154 results about "Fall detection" patented technology

The invention discloses a tumble detection method, wearable equipment and a storage medium, relates to the technical field of tumble detection, and discloses a tumble detection method applied to the wearable equipment, the wearable equipment comprises an acceleration sensor and at least one other sensor except the acceleration sensor, the tumble detection method comprises the following steps: acquiring acceleration data acquired by an acceleration sensor based on a time sequence; if the acceleration data accord with the free falling acceleration performance in the first time period and accord with the impact rebound acceleration performance in the second time period, it is judged that a falling impact event is detected; in response to the falling impact event, obtaining a falling detection result determined based on detection data collected by other sensors; and if the tumble detection result is that the tumble event exists, triggering a tumble event response process. On the basis, when falling impact is detected, detection and identification are carried out based on other sensor parameters, so that the falling detection accuracy of impact caused by falling is improved.

The invention discloses a waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

The invention discloses a fall detection method based on an OEF-YOLO model, and is applied to the technical field of computer vision. Comprising the following steps: constructing a data set for training and detecting the performance of an OEF-YOLO model; the YOLOv8 model is improved, and an OEF-YOLO model used for tumble detection is obtained; s2, training an OEF-YOLO model through the data set constructed in the step S1; and the detection effect of the OEF-YOLO model is evaluated through the data set constructed in the step S1. According to the method, the YOLOv8 model is improved through the EMA attention mechanism module, the C2f-ODConv module and the loss function integrated with the Focal Loss, the reliability of fall detection in the indoor scene is improved, and reasonable balance is achieved in the aspects of light weight and high precision.

The invention discloses a tumble detection method and tumble detection equipment, and the method comprises the steps: determining whether a target object triggers a tumble detection instruction or not based on a radar echo signal of a target detection region; the target object is an object needing tumble detection; under the condition that a target object triggers a tumble detection instruction, fast Fourier transform of a distance dimension and fast Fourier transform of a Doppler dimension are respectively carried out on radar echo signals, and a multi-frame feature matrix is constructed; the feature matrix is used for representing distribution information of a radar detection target in a distance dimension and a speed dimension, and the radar detection target comprises a target object; and based on the multi-frame feature matrix, determining a tumble detection result of the target object through a tumble detection model. The tumble detection result of the target object can be more accurately determined, and the identification accuracy of the tumble behavior is improved.

The invention discloses a hanging object tracking and falling detection method and device based on multi-sensor fusion. The method comprises the following steps: monitoring revolutions of a main winch and an auxiliary winch through an encoder to adjust a vertical rotation angle of a camera to ensure that a view angle covers a lifting hook and a lifted object; a six-axis sensor on the lifting hook is used for judging the motion state of the lifting hook, if the lifting hook is in motion, image SIFT feature point displacement is analyzed to calculate background displacement, a mean square error is used for dividing a background area and a motion area, and the positions of the lifting hook and a lifted object are determined in combination with encoder data; based on a visual judgment result and six-axis sensor information, PID control logic is applied to adjust the speed and angle of a camera in real time, so that the hanging object is kept at the center of a visual angle; when the tower crane moves and the hung object stops, the rotating speed of the camera is predicted and adjusted according to the movement law of the tower crane, continuous tracking of the hung object is achieved, and whether the hung object falls off or not is determined according to the multiple hung object positions. By implementing the method provided by the invention, accurate real-time tracking of the hung object can be realized, and the system can effectively work even in a dynamic environment or under an abnormal condition.

A method for determining a truthful label of a fall event is disclosed. The method comprises receiving signals from one or more sensors configured to measure from a distance signals indicative of characteristics of movement of a user, analyzing the received signals using a fall detection algorithm to determine a label indicative of a fall event by the user, initiating a first user interface interaction mode of a user interface, wherein in the first user interface interaction mode, the user interface is configured to receive a first input from the user indicative of a self-label of the fall event, receiving the first input and determining a level of mismatch between the self-label and the determined label. If the level of mismatch is above a threshold, the method further comprises switching the user interface to a second user interface interaction mode, wherein in the second user interface interaction mode, the user interface is configured to receive a second input from the user indicative of contextual information regarding the fall event, receiving the second input, and updating the self-label of the fall event based on the second input received.

The utility model provides an electroencephalogram anti-falling detection cap which comprises an auxiliary fixing frame, two head sleeves, an anti-falling mechanism, a mounting assembly and an electrode slice, and the two head sleeves are oppositely arranged in the auxiliary fixing frame; the anti-falling mechanism comprises a threaded rod, a connecting cylinder, a pushing rod, a first elastic piece, a limiting chuck and a movable limiting assembly, the threaded rod is arranged on the auxiliary fixing frame in a penetrating mode and connected with the connecting cylinder, the connecting cylinder movably sleeves one end of the pushing rod, the other end of the pushing rod is connected with the outer side wall of the head sleeve, the first elastic piece is arranged in the connecting cylinder, and the limiting chuck is arranged in the connecting cylinder. One end of the first elastic piece is connected with the top push rod, the other end of the first elastic piece is connected with the connecting cylinder, the threaded rod is sleeved with the limiting chuck, the limiting chuck is provided with a plurality of clamping positions, the movable limiting assembly is movably and adjustably arranged on the auxiliary fixing frame, and the movable limiting assembly is clamped in one clamping position. The size of the head circumference space can be adjusted so as to adapt to the sizes of the head circumferences of different patients.

Various arrangements for performing fall detection are presented. A smart-home device may transmit radar waves. Based on reflected radar waves, raw waveform data may be created. The raw waveform data may be processed to determine that a fall by a person has occurred. Speech may then be output announcing that the fall has been detected via the speaker of the smart home device.

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.

The application discloses a fall detection method, device and equipment based on laser point cloud. The method comprises the following steps: acquiring multiple frames of point cloud data including current frame point cloud data; for each frame of point cloud data, identifying the key parts of a human body from the point cloud data, determining the key points of the key parts, calculating the displacement of the key points in the vertical direction within the time length of the multiple frames of point cloud data, and judging whether the displacement is greater than or equal to a threshold value; when the displacement is greater than or equal to the threshold value, it is determined that a fall event occurs, and a fall alarm is sent. The method can achieve non-sensing detection, is easy to be accepted by the elderly, and the point cloud data does not collect the biological characteristics of the human body, so there is no risk of privacy leakage. In addition, by identifying the key parts of the human body and judging whether a fall event occurs according to the displacement of the key points of the key parts in the vertical direction, the whole human body does not need to be modeled for the fall action, and the detection efficiency is improved.

A fall detection device (1) according to the present invention comprises a first detection unit (11) that detects a set of skeletal points of a person outside a vehicle traveling on a road from a first image captured by a first onboard camera mounted on the vehicle, a second detection unit (12) that detects a road area indicating the area of the road from the first image, and a determination unit (13) that determines whether or not the person is falling on the road on the basis of the positional relationship between the skeletal points and the road area. In particular, for the positional relationship, the determination unit (13) uses the distance from the ground to the skeletal points calculated from the position information of the skeletal points and the position information of the road area.

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.

The invention relates to a neural network-based real-time human body tumble detection method suitable for an FPGA (Field Programmable Gate Array). The invention relates to the technical field of machine vision, and the method comprises the steps: shooting a falling video, intercepting and screening picture frames from the video, marking human body key points, randomly selecting pictures from a COCO2017 human body key point detection data set, and carrying out the preprocessing to make a human body key point fusion data set; a YOLOv8n pose human body key point detection model is established; and identifying the key points of the human body, preprocessing the identified key points, and sending the key points into a human body falling judgment network for falling judgment. According to the invention, the human body key point detection accuracy of the human body key point detection model under side tumbling and sheltered tumbling is improved, and the hardware resource consumption when the network model is deployed on the FPGA is reduced.

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.

The invention relates to the technical field of escalator, in particular to an escalator device with a human body anti-falling detection function, which comprises an escalator main body, a detection main body and a power supply main body, handrail guardrails are arranged on the front side and the rear side of the escalator main body, and handrail belts are arranged on the outer sides of the handrail guardrails; the escalator comprises an escalator main body, a handrail guardrail, a handrail belt, a rubber drawstring, a detection main body, a rubber plate and a deformation groove, the handrail belt is internally provided with rubber plates which are uniformly distributed, the rubber plates are internally provided with deformation grooves which are uniformly distributed, and one side of each rubber plate is provided with a power supply main body. The escalator main body, the handrail guardrail, the handrail belt, the rubber drawstring, the detection main body, the power supply main body, the rubber plates and the deformation grooves are arranged; the rubber drawstring can be grasped by hands, when falling occurs, the detection main body performs detection, running of the escalator is stopped in time, injury to pedestrians is reduced, deformation can be provided for the bottom of the power supply main body in the handrail belt conveying process through the rubber plate and the deformation groove, and the power supply main body is more stable in the handrail belt running process.

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.

To provide a fall detection device capable of accurately detecting a signal relevant to a fall from radar signals.SOLUTION: A fall detection device according to an embodiment of the present invention comprises: a transceiver configured to transmit a radar signal toward a subject and receive radar signals reflected from the subject; an observation determination unit configured to determine whether or not to enter an observation mode for the subject by analyzing the received radar signals; and a fall determination unit configured to derive a range value between the transceiver and the subject when entering in the observation mode and determine whether the subject has fallen or not based on the range value.SELECTED DRAWING: Figure 1

The application discloses a fall detection method and system based on a wrist inertia sensor, and the method comprises the following steps: acquiring motion data collected by an inertia sensor installed on a human wrist and performing pretreatment to obtain target motion data; constructing a fall detection model and a fall direction detection model based on one-dimensional convolution and a long short-term memory neural network; performing fall detection by using the fall detection model; if falls are detected for a continuous setting number of times, it is confirmed that a fall occurs, then fall direction detection is performed by using the fall direction detection model, and a fall direction detection result is output. The application combines the advantages of a convolution neural network and a long short-term memory neural network to construct the fall detection model and the fall direction detection model, not only realizes automatic recognition of a fall direction, but also improves the accuracy of fall detection; and a judgment method based on multiple continuous sliding window data can effectively avoid that an approximate fall behavior is wrongly judged as a fall.

Activity level and fall detection using accelerometer data is described. In one or more implementations, measurements of a user generated by a wearable monitoring device during an observation period are obtained, the measurements including accelerometer data. Physical steps taken by the user are detected based on peaks in the accelerometer data above a threshold, and a step count is generated based on the detected physical steps within predetermined time epochs. Activity level classifications of the user for the predetermined time epochs are generated based on the step count. The step count and the activity level classification for the predetermined time epochs may then be output, such as a notification or in a user interface. Fall predictions may be generated and output by processing the accelerometer data using machine learning models trained to correlate patterns in accelerometer data to fall events.

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.

The invention discloses a device for acoustically detecting the height of a free falling body, and the device comprises a mobile control frame, the upper end of the mobile control frame is fixedly connected with an N-shaped lifting frame and a detection box, the detection box is located at the inner side of the N-shaped lifting frame, and the inner side of the N-shaped lifting frame is movably connected with a clamp. According to the device for acoustically detecting the height of the free falling body, a clamp is positioned on the inner side of an N-shaped lifting frame, a control motor drives an inner main rotating rod to drive a first bevel gear to rotate, and a second bevel gear on the outer wall of the first bevel gear can drive a vertical rotating rod and a control threaded rod to rotate; the lifting block controls the outer wall of the threaded rod to ascend and descend, meanwhile, the clamp is driven to ascend and descend on the inner side of the N-shaped lifting frame, the clamp can clamp an object to adjust different heights for falling detection, the initial falling height of the object can be accurately controlled, and personal errors caused by manual adjustment of the height of the object can be reduced.

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.

The present application relates to the technical field of fall detection, in particular to a fall detection method and system based on infrared sensors and laser sensors; wherein the system comprises a laser sensor module; an infrared image acquisition module; a data preprocessing module; a human posture recognition module and a fall discrimination module; the detection method comprises the following steps, the fall detection system, characterized by comprising the following steps, acquiring the infrared image of the human posture and the distance information of the human activity; the acquired human posture infrared image is preprocessed by using the retinexformer algorithm, and the preprocessed data set is labeled; the acquired data set is trained in the yolov8 model to obtain a weight file, update the model configuration parameters, and obtain a trained detection model; the trained detection model is added to the posture evaluation of the laser, and the fall and the fall posture are distinguished by the distance threshold and the distance change rate.

1. Name of the designed product: fall monitoring radar. 2. Use of the designed product: for fall detection. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

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.

The utility model belongs to the technical field of drop test, and particularly discloses a drop detection safety protection device which comprises a bottom table, a frame plate is installed on the outer side of the bottom table through bolts, an opening is formed in the front end of the frame plate, a protection door is arranged in the opening in a sliding mode, and a working platform is movably installed on the surface of the bottom table. A side plate is installed at the end of the rear portion of the working platform, a door opening mechanism and a path groove are arranged on the inner wall of the frame plate, a protruding column and a pushing column are arranged on the surface, close to the frame plate, of the side plate and matched with the path groove and the door opening mechanism correspondingly, and a driving mechanism is arranged in the bottom table. According to the utility model, when substandard objects are broken after a falling test, the driving mechanism drives the working platform to move forwards, and the working platform is in an inclined state after extending out, so that the cleaning and the maintenance are facilitated.

The invention provides a video data storage method based on fall detection and a related device. The method comprises the following steps: acquiring video data and inertial measurement data in a movement process of a wearer according to a chest card recorder; preprocessing a sensor sequence corresponding to the inertial measurement data to generate a multi-modal data sequence; extracting a first statistical feature of the multi-modal data sequence in a preset sliding window, judging the statistical feature according to a first threshold condition, and generating a suspected tumble sensor data fragment; inputting the sensor data fragments into a preset deep neural network model for classification operation to obtain the classification probabilities of tumble and non-tumble, performing consistency judgment on the classification probabilities, and outputting a tumble event confirmation signal; and storing the video data according to the tumble event confirmation signal. Through the implementation of the scheme of the application, the integrity of the video data of the chest card recorder can be ensured even if a wearer falls down.

The invention belongs to the technical field of robot detection, and discloses a fall detection method based on a robot. The method comprises the steps that 1, a patrol area of the robot is set, and detection equipment is installed; 2, collecting data parameters collected by the robot and the detection equipment, and processing the data parameters to obtain first processing data and second processing data; 3, fusing the first processing data and the second processing data for analysis, and judging whether the target person has a falling behavior or not; 4, the robot confirms the falling behavior and generates alarm information; 5, generating a corresponding alarm signal response according to the alarm information; the first judgment value can be analyzed and generated according to the image data collected by the robot, the second judgment value can be analyzed and generated according to the physiological data collected by the detection equipment worn by the target person, the falling condition can be detected more accurately by combining the posture change and the physiological change of the target person, and misjudgment or missed judgment is reduced.

To provide a fall detection technique with improved accuracy.SOLUTION: The present invention relates to a fall detection device and a system thereof, and includes a human body sensing unit that senses a motion and a state of a monitoring target to accurately and quickly detect a fall of a user through real-time monitoring, generates the motion and the state as sensing information, and transmits the sensing information to a control unit, a storage unit that stores simulation information for a motion of a human body occurring when the user falls and a fall determination algorithm based on a displacement amount from the sensing information sensed by the human body sensing unit, and a control unit that controls an operation of each component, determines whether the monitoring target falls through the sensing information, the simulation information, and the fall determination algorithm, and generates a determination signal.SELECTED DRAWING: Figure 1