35 results about "Fall detection" patented technology

A fall detection device and method are provided. In at least some embodiments, the system can utilize sensing devices such as smartwatches or smartphones (e.g., using iOS, Android, or Pebble operating systems) to read a user's vital signs and apply algorithms to interpret these signs. If the patient is interpreted as having a fall, tremor, or seizure, a notification is sent to a designated caregiver via a reporting process. In at least some embodiments, a doctor or other party can log into a protected dashboard and review patient data in real time. Also in at least some preferred forms, the doctor or other party can analyze the patient's medical history. In at least some embodiments, the user / patient can also use the data to track the onset and progression of falls, tremors, or seizures. Embodiments of the invention can be applied, for example, to situations where the patient / user experiences a condition such as epilepsy that makes the patient / user susceptible to falls and related events.

This invention discloses a fall detection method, apparatus, device, and medium. The method includes: determining target noise point detection information corresponding to the target radar detection information of a target person based on personnel radar detection information detected by millimeter-wave radar and noise point detection information detected by an acoustic detection device; determining the detection mode of the target person based on the target radar detection information, and determining target detection information from the target radar detection information and target noise point detection information based on the detection mode; wherein the detection mode includes an obstruction mode, a stationary mode, and a motion mode; and detecting the fall state of the target person based on the target detection information. This invention uses a combination of millimeter-wave radar and acoustic detection for fall detection, enabling continuous fall state detection even when the target person is obstructed by objects indoors, improving the accuracy and efficiency of fall detection; and since both millimeter waves and acoustic waves are invisible waves, user privacy can be effectively protected.

This invention discloses a human fall detection system based on Wi-Fi channel state information, belonging to the field of wireless sensing technology. It includes a Wi-Fi transceiver unit, a data preprocessing unit, a multi-dimensional feature extraction unit, an adaptive threshold judgment unit, a fall recognition inference unit, an early warning output unit, and a local storage unit. This invention eliminates the need for users to wear any devices or engage in any image acquisition, fundamentally eliminating the poor compliance and strong foreign body sensation associated with wearable solutions, as well as the privacy leakage risks of visual solutions. This allows the system to be safely applied in privacy-sensitive scenarios where the elderly frequently engage in activities such as bedrooms and bathrooms. Users can receive 24 / 7 protection without changing their lifestyle. The system effectively filters out static multipath interference from walls, furniture, etc., through static environmental baseline subtraction and extracts human spatial posture features using 2D AoA estimation, accurately distinguishing falls from similar daily actions such as bending over, sitting, and lying down.

There is provided a human falling detection system including an image sensor, a thermal sensor and a microphone. The image sensor captures an image frame that is used to identify a face and a height-width ratio of a human image. The thermal sensor is used as a filter for filtering out a living body and captures a thermal image that is used to identify a height-width ratio of a human thermal image. The microphone records a time stamp of an abrupt sound appearing.

The utility model relates to anti -fall detection buffering positioning technical field, specifically disclose a kind of display anti -fall performance detection buffering positioning mechanism, including locating seat and test box, locating seat is located in the inside of test box, the side of test box is equipped with safety door, the inside of test box far from safety door one side is equipped with lifting linear module, the side of lifting linear module close to safety door is equipped with lifting seat, lifting seat is equipped with displacement sensor, the side of lifting seat close to safety door is equipped with electromagnetic seat, locating seat far from safety door one side is equipped with magnetic attraction seat, locating seat is connected with lifting seat by magnetic attraction seat and electromagnetic seat cooperation, the inside of test box of lifting linear module close to safety door one side is equipped with directional slide bar. The utility model is provided with a series of structures, anti -fall detection vacuum adsorption positioning angle adjustable, satisfy the positioning of different angle display anti -fall performance detection, positioning angle adjustment is convenient and accurate, reliable, and locating seat multiple structure buffering is good.

A fall detection method and a fall detection system, the method comprising: acquiring a first motion signal collected by a first motion sensor worn on the torso of a user, and acquiring a second motion signal collected by a second motion sensor worn on the arm of the user; processing the first motion signal to obtain a first processing result, the first processing result comprising first waveform change information and first posture change information, the first waveform change information comprising at least first acceleration change information; processing the second motion signal to obtain a second processing result, the second processing result comprising at least one of second waveform change information and second posture change information, the second waveform change information comprising at least second acceleration change information; determining whether the user has fallen according to the first processing result and the second processing result, and performing fall alarm when it is determined that the user has fallen. The present scheme can improve the accuracy of fall detection.

A system for pre-fall detection and mitigation includes: at least one wearable sensor device attached noninvasively to a patient, the sensor device continuously generating new sensor data associated with the user; at least one fall impact mitigation device attached noninvasively to the user; and a data system comprising a database and at least one data processing device. The data processing device collects historical laboratory-collected data and historical community-collected data associated with other users from the database, trains and validates at least one predictive model based on the historical laboratory-collected data and historical community-collected data, extracts features from the new sensor data received from the sensor device, predicts, using the trained and validated model, a fall event based on the extracted features, generates instructions to deploy a fall impact mitigation device, and transmits the instructions to the fall impact mitigation device.

This invention discloses a fall detection system and recognition method based on radar echo noise reduction, belonging to the field of human behavior perception technology. The system includes the following steps: In a fall detection and recognition scenario, information on changes in human distance, human movement speed, and human posture collected by millimeter-wave radar is synchronously processed and spliced ​​in chronological order to form a time-series of height changes reflecting the gradual decrease in human height. This invention, by constructing a time-series of continuous evolution of human height and introducing a set of slow-falling behaviors and spatial boundary offset features, effectively identifies the process of slowly falling over furniture. Furthermore, by combining phased judgment, time backtracking, and observation rhythm adjustment mechanisms, it continuously observes and dynamically judges high-risk fall behaviors without relying on violent movement characteristics, improving monitoring reliability and timely alarm.

This invention discloses an intelligent fall monitoring device, belonging to the field of fall monitoring technology, aiming to solve the problem of accuracy in fall monitoring and prediction for the elderly. It employs a collaborative artificial intelligence model architecture, integrating two different-dimensional artificial intelligence models for risk assessment. Model AI1, based on fuzzy logic, classifies fall risk levels into five levels: normal, low, medium, high, and emergency by real-time monitoring of vital signs such as heart rate, blood pressure, and blood oxygen saturation. Model AI2, based on deep belief networks, predicts risk by analyzing daily living activity patterns such as sitting, standing, walking, running, and jumping. The prediction results of the two models are input into a meta-model for comprehensive decision-making, generating the final low, medium, and high risk level prediction. For real-time fall monitoring, this invention proposes a hybrid monitoring algorithm, combining rule-based triggering based on acceleration and posture angle thresholds with AI fine-grained classification to achieve low latency and high accuracy.

The utility model relates to the technical field of automation equipment of semiconductor industry discloses a kind of based on FOSB wafer box handling and boxing robot clamp, including clamp frame assembly, servo drive assembly, anti-falling detection component and two groups of hook claw components.Claw frame assembly contains robot flange and clamp bottom plate, realize and external robot connection;Servo drive assembly is driven synchronous belt operation with servo motor with brake, and symmetrically distributed clamp plate and hook claw component are driven along linear guide rail component sliding;Anti-falling detection component monitors wafer box clamping state and anti-falling by photoelectric component, this clamp replaces artificial handling, improves efficiency, reduces labor intensity, through orientation, force detection and multilayer protection, guarantee wafer box stable clamping, avoid damage and drop, adapt to the automation of semiconductor industry, high-precision, high safety requirement.

This application provides a fall detection method, electronic device, and medium, belonging to the field of visual detection technology. The method includes: acquiring a video segment to be detected; and obtaining a detection result based on the video segment to be detected and a trained deep learning model. The detection result is used to characterize whether a fall has occurred in the video segment to be detected, or whether a fall has not occurred in the video segment to be detected. In the technical solution provided in this application, after acquiring the video segment to be detected, the detection result is obtained through a trained deep learning model. The deep learning model is trained using a first training video with a first label and a second training video with a second label. Therefore, coarse-grained video-level labels are used during the training phase, rather than fine-grained video segment labels or frame-level labels, thereby significantly reducing the workload of video data annotation.

The application discloses a through-wall fall detection and early warning system and method based on a Lora signal, wherein a transmitter and a receiver are placed on the same side under a partition in a room, the receiver is installed with two antennas, can simultaneously receive the signals reflected and diffracted by multiple paths in the environment by the transmitter, and utilizes signal division operation to eliminate the influence of phase offset, so that the transmission signal and the receiving signal clock are synchronized; in order to cope with the problem of weak signal caused by the attenuation of Lora signal transmission through the partition, a time domain aliasing method is used to amplify the signal, the time-frequency spectrum of the signal is extracted, the signal entropy information, the power mutation curve and the signal-to-noise ratio are calculated as the signal characteristics. The data is labeled based on a fuzzy labeling method to train a deep model, and the fall behavior of a person in the room is detected through the model, and if the fall behavior occurs, an alarm signal is sent. The application can distinguish the normal activity of a person from the fall behavior, has a wide sensing range and high sensing accuracy.

This application provides a fall detection method, electronic device, and program product based on human posture image prediction. The method includes: acquiring video frames; converting the video frames into human posture images using a preset posture estimation algorithm; predicting the predicted posture image for the next moment based on historical human posture images using a constraint-based generative adversarial network (CGN); using the human posture image corresponding to the video frame of the target person captured at the next moment as the real posture image; determining the individual posture features of the target person based on the real posture image using a preset individual posture recognition strategy; determining the error features between the predicted posture image and the real posture image based on the loss function of the CGN; and determining the detection result representing whether the target person has fallen based on the individual posture features and the error features. This improves upon the problems of weak generalization ability, unstable detection results, and cumbersome video annotation in traditional fall detection methods.

A computer-implemented method for assessing a fall risk of a user is provided. The computer-implemented method includes obtaining data indicative of the user engaging in a fall risk activity. The computer-implemented method includes adjusting a fall detection threshold of a wearable computing device worn by the user based, at least in part, on the data indicative of the user engaging in the fall risk activity. The method includes providing a notification indicative of the user being at risk of falling due, at least in part, to the user engaging in the fall risk activity.

This invention discloses a computer vision-based method and system for fall detection and alarm in elderly Alzheimer's patients. To address the difficulty in accurately identifying patient instability and falls in the presence of assistive devices such as canes, walkers, and wall handrails, this invention employs video preprocessing and online camera parameter estimation, implicit 3D reconstruction of the human body and assistive devices, construction of a ternary hypergraph of the human-assistive device-ground relationship with risk-gated attention, inference of contact state and grip stability using a dwell state memory unit in a contact Transformer, calculation of stability index, risk rate, and time to instability based on a differentiable physics model, and setting an adaptive alarm threshold for tiered alarm output. This achieves real-time identification of patient instability and fall risk under conditions of carrying or using assistive devices, improving accuracy and reducing false alarms.

The present application relates to the technical field of fall detection, and relates to an old person fall detection and alarm system based on a multi-modal sensor.The present application collects the height of the center of gravity from the ground in real time, combines the allowable height deviation of the detection object, analyzes whether the center of gravity is moving downward, calculates the azimuth of the center of gravity displacement based on the displacement vector of the center of gravity in the reference rectangular coordinate system, constructs a real-time center of gravity feature parameter set, obtains the azimuth of the hands and the distance from the hands to the center of gravity within a set time period, combines the center of gravity feature parameter set and the historical normal behavior data set of the detection object, determines whether the current state is reasonable, if not, analyzes the overall offset consistency based on the azimuth of the center of gravity offset and the azimuth of the hands, combines the time sequence of the height of the center of gravity from the ground and the height of the hands from the ground to determine whether a fall has occurred, reduces the false alarm and missed alarm probability, improves the recognition accuracy of the detection system, and guarantees the personal safety of the old people.

To provide a fall detection device enabling a station clerk or the like to immediately recognize which car of a train a fall event occurs when the occurrence of the fall event is detected while suppressing introduction cost and enabling the station clerk or the like to take appropriate action.SOLUTION: A fall detection device 4 includes a scanning data acquisition unit 14, a fall detection unit 15, a wheel detection area setting unit 16, a wheel detection unit 17, a wheel detection frequency detection unit 18, and a car number specification unit 19. The wheel detection area setting unit 16 sets such that a length L in a car traveling direction is made shorter than a length X between a front wheel position and a rear wheel position of the same car, and is made longer than a length Y between a center position of a rear wheel group of a preceding car of adjacent cars and a center position of a front wheel group of a succeeding car, in a wheel detection area set in a stop position of the rear wheel of the car of a train. The car number specification unit 19 determines that a car stopped in the wheel detection area is n-th of the train when the detection frequency of a wheel detected by the wheel detection frequency detection unit 18 is n.SELECTED DRAWING: Figure 2

This utility model discloses an integrated stove with a fall detection alarm function, comprising: a stove body, a stove panel, and a burner. The stove panel has a fixing through hole, through which a support plate is connected. The support plate has a central hole for the burner to pass through. Connecting parts are symmetrically distributed on the bottom surface of the support plate. The burner base has an outer edge with a limiting through hole that mates with the connecting parts. The connecting parts include a shaft and elastic extension blocks. The elastic extension blocks are distributed around the shaft, and a latch is provided on the outer side of the elastic extension blocks. The latch passes through the limiting through hole to prevent the connecting parts from freely disengaging, thus limiting the support plate to the burner base. This utility model has a novel structure, replacing the bolted connection method used in the prior art for mounting pot supports. The support plate is quick and easy to install and remove, saving time and effort, and avoiding the problem of bolts being affected by oil contamination during installation.

This invention relates to the field of emergency rescue, providing a multimodal perception and retry proactive alarm method, system, and device, including a fall detection module, a life-saving voice monitoring module, a situation judgment module, an alarm module, and an alarm retry module. This invention offers multi-dimensional proactive protection: integrating three major mechanisms—proactive detection, passive confirmation, and scenario fallback—overcoming the limitations of existing technologies with their single-function approach; differentiated response strategies: employing different alarm methods for different levels of inactivity, avoiding excessive disturbance to contacts while ensuring timely response in serious situations; multi-language scalability: the voice wake-up model supports future expansion to multiple languages ​​through model file updates; the pioneering automatic retry function after an alarm significantly improves the success rate of delivering distress messages, avoiding the risk of rescue delays due to a single alarm failure; low false alarm rate and high reliability: strong universality and low cost.

A system for pre-fall detection and mitigation includes: at least one wearable sensor device attached noninvasively to a patient, the sensor device continuously generating new sensor data associated with the user; at least one fall impact mitigation device attached noninvasively to the user; and a data system comprising a database and at least one data processing device. The data processing device collects historical laboratory-collected data and historical community-collected data associated with other users from the database, trains and validates at least one predictive model based on the historical laboratory-collected data and historical community-collected data, extracts features from the new sensor data received from the sensor device, predicts, using the trained and validated model, a fall event based on the extracted features, generates instructions to deploy a fall impact mitigation device, and transmits the instructions to the fall impact mitigation device.

The application provides a human body fall detection method, device and readable storage medium in a bathroom environment, the method comprising: extracting a Doppler spectrum width feature from a range-Doppler spectrum corresponding to a bathroom radar echo signal; wherein the Doppler spectrum width feature is used to represent the width of the detected target speed distribution; determining whether the shower device in the current bathroom is in a working state based on the Doppler spectrum width feature; if the current shower device is in a working state, generating a 4D point cloud image representing human body action information based on the range-Doppler spectrum; and determining the detection result of the human body fall event according to the 4D point cloud image. Through the implementation of the application scheme, the flow state of the shower in the bathroom is judged by extracting the Doppler spectrum width feature corresponding to the radar echo signal, the human body target and the water flow target are distinguished, and the human body action is recognized and processed based on the 4D point cloud imaging technology, so that high-precision detection of human body fall can be realized.

Systems and methods are provided for performing physical activity monitoring and fall detection of a subject using a wearable sensor including at least one audio sensor and at least one of: an accelerometer, a gyroscope, and a magnetometer. The methods include monitoring activity of the subject using the at least one of: the accelerometer, the gyroscope, and the magnetometer, and identifying at least one characteristic motion pattern based on the monitored activity; detecting an apparent fall experienced by the subject based on the identified at least one characteristic motion pattern; monitoring sound provided by the at least one audio sensor following the detected apparent fall; determining whether the detected apparent fall is an actual fall experienced by the subject based on the monitored sound; and communicating an indication of the actual fall to a monitoring system, thereby enabling a response action to be initiated.

An apparatus includes a microphone, a memory, and a processor operably coupled to the memory. The processor is configured to determine an energy level of an audio signal captured by the microphone and determine whether the audio signal indicates a possible fall. The processor is further configured to, upon a determination that the audio signal indicates the possible fall, poll a fall detection module.

The present application provides a kind of personnel fall detection method, device, electronic equipment and storage medium, method includes: obtaining person falls classification model and behavior classification model;Wherein, person falls classification model is obtained by the standing image set of personnel, falls classification model is obtained by the multiple behavior image set of personnel training;Real-time personnel image in the set period is classified based on person falls classification model, to classify whether personnel falls;In response to personnel falls, the image sequence of real-time personnel image in the set period is obtained;Image sequence is classified based on behavior classification model, to classify the behavior sequence of personnel;According to behavior sequence, the probability of personnel fall is calculated, and whether personnel fall behavior exists is determined based on the probability of fall. Therefore, through the personnel falls and behavior sequence classified by person falls classification model and behavior classification model, it is comprehensively confirmed whether personnel fall behavior occurs, and the accuracy of fall detection is improved.

According to an aspect, there is provided a fall detection apparatus comprising one or more processing units configured to: obtain a first input indicative of which one or more of a plurality of fall detection algorithms detected a potential fall of a subject, wherein each fall detection algorithm of the plurality of fall detection algorithms is associated with a respective type of fall and detects a potential fall of the associated type by analyzing a set of movement measurements for the subject, wherein each respective type of fall has an associated initial state of the subject; obtain a second input indicative of a state of the subject prior to the potential fall, wherein the state of the subject is determined by analyzing a set of measurements from one or more sensors in an environment of the subject; compare the determined state of the subject prior to the potential fall to the initial state for each type of fall associated with any potential fall indicated in the first input; and output an indication that the subject has fallen if the determined state of the subject matches the initial state of any respective type of fall associated with any potential fall indicated in the first input.

The application discloses an anti-falling detection method and an anti-falling detection system, which mainly comprises the following steps: obtaining a target environment temperature according to a sample to be detected, and then adjusting an experimental environment temperature to the target environment temperature; obtaining a target height, a target angle and a swing falling state; controlling the sample to be detected to fall from different target heights and different target angles, recording damage degrees after falling, and obtaining corresponding detection data; controlling the sample to be detected to fall in different swing falling states for falling detection, recording damage degrees after falling, and obtaining corresponding detection data; and repeating the falling detection after switching a ground state, and obtaining an anti-falling detection result. It can be seen that the application can comprehensively understand the anti-falling performance of the sample to be detected, can identify potential weaknesses in design and materials, improve the overall durability and reliability of products, ensure that the products are safer and more reliable in the use process of users, and improve the user experience.