108 results about "Wearable sensing" patented technology

The invention relates to a wearable sensing device for sweat calcium ion detection. The wearable sensing device comprises a micro-fluidic chip and a signal processing and transmission module which can be matched with the sensing chip and is used for signal transmission and data processing, the micro-fluidic chip comprises a Janus fabric layer, a channel layer and a chip cover layer printed with an electrochemical electrode which are sequentially arranged in an attached manner; rapid sweat sampling and concentration refreshing can be realized, and the concentration of calcium ions in sweat can be directly and selectively measured; and the signal processing and transmission module is electrically connected with an electrochemical electrode for detecting the calcium ion concentration in the micro-fluidic chip and is used for data processing and transmission. By using a micro-fluidic chip, sweat secreted by skin continuously flows through an interface of the electrochemical sensor, so that the concentration of calcium ions in the sweat can be detected in real time; the sweat sample is small in size, detected sweat can be rapidly discharged through the micro-channel and the waste liquid discharging area, sweat accumulation is avoided, and the testing accuracy is guaranteed.

The invention provides an artificial intelligence spinal nerve regulation and control system and method based on a multi-modal physiology-nerve-experience closed loop, and relates to the technical field of medical instruments, the system comprises an implantable stimulation and sensing subsystem, a body surface and wearable sensing subsystem, a communication and time synchronization subsystem and a control processing unit, the implantable stimulation and sensing subsystem, the body surface / wearable sensing subsystem and the control processing unit are in communication connection in pairs through the communication and time synchronization subsystem. According to the method, the fundamental limitation that in the prior art, only dosage is maintained, and brain and subjective experience is ignored is broken through, and more stable, more efficient and more personalized accurate pain management is realized through multi-modal fusion and hierarchical cooperative control.

The invention belongs to the technical field of physical ability analysis and diagnosis, and particularly relates to a latent variable fusion modeling method and system for old people weakness trajectory prediction based on biomechanics. According to the method, the plantar pressure, the joint angle and the electromyographic signal in the gait process of the elderly are collected through wearable sensing equipment, multi-dimensional structured feature parameters are extracted, and a key index group reflecting the weakness degree is generated based on preset weight fusion. And in combination with time sequence fitting and variation trend analysis, dynamic evaluation and risk level judgment of the weakness state of the old people are realized. The system comprises a data acquisition module, a feature extraction module, an index fusion module, a trend analysis module and a result output module, has the characteristics of high accuracy, strong real-time performance, good interpretability and the like, and is suitable for weakness risk screening and rehabilitation intervention guidance.

The invention discloses a badminton player action posture analysis system and method based on multi-sensor fusion, and relates to the technical field of athletic training auxiliary systems.The system comprises a wearable sensing subsystem, an environment sensing subsystem and a central processing and feedback subsystem; the wearable sensing subsystem is arranged on an inertia measurement unit and a pressure sensing insole of a body to collect movement inertia and plantar pressure data; the environment perception subsystem collects global videos through multiple cameras. The central processing and feedback subsystem performs synchronous processing on multi-source data, adopts a hierarchical fusion algorithm, restrains inertia integral drift by utilizing a plantar contact state, reconstructs a three-dimensional skeleton posture and a motion trail in combination with visual key points and skeleton restraint, drives a digital twin model, compares with a standard motion model, and performs three-dimensional motion control on the three-dimensional skeleton posture and the motion trail. Quantitative evaluation results such as joint angle deviation, time sequence difference and pressure center track are generated, and visual feedback is performed through a display device and an augmented reality terminal for badminton training evaluation and technical deviation correction.

The invention discloses a chronic disease dynamic monitoring and intervention system based on multi-modal data fusion and deep learning, and the system integrates a wearable sensing technology, a multi-modal data fusion algorithm, a deep learning model (DeepSeek) and a dynamic intervention strategy, and achieves the real-time monitoring, risk prediction and personalized management of chronic diseases. The system has the following functions: performing filing by inputting user information, performing reasoning processing by using a deep model after data acquisition, outputting short-term prediction and risk assessment results, and performing a dynamic personalized intervention scheme (including emergency event processing, drug dose optimization and behavior intervention strategies).

The invention relates to the technical field of sensing signal processing, in particular to a multi-modal wearable sensing data real-time analysis method based on a layered architecture. The method comprises the following steps: acquiring sensor data, and respectively performing inertial navigation unit data acquisition, goniometer data acquisition and electrocardiogram data acquisition; performing data signal synchronization on the acquired sensor data; performing layered transmission on the synchronized data signal, performing sensor data processing on an edge layer and a fog layer in sequence, and transmitting the processed data to a cloud layer; performing sensor data modeling and comprehensive analysis on the cloud layer; and storing a data analysis processing result. According to the method, human body signals are collected, detection and classification of motion states, analysis of breathing states and monitoring of electrocardiosignals are included, different models and data processing are distributed to different levels for implementation according to different processing difficulties and different required calculation capabilities, and high efficiency of the whole model and algorithm is ensured.

The invention provides a safety three-level management and control system in a complex environment, and the system comprises a first-level wearable sensing unit which is worn by an operator and is used for collecting environment parameters and physiological parameters of the operator and generating corresponding safety monitoring data; the second-stage relay gateway unit is used for establishing two-way communication connection with the first-stage wearable sensing unit through a first wireless self-organizing network so as to receive and gather the safety monitoring data and relaying the gathered data to the outside through a second high-bandwidth self-organizing communication link; and the third-stage monitoring and distributing unit is used for receiving the safety monitoring data from the second-stage relay gateway unit and carrying out centralized monitoring and management on the operation state through a local network or a remote network. The method is suitable for operation real-time scenes in complex environments with serious metal shielding and limited signals, such as mines, factory closed spaces, steel structures, underground facilities and the like.

A wearable device may include a biofluid generation unit, a microfluidic channel for transporting the biofluid, and a sensing module including one or more electrochemical aptamer-based sensors having attached reporters that are displaceable by a target analyte. A wearable device may include electrodes for producing a potential sufficient for exchanging charged particles with the displaced reporters. A wearable device may include a processor for executing computer readable instructions including causing activation of the electrodes to exchange the charged particles with the displaced reporters, receiving a measurement of the exchanged charged particles, determining a concentration of the target analyte in the biofluid based on the received measurement, detecting a circadian rhythm based on the determined concentration; and generating an individualized set of circadian entraining factors based on the detected circadian rhythm to guide a user towards an improved circadian rhythm.

The invention discloses a dynamic risk early warning method and system based on student exercise data. The method comprises the steps that S1, original exercise data of student exercise is collected through wearable sensing equipment; s2, obtaining mapping motion data through an improved near-end depth Q network based on the original motion data and the normal motion data; s3, according to the mapping relation data, obtaining an action risk point through threshold comparison and considering duration time; and S4, performing matching detection on the obtained action risk points through an abnormal action chain type detection mechanism based on motion time-space association to obtain a risk prediction result, thereby avoiding misjudgment of a single tiny deviation as a risk, capturing a damage trend of a continuous abnormal action, scientifically fitting a motion damage occurrence rule, and accurately adapting to various campus motion scenes.

The invention discloses an intelligent monitoring system for tumor nursing. The intelligent monitoring system comprises a wearable sensing terminal group, a patient terminal, a cloud platform intelligent analysis center and a medical care terminal. The wearable terminal is used for collecting multi-modal physiological and behavior data such as electroencephalogram, near-infrared brain blood oxygen, heart rate variability, body movement, electrodermal response and gaits; the patient terminal is used for recording subjective symptoms and completing cognition and exercise task evaluation. The cloud platform intelligent analysis center constructs a multi-parameter individualized baseline through data fusion, noise suppression and time alignment, comprehensively analyzes real-time deviation, electroencephalogram abnormality, cognitive deviation and gait change by using a short-term acute early warning model and a long-term trend early warning model, and generates a risk level. The digital twinborn visualization module is used for dynamically displaying the state of the patient, and the medical care terminal performs clinical response based on the graded alarm board. According to the invention, extra-hospital multi-mode continuous monitoring, individualized trend identification and intelligent early warning can be realized, and the real-time performance of follow-up management of brain tumor patients is improved.

Disclosed are systems and techniques for proximity detection using a wearable sensing device. An example method can include obtaining sensing data based on one or more wireless signals, the sensing data comprising at least one of a proximity measurement and a distance measurement; determining, based on the sensing data, a spatial relationship between a device associated with a user and a source of the one or more wireless signals; determining whether the spatial relationship satisfies a proximity condition associated with a distance threshold; and based on a determination that the spatial relationship satisfies the proximity condition associated with the distance threshold, triggering a sensory alert via one or more output components.

Disclosed are systems and techniques for proximity detection using a wearable sensing device. An example method can include obtaining sensing data based on one or more wireless signals, the sensing data comprising at least one of a proximity measurement and a distance measurement; determining, based on the sensing data, a spatial relationship between a device associated with a user and a source of the one or more wireless signals; determining whether the spatial relationship satisfies a proximity condition associated with a distance threshold; and based on a determination that the spatial relationship satisfies the proximity condition associated with the distance threshold, triggering a sensory alert via one or more output components.

The invention discloses a wearable sensing system and a respiratory wave generation and respiratory rate estimation method thereof, relates to the field of respiratory processing, solves the problem that respiratory waves and respiratory rates cannot be accurately analyzed based on electrocardiograms, and comprises a data processing module, a stacking convolution module, a respiratory discrimination module, a signal demodulation module and a data calculation module. The data processing module is used for performing initial processing on an electrocardiogram of a person to be detected; the stack convolution module is used for performing stack convolution on the voltage intensity sequence of the heart of the to-be-detected person and the convolution kernels in different convolution layers to obtain a respiratory wave sequence of the to-be-detected person; the respiration discrimination module is used for discriminating a respiration wave sequence of a person to be detected; the signal demodulation module is used for carrying out signal demodulation on the respiratory wave of the person to be detected to obtain a final respiratory wave; and the data calculation module is used for calculating the respiratory rate of the to-be-detected person. According to the invention, accurate analysis of the respiratory wave and the respiratory rate based on the electrocardiogram is realized.

A wearable sensing device (120) and associated method are provided for determining whether or not a drink (112) has been spiked. The sensing device (120) includes a sensor (310) for holding against a container (110) including the drink (112). The sensor (310) includes at least first and second light sources (170) which are configured to be placed against the side of the container (110) to transmit light at first and second wavelengths into the container (110), wherein the first wavelength is different from the second wavelength. The sensor (310) further includes at least one photoreceptor (160) configured to be placed against the side of the container (110) to receive light which has been transmitted into the container (110) by the light sources (170) and reflected out of the container (110). The intensity of the light received by the photoreceptor (160) for the first and second wavelengths is compared with reference characteristics to determine whether or not the drink (112) has been spiked.

The invention provides a personalized training subject adaptive generation and recommendation method for an unmanned aerial vehicle driver, and the method comprises the steps: collecting environment dimensions and physiological and psychological state data through a flight simulator and wearable sensing equipment, carrying out the time-space alignment and normalization processing, discretizing situation parameters through the combination of the statistical features of a sliding time window and a clustering algorithm, and carrying out the adaptive generation and recommendation of a personalized training subject. Forming a standardized situation mode label set; performing off-line modeling on a situation compensation function library by utilizing historical behavior data and situation characteristics to realize accurate evaluation on various situation influence coefficients; in the real-time evaluation, the skill score and the situation adjustment amount are dynamically fused through the gating proportion parameter, personalized correction is carried out based on the personal historical offset characteristics, the stable and adaptive calibrated skill score is output, and the fairness, accuracy and individualization level of training evaluation are effectively improved.

This invention relates to a wearable, integrated, self-powered pressure sensing device based on a flexible zinc-sodium hybrid battery and its fabrication method. The device includes an integrated sensor and a signal processing unit. The integrated sensor comprises a sensing layer, a shared positive electrode layer, and a battery layer arranged sequentially from top to bottom. The signal processing unit is connected to the sensing layer, the shared positive electrode layer, and the battery layer. This invention effectively overcomes the challenges of baseline instability and mechanical failure of flexible devices in complex environments by leveraging the sensitivity enhancement effect of MXene nanosheets, the zero-drift control technology of nylon mesh, and the low-temperature antifreeze mechanism of ethylene glycol (EG). This invention opens up a key path from "modification of basic electrochemical materials" to "portable terminal system applications," truly realizing portable, wide-temperature-range, and long-life in-situ online monitoring of human physiological signals, providing a new design paradigm for the engineering implementation of next-generation high-performance, low-power wearable sensing systems.

The invention is applied to the field of sleep management, and discloses a sleep state recognition regulation and control method and system.The method comprises the steps that piezoelectric signal data captured by piezoelectric sensing equipment and optical pulse signal data captured by wearable sensing equipment in the sleep process of a target user are obtained; body movement artifact removal processing is conducted on the piezoelectric signal data and the optical pulse signal data, and first piezoelectric signal data and first optical pulse signal data are obtained; performing fusion processing on the first piezoelectric signal data and the first optical pulse signal data to obtain a target fusion feature; determining a sleep type of the target user based on the target fusion feature, performing sleep state recognition on the target fusion feature based on the sleep type, and generating a sleep core index corresponding to a recognition result; and according to the sleep type and the sleep core index, generating and executing a sleep regulation strategy of the target user. According to the method, the sleep quality of the user is improved.

The invention relates to an organic heterojunction-based light-temperature dual-response sensor element, which is prepared from a substrate, a source electrode, a drain electrode, a grid electrode, an organic active layer, a dielectric layer and a device encapsulation layer, and is characterized in that the organic active layer is composed of a Rho group and an imide group in a Rho diimide system organic small molecular structure; the compound has a long conjugated molecular structure, and charge transfer and energy level matching can be effectively promoted. The organic heterojunction structure has the advantages that the organic heterojunction structure has an interpenetrating network form, the donor-acceptor interface area is remarkably increased, so that the charge separation efficiency is enhanced, the carrier mobility is improved, the transconductance, the on-off ratio and the stability of a sensor element are improved, and the organic heterojunction structure has good solution processability and can be applied to the field of sensor devices. The method is suitable for large-area and low-cost flexible electronic manufacturing. The invention provides a new material system for light and temperature dual-response sensor elements, and has wide application prospects in the fields of wearable sensing, Internet of Things, environment monitoring and the like.

The invention discloses a rehabilitation treatment evaluation method and system based on wearable equipment, and relates to the technical field of medical rehabilitation. The system comprises a wearable sensing module, a wireless communication module, a cloud data processing module, a man-machine interaction module and an auxiliary stimulation module. The wearable sensing module collects joint movement, muscle movement and stress parameters in a non-invasive mode. According to the method, through closed-loop processes of equipment preprocessing and personalized configuration, adaptive model training, real-time data acquisition, cloud quantitative evaluation and multi-dimensional feedback adjustment, full-chain coverage of'perception-analysis-evaluation-feedback 'of rehabilitation treatment is realized. According to the method, the defect that a cognitive movement therapy lacks quantitative indexes is overcome, the problems that traditional rehabilitation evaluation is subjective and a sensor can only detect and cannot assist in treatment are solved, rehabilitation treatment standardization, individuation and high efficiency are achieved, the labor cost is reduced, the rehabilitation quality is improved, and the method is suitable for rehabilitation scenes such as cerebral apoplexy and movement injury.

Various components, methods, and systems are provided herein for monitoring physiological responses associated with force-impact events using a mouth-wearable sensing device positioned within an oral cavity of a subject. In some embodiments, the device integrates inertial sensing, force sensing, temperature sensing, and optical physiological sensing on a compact intraoral substrate and transmits physiological data wirelessly to an external computing device for analysis and reporting. In some embodiments, the mouth-wearable sensing device is customized using intraoral scan data to generate a three-dimensional oral cavity model and to position one or more sensors to align with anatomical landmarks when worn.

A personal electronic device and wearable sensing system includes one or more dermal-contact electrochemical sensors integrated into one or more skin-contact surfaces of the electronic device, wearable, accessory, or interactive electronic system. The electrochemical sensors obtain measurements indicative of one or more analyte classes associated with a user. One or more additional sensors obtain non-electrochemical measurements associated with the user. A sensor-fusion module derives electrochemical and non-electrochemical features and generates a unified state estimate. One or more inference modules determine a user-state output based on the unified state estimate and a comparison of electrochemical-derived information, or features derived therefrom, to an individualized baseline reference profile associated with the user. In some embodiments, the analyte classes include endogenous physiological markers including glucose-related markers, exogenous exposure analytes, or both. In some embodiments, a credential derived from the individualized baseline reference profile is portable across multiple authorized devices within a trusted ecosystem.

The application provides a blind person discrimination device, system and method based on a distributed optical fiber sensing device.The device comprises an optical time domain reflectometer module, a signal preprocessing module, a signal cutting module, a signal feature extraction module and a signal classification module.The method provides a high recognition rate without video monitoring equipment and wearable sensing equipment, more comprehensively and effectively provides application support for the field of security and blind person travel, enhances the adaptability for different human body recognition, and identifies the source of the human body walking signal accepted by the current device, whether it is a visually impaired user or a normal user, so as to provide related services for the visually impaired user in the future.The long-time and short-time feature joint recognition mode is adopted in the feature extraction algorithm, and the accuracy is further improved.

According to the plug-in type interface between the flexible sensor and the mobile detector provided by the invention, the trapezoidal bulge structure with specific three-dimensional geometric parameters is constructed at the tail end of the polyimide substrate, and mechanical self-locking in the plug-in process is realized through the matching of the self-locking plate at the tail part of the patch and the limiting groove of the connector; therefore, the connection stability and the anti-seismic property are effectively improved. The structure does not need an additional clamping or fixing device, is suitable for a high-density and ultra-thin wearable sensing system, has the advantages of being small in size, high in self-locking force, capable of being repeatedly plugged and unplugged, simplified in manufacturing process and the like, can remarkably reduce the contact failure risk caused by mechanical vibration, and meets the development requirements of high-reliability flexible electronic products in the future. The pluggable interface between the flexible sensor and the mobile detector is arranged between the flexible sensor and the wearable mobile detector, and is suitable for a non-invasive continuous blood glucose monitoring and wearable medical electronic system.

The application provides a silk fabric sensor and application thereof in gesture recognition, and belongs to the technical field of flexible electronics and wearable sensing. X MXene nanosheets are functional fillers, and Ti X MXene nanosheets are loaded on the surface of silk fibers through an immersion coating process to form a core-shell structure conductive fabric, and Ti X MXene and silk fibroin are stably combined through a hydrogen bond network and electrostatic interaction, the water vapor transmission rate of the sensor is more than 2000 g / m²・day, the sensitivity coefficient GF in the low strain region of 0-18% is 149.31, and the sensitivity coefficient GF in the high strain region of 18-32% is 24.57. X MXene nanosheets are functional fillers, and stable combination of MXene and silk fibers is realized through optimization of a preparation process, so that the application has high sensitivity, good air permeability and excellent fatigue resistance, and a high-precision and low-delay gesture recognition and human-computer interaction system is constructed.

The invention discloses a holographic sensing high-voltage live-line anti-mistaken-entry locking method, and relates to a high-voltage live-line anti-mistaken-entry method, which is characterized in that a group of cable temperature information, current information, insulation aging information and partial discharge information which are monitored and acquired by a fusion monitoring device are respectively arranged in various power equipment and are uploaded to a monitoring department after being collected; a detection sampling module of an upper computer main control system of a monitoring department compares collected information with a multi-parameter model module, and after comparison, a parameter comparison module of the upper computer main control system sends information exceeding a preset value to portable sensing equipment of inspection personnel. The inspection personnel read the fault prompt problem of the corresponding cable through the OLED display screen of the inspection personnel carry-on sensing equipment, and the inspection personnel carry-on sensing equipment can carry out a specific maintenance task after receiving an operation notification; according to the invention, the fault information of the cable is obtained by fusing holographic sensing monitored by the monitoring device, and the inspection personnel carry-on sensing equipment ensures the electrified anti-mistaken-entry locking of the inspection personnel.

A wearable sensing device 120 e.g. smart ring for determining whether or not a drink has been spiked includes a sensor for holding against a container e.g. glass 110 including the drink. The sensor in

The invention relates to the technical field of artificial intelligence and self-powered wearable sensing, in particular to an intelligent insole based on a self-powered sensor array and a sole pressure behavior recognition method thereof. The system comprises a self-energized sensor array, a signal acquisition and processing module and a data analysis module which are arranged in an insole, the self-energized sensor array is composed of a plurality of friction nanometer power generation units which are arranged in an array mode, and the friction nanometer power generation units are arranged in the forefoot area, the arch area and the heel area of the insole respectively. Electric signals can be generated under the action of plantar pressure or impact without external power supply. The system performs synchronous acquisition, analog-to-digital conversion and normalization processing on the electric signals output by the friction nanometer power generation units to form multi-channel plantar pressure characteristic signals, and realizes reconstruction of plantar pressure distribution and identification analysis of behavior states based on the characteristic signals. The system can effectively reflect stress differences of different areas of the sole, can be used for distinguishing various motion behaviors such as walking and running, realizes self-energy-supply acquisition, visual perception and behavior recognition of sole pressure information, and has good application prospects in the fields of motion monitoring, gait analysis, health assessment and the like.

The present application relates to the technical field of intelligent monitoring, in particular to an old people fall monitoring and early warning method and system based on multi-source information fusion, the system comprising a visual perception module, a wearable sensing module, a central processing unit and an early warning module. The method is as follows: collecting human posture video stream, human waist three-axis acceleration and three-axis angular velocity, foot pressure, heart rate and blood oxygen physiological data in the monitoring area; respectively analyzing visual posture information, inertial sensing and foot pressure data, and extracting key motion features; comprehensively analyzing the features of the three types of information sources, especially using the MediaPipe framework to efficiently analyze human posture, combining with wearable sensor data, adopting a multi-modal feature fusion strategy based on attention mechanism, improving the accuracy of multi-source information fusion, so as to accurately judge the stability of the current posture of the old people and the fall risk level, and timely trigger the early warning, improving the accuracy and safety of the monitoring.

The invention discloses an antelope migration trajectory tracking and emergency risk avoiding system in a Qinling Mountain high-altitude environment. The system is composed of a wearable sensing unit, an edge calculation and self-adaptive sampling module, a non-uniform space-time gridding trajectory modeling unit, a risk field calculation and risk avoiding decision engine, a multi-channel low-duty-ratio communication and disaster recovery link, an emergency risk avoiding execution and linkage module and an operation and maintenance and safety audit module. By introducing a non-uniform space-time grid, risk field-velocity field coupling, a self-adaptive sampling frequency function, multi-channel link self-adaptive switching and an emergency risk avoiding instruction sequence, high-precision reconstruction of an antelope migration trajectory and dynamic generation of a risk avoiding path under a high-altitude complex terrain are realized; and the availability and the security of the system are kept in a communication limited scene.