and the safety prompt message can be prompted to the user when the electronic device is in various use states. Therefore, the user can know and check the safety prompt message in time, so that the safety and timeliness of the safety prompt touch of the wearable equipment are improved, and the user experience is improved.

The invention discloses a millimeter wave radar health monitoring method and system based on wearable equipment, and relates to the technical field of non-contact physiological signal monitoring, the specific steps are as follows: low-frequency life signals of a monitored object are collected based on frequency modulation continuous waves emitted by a radar system, and the low-frequency life signals are time sequence signals of multiple channels; performing wavelet packet decomposition and adaptive filtering on the low-frequency life signal to obtain a filtered signal; and inputting the filtered signal into a trained feature extraction and separation model, carrying out feature extraction and evaluation, and outputting a heartbeat signal and a respiration signal. According to the method, a strong anti-interference preprocessing process is constructed by fusing wavelet packet dynamic decomposition and a sub-band energy entropy threshold method and combining an NLMS adaptive filtering technology, motion artifacts and environmental noise can be effectively stripped, the signal-to-noise ratio of original millimeter wave radar signals is increased, and the anti-interference performance of the millimeter wave radar signals is improved. And a solid foundation is laid for subsequent accurate extraction of weak vital sign signals (respiration and heart rate).

To provide information for notifying a user of an appropriate charging timing in a wearable-type biological signal measuring device in which the number of channels to be activated can be dynamically switched.SOLUTION: A wearable-type biological signal measuring device that is capable of measuring the biological signal of a plurality of channels and driven by a battery, includes: an operation mode switching part which can switch between a first operation mode for measuring the biological signal of the plurality of channels by activating the plurality of channels and a second operation mode for measuring the biological signal of a part of the channels by activating the part of the channels; a battery remaining amount acquiring part for acquiring the information of battery remaining amount of the battery; a prediction part for predicting an operable time of the biological signal measuring device based on the consumption power in each of the operation mode and the battery remaining amount; and an output part for outputting the information of the predicted value of the operable time.SELECTED DRAWING: Figure 1

1. The name of the product of this design: AR glasses. 2. Purpose of the product of this design: wearable device used for human-computer interaction such as information collection, processing, storage, output and digital access. 3. The design key point of this appearance design product lies in the shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The present invention belongs to the field of state of health monitoring of cockpit flight crews, and particularly relates to a system for monitoring an incapacitation state of a crew on the basis of a contact sensor. The system comprises a wearable wrist sensor, a wearable chest strap sensor, a seat pressure sensor, a data synchronization module, an intelligent computing platform, a speech collection and recognition module and a state alarm module. By means of a contact multi-sensor integration technique and algorithm, the present invention can implement accurate monitoring and analysis of an electrocardiograph (ECG) signal, a photoplethysmography (PPG) signal, a saturation of peripheral oxygen (SPO2) signal, a skin temperature (SKT) signal and a heart rate (HR) signal of a pilot, thereby realizing real-time monitoring of an incapacitation state of a flight crew, and improving the safety of a single-pilot driving mode.

The invention relates to a mechanical fingerstall based on arm electromyographic signal intelligent control, and belongs to the technical field of medical instruments, the mechanical fingerstall comprises a spliced mechanical glove, the back of the spliced mechanical glove is provided with a touch display screen, and the touch display screen is wirelessly connected with a forearm electromyographic signal collector and an upper arm electromyographic signal collector. The forearm electromyographic signal collector comprises a wearable sensor bracelet main body, an inflatable fixing gasket layer, a flexible electromyographic sensor strip and the like, and is used for collecting and transmitting forearm electromyographic signals. The upper arm electromyographic signal collector is composed of a shoulder fixing sleeve, a signal preprocessing box, an electrode cluster and the like and collects upper arm electromyographic signals. The main control unit box controls the mechanical fingerstall to act through the intelligent control algorithm module according to the received electromyographic signals, and switching from pure upper arm muscle control to double-arm cooperative control can be achieved. According to the mechanical fingerstall, electromyographic signals can be accurately collected, fingerstall actions are intelligently controlled, personalized rehabilitation and data management are provided, and the hand rehabilitation training effect and the life quality of a patient are effectively improved.

Aspects of the present invention relate to providing see-through computer display optics. In embodiments, a wearable head device comprises a frame, a first optical module comprising a first reflecting surface, a second optical module, electronic components, and a heat sink. The electronic components may be disposed adjacent to the frame and further disposed between the first reflecting surface and the second optical module. The electronic components may comprise a processor. The electronic components may be configured to rest a first distance from the head of a user of the wearable head device. The heat sink may be disposed between the first reflecting surface and the second optical module and configured to rest a second distance from the head of the user, the second distance greater than the first distance. The heat sink may be thermally coupled to the processor.

Wearable nystagmus detection devices are provided. The wearable device comprises first and second sensors configured to sense eye movement of the subject, and circuitry operably coupled to the sensors and configured to detect horizontal and vertical eye movements based on signals from the first and second sensors and / or a transmitter configured to transmit signals sensed by the first and second sensors to remote circuitry configured to receive signals transmitted by the transmitter and to detect horizontal and vertical eye movement based on signals from the first and second sensors. Also provided are systems and kits that include the devices, as well as methods for using devices and systems to monitor eye movement of a subject. The devices, systems, methods and kits find use in a variety of different applications.

A system configured to characterize a physical motion performed by a user is disclosed herein. The system can include a wearable article including a first flexible circuit, that includes a first trace formed from a deformable conductor. The first flexible circuit is positioned in a first location of interest on the wearable article. The system can further include a computing device configured to receive a first signal generated by the first flexible circuit, determine a first electrical parameter based on the first signal, determine a physical condition of the first flexible circuit based on the first electrical parameter, compare the physical condition of the first flexible circuit to previously determined physical conditions associated with the wearable article, and characterize the physical motion performed by the user based on the comparison.

The invention relates to an intelligent depression detection system based on physiological behavior multi-modal information. The system comprises an Android application program and a wearable device capable of collecting electrocardio, electroencephalogram and PPG signals (photoplethysmography), and voice and video signals are collected through a camera and a microphone. The system is provided with a user information input module, a sensor detection module, a resting state signal acquisition module, a picture video emotion induction experiment module, a multi-mode signal fusion analysis module, an evaluation report generation module and other functional modules. The system combines resting state signals and induced state experiment data, through intelligent analysis and fusion of physiological and behavioral two-dimensional signals, depression identification and detection can be carried out based on comprehensive characteristics of the five signals, the accuracy and reliability of depression detection are effectively improved, and therefore the psychological state of a user is evaluated more comprehensively. The system has the characteristics of noninvasiveness, convenience, real-time acquisition, multi-mode fusion and the like, and can provide a powerful auxiliary tool for clinical depression diagnosis.

The invention relates to the technical field of medical Internet of Things, and provides a ward patient sign information acquisition and intelligent Internet of Things control method, which comprises the following steps: acquiring patient multi-dimensional sign data by adopting a wearable sensor and sickbed peripheral monitoring equipment, and transmitting the data to a hospital data center through Internet of Things gateway hybrid networking after data processing; the data center analyzes the data by adopting a dynamic threshold model and generates early warning; and according to the early warning type and the emergency degree, intelligent equipment in the ward is controlled, and a control strategy is optimized according to equipment feedback. According to the invention, a traditional fixed threshold monitoring mode is changed, personalized accurate monitoring is realized, and misjudgment and missed judgment are avoided; multi-device cooperative collection and intelligent control linkage are achieved, and the ward environment and the device state are accurately adjusted; through data mining and prediction, the health risk of the patient is intervened in advance, a monitoring, control and optimized closed-loop system is formed, the medical monitoring accuracy and reliability and the ward intelligent management level are remarkably improved, and the personalized rehabilitation requirements of the patient are met.

The invention discloses an intelligent safety protection system for ship construction. The intelligent safety protection system comprises wearable intelligent safety detection equipment, an operation environment detection and personnel management and control system, an AI perception prediction and judgment module and a safety production detection integrated platform, wherein multi-source data acquired by the wearable intelligent safety detection equipment and operation environment detection and personnel management and control system are input into the AI perception prediction and judgment module; the AI perception prediction and judgment module generates a risk type and a risk level through cross-modal analysis; the safety production and detection integrated platform is responsible for transmission and interaction of multi-source data and generates a decision signal of an equipment control instruction according to the risk type and the risk level; driving an operation environment detection and personnel management and control system to dynamically adjust a safety protection area; an active protection instruction is sent to the wearable intelligent safety detection equipment; according to the intelligent safety protection system for ship construction, the risk can be intelligently predicted, protection can be dynamically adjusted, and the ship construction safety is improved.

A wearable diagnostic device that is worn on a user's body and is capable of providing various types of health-related information to the user is described. The wearable diagnostic device is capable of providing real-time, non-invasive, accurate, and continuous data about the user's heart rate, hemoglobin level, body temperature, oxygen level, glucose level, and blood pressure. In some embodiments, the wearable diagnostic device can also provide electrocardiogram (EKG) data or detection of Parkinson's symptoms, such as involuntary movements of the user's hands. The user can configure the wearable diagnostic device to provide information about one, more, or all of the health-related information listed above.

An integrated cyber-physical system and method for high-fidelity distress monitoring and automated alert error mitigation. The system comprises a wearable multi-parametric sensing suite (100) for primary anomaly detection and an autonomous mobile robotic agent (200) for secondary contextual verification. Upon detection of a potential distress event (S110), the robotic agent (200) executes a priority undocking sequence (S120) from a charging station (500) to perform multi¬ modal acquisition (S130), including 3D skeletal pose estimation and acoustic sentiment analysis. A Cloud Processing Engine (300) synthesizes these data streams — including optional spatial occupancy data from an environmental sensor network (400) — to calculate a Validation Confidence Score (VCS) (S140) using a dynamic weighted fusion algorithm. This score enables the system to autonomously perform alert de-escalation (S150) for verified safe-states or execute prioritized escalation protocols (S160) to a caregiver interface (600) and an emergency services gateway (700). The invention effectively reduces alarm fatigue and improves emergency response reliability by providing contextual validation of biometric triggers.

The utility model relates to the technical field of wearable intelligent equipment, and discloses a wearable electroencephalogram acquisition helmet for a neurolinguistics experiment, which comprises a self-adjusting cap body and a plurality of electrode modules, the self-adjusting cap body comprises a head surrounding belt and a plurality of electroencephalogram acquisition belts; the plurality of electrode modules are arranged on the plurality of electroencephalogram acquisition belts according to a preset mode; the head shroud ring has a first width, and a plurality of first strip holes are formed in the circumference; a first adjusting mechanism is arranged on the head surrounding belt; all the electroencephalogram collecting belts are at least connected with three second adjusting mechanisms, and each second adjusting mechanism is located on the outer side of the corresponding first strip hole. The helmet can adapt to head circumferences of different sizes and head shapes of different shapes, and the product is more universal; therefore, material resource cost is saved, operation is simple and convenient, time and labor are saved, and electroencephalogram data acquisition efficiency is improved. According to the scheme, the design application range is wide, the method is suitable for various neurolinguistics experiments such as bilingual conversion experiments and bilingual translation experiments, and therefore the scheme design has high application value.

A wearable audio recorder with integrated identification badge, characterized by a flattened, horizontally oriented rectangular case; the central part of the front panel of the case contains a trademark "SC.SteadyControl"; the rectangular case has rounded corners and a rim protruding above the front panel of the audio badge on one side; the central part of the front panel contains a hole for a rounded touch button; on one of the side walls of the case there is a connector and contacts for charging the recorder and exchanging data; In the upper part of the back panel of the recorder, there is a rectangular fastener with rounded corners.