294 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 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 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 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).

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.

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.

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.

The invention discloses a wearable fatigue monitoring system for senior high school students based on EEG and PPG, and relates to the field of human factor engineering, the system is based on electroencephalogram signals and pulse wave signals of senior high school students under daily learning and living conditions, the electroencephalogram signals and pulse wave characteristic values are fused and jointly input into a fatigue evaluation algorithm to obtain fatigue state evaluation of the senior high school students. The accuracy of the method reaches 80.6%, and powerful support is provided for guaranteeing physical and psychological healthy development of senior high school students.

This application discloses a wearable EEG signal processing method and device based on feature fusion. The method includes: synchronously latching the main channel EEG signal from bioelectric electrodes and multimodal auxiliary signals from various auxiliary sensor interfaces within a preset period; inputting the multimodal auxiliary signals into a pre-trained interference identification model, outputting a structured interference quantization vector, which includes the confidence and intensity of different interference sources; processing the main channel EEG signal according to the confidence and intensity of different interference sources and a preset parameterized anti-interference algorithm library to obtain the final EEG data; quantifying the quality confidence of the final EEG data, storing the ternary mapping relationship between the current period, quality confidence, and the final EEG data, and sending it to a downstream application. Using this application, adaptive matching of the processing strategy with the real-time interference scenario is achieved, effectively avoiding overprocessing or underprocessing, and greatly improving the data robustness and accuracy of downstream applications.

The invention provides a preparation method of a miniature carbon-based flexible temperature sensor based on a screen printing technology, and belongs to the technical field of temperature sensor preparation. Comprising the steps of modulation of high-viscosity carbon-based electronic ink MWCNT / rGO, preparation of a temperature-sensitive electrode through a screen printing technology and packaging of a temperature sensor. The miniature carbon-based flexible temperature sensor prepared by the invention has stability of high response and high mechanical performance; meanwhile, the preparation method of the miniature carbon-based flexible temperature sensor is relatively low in cost, and is suitable for mass production of high-performance miniature wearable temperature sensors.

The invention discloses a wearable teleoperation master hand, and belongs to the technical field of robot teleoperation and wearable equipment. The main hand comprises main glove sets symmetrically arranged on the two sides of a carbon fiber tube, each set comprises a shoulder joint, an elbow joint, a wrist joint and an adjustable large / small arm wearing structure, and each joint is integrated with an angle sensor to collect motion data in real time. The master manipulator adapts to different users through the length adjusting device, and naturalness and precision of teleoperation are improved. The upper limb motion information of an operator is captured through lightweight wearable design, and a more visual and efficient input mode is provided for remote control of the robot.

1. The name of the design product: AR glasses (G520A). 2. The use of the design product: AR augmented reality technology application equipment, wearable display device. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention belongs to the technical field of air water production equipment, and provides a portable wearable air water production machine capable of recovering human body humidity, a machine body comprises a rectification bin and a water tank which are arranged up and down, two wearable straps are fixed on the side surface of the machine body, a power supply bin is arranged on the side surface of the water tank, and the rectification bin communicates with the water tank through a water through hole; a semiconductor refrigeration device is installed in the rectification bin, the two ends of the upper side of the rectification bin are each communicated with an air inlet pipe, adsorption pipes are installed at the outer ends of the air inlet pipes, micron-level array adsorption holes are distributed in the surfaces of the adsorption pipes, and an air outlet is formed in the middle of the upper side of the rectification bin; and a water outlet switch is mounted at the lower end of the water outlet. The semiconductor refrigeration technology and the human body temperature and humidity recovery technology are combined, drinkable water can be produced more efficiently, the weight is light, noise is low, energy consumption is low, refrigeration is rapid, and no pressure container and no potential safety hazard exist; the device is small and exquisite in structure and convenient to carry and can be easily carried by adults.

This application discloses a data transmission method, apparatus, device, and storage medium, belonging to the field of wearable devices. The method includes: receiving first communication data sent by a terminal, the first communication data being transmitted through a data communication connection established between a first system and the terminal; processing the first communication data through a target middleware to obtain service data contained in the first communication data, the target middleware being a first middleware in the first system or a second middleware in a second system; and sending the service data to a target application through the target middleware so that the target application can process the service data, the target application being an application in the first system or the second system. The wearable device maintains data communication with the terminal through the first system, avoiding the impact of system switching on data communication, ensuring the stability of data communication between the wearable device and the terminal, and the middleware set in the system performs data processing and data dispatch, ensuring that service data can be processed in a timely manner.

We provide the system. [Solution] A wearable camera for capturing the user's visual information, Artificial intelligence means that analyzes the aforementioned visual information and generates information based on object recognition and location information, An audio output device that provides the generated information to the user in audio format, A system that includes this.

The invention provides a wearable myoelectric prosthesis control method, a terminal, a storage medium and a system, and the method comprises the steps: carrying out the wavelet packet decomposition of a surface electromyogram signal for the surface electromyogram signal of each preset muscle position of a target body, and obtaining a plurality of sub-bands and the decomposition coefficients thereof; variational mode decomposition is carried out on the surface electromyogram signals to obtain a plurality of intrinsic mode functions; extracting a first feature corresponding to each sub-band based on the decomposition coefficient; extracting a second feature of each intrinsic mode function; extracting time domain features of the surface electromyogram signals; inputting the first feature, the second feature and the time domain feature into a TabPFN model to obtain an action classification result of the target body; and controlling the myoelectric prosthesis based on the action classification result. According to the method, the wavelet packet decomposition method and the variation mode decomposition method are fused for feature extraction, and the TabPFN model is combined, so that a signal complex mode can be learned more accurately when the high-dimensional nonlinear electromyographic signals are processed, and the action recognition accuracy is improved.

The invention relates to a passive driving microcapsule movement color development method, and belongs to the field of sensor detection. According to the method, sweat generated during movement of a human body drives a photovoltaic device to spontaneously generate electric potential, the electric potential directly acts on microcapsule dispersion liquid to drive directional movement of black and white particles in the microcapsule dispersion liquid, and visual color development response without an external power source is achieved. The method mainly comprises the steps of sweat induced power generation, particle electrophoresis driving and ion concentration associated color development. The problems that an existing sweat detection technology depends on an external power source, the equipment size is large, and real-time visual reading cannot be achieved are solved, and the device is mainly applied to the fields of real-time sweat electrolyte analysis in exercise physiological monitoring, autonomous color development change of wearable medical equipment, passive sensing display integrated systems based on ion concentration gradient and the like.