20 results about "Elderly falls" patented technology

The utility model discloses an old man falling alarm device which comprises a waistband body, an alarm mechanism is arranged at the front end of the waistband body, the alarm mechanism comprises a fixing plate fixedly connected to the front end of the waistband body, and a guide frame is fixedly connected to the front end of the fixing plate. The lower side, close to the guide frame, of the front end of the fixing plate is slidably connected with an alarm device body. According to the falling alarm device for the old people, the waistband main body penetrates through the inner side of the buckle belt ring and drives the alarm device main body to move towards one side, so that the alarm device main body is more tightly clamped by means of the butt joint surface and the contact surface, and the purpose of conveniently wearing the alarm device main body on the waist of the old people is achieved; and the waistband main body is a necessity for the old people to wear clothes, so that the old people cannot forget to wear the alarm device main body, and when the old people walk, the movement range of the alarm device main body is small, and the alarm device main body cannot be triggered easily.

The invention discloses an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home, the intelligent detection system is composed of a plurality of sets of detection devices installed on the ceiling and the wall surface of a living space of the elderly, each set of detection device works independently, and each detection device comprises a multi-modal data acquisition module. Collecting movement, sound and environment data of the elderly; the data preprocessing module is used for carrying out denoising, filtering and feature extraction on the data; the tumble detection module judges whether the elderly tumble based on a deep learning algorithm; the alarm module is used for giving an alarm to an emergency contact person when falling is detected; and the storage module is used for storing various data. The detection method comprises the steps of data acquisition, preprocessing, fall judgment, alarm processing and data storage. The invention belongs to the technical field of smart home and health monitoring, and particularly relates to an intelligent detection system and method for avoiding missing report of falling events of the elderly living at home.

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 application discloses an old people fall risk assessment and dynamic protection intervention system and method, relates to the technical field of computer vision, and comprises a hardware component module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module and an application scenario module.The hardware component module is used for collecting the motion posture, gait and environment data of old people in real time through a multi-modal sensor, and providing hardware support for risk assessment and protection.The application realizes the deep fusion of visual, inertial and environment data through an attention mechanism, combines a light-weight CNN-LSTM network to quickly extract space-time features, and then accurately calculates the dynamic relationship between the center of mass and the support surface based on a biomechanical model, thereby effectively improving the real-time performance of posture analysis and the accuracy of fall risk assessment, reducing the consumption of computing resources, greatly improving the prediction accuracy of the system for the fall risk of old people, reducing the false alarm rate, and providing a reliable basis for a graded protection strategy.

The invention discloses a nursing home old man falling identification method and device based on a multi-modal large model, and the method comprises the steps: firstly obtaining an old man target bounding box coordinate set, and extracting a target region based on the old man target bounding box coordinate set to obtain an old man behavior image set; and then matching a question text for each old person behavior image in the old person behavior image set, inputting the question text into a multi-modal large model, and performing fall detection on the old person behavior image set to obtain a fall analysis answer list. And then semantic analysis and classification operation are performed on the tumble analysis answer list to obtain a tumble sample index set, and tumble event positioning and visual marking are performed on the nursing home video monitoring image based on the tumble sample index set and the old man target bounding box coordinate set to obtain a nursing home old man tumble identification annotation graph. Compared with a traditional detection method, the method has higher recognition accuracy and lower missing report rate.

This invention belongs to the technical field of geriatric clinical rehabilitation equipment, specifically relating to an adjustable geriatric clinical exercise device, including a base, an upper limb exercise component and a lower limb exercise component mounted on the upper surface of the base, and an anti-fall component mounted on the base. The anti-fall component includes a first fixed seat mounted on the upper surface of the base, on which a hydraulic push rod is fixedly mounted. A seat is detachably mounted on the top of the hydraulic push rod, and the rear end of the seat is detachably connected to the backrest. Armrests are also mounted on both sides, and the bottom of the armrests is rotatably connected to the bottom of the seat. The invention incorporates an anti-slip pad on the base to increase the device's stability. The combination of a guardrail, a flexible layer, and a pressure alarm sensor forms comprehensive fall protection. The flexible layer cushions impacts, and the pressure alarm sensor provides timely warnings, reducing the risk of falls and injuries to the elderly. The armrests are foldable, providing support for the elderly to stand up, further enhancing safety.

The application discloses an old people fall early warning method and system based on non-complete sequence and physical checking, and the method comprises the following steps: obtaining the reference standing height of a person to be detected; buffering the video stream containing the person to be detected to a ring buffer with a capacity of N frames frame by frame, updating the buffer and triggering detection after a new frame is entered; performing skeleton key point extraction and normalization on each frame of image to obtain a skeleton sequence; cutting a sub-sequence from the skeleton sequence as a truncated early skeleton sub-sequence and resampling to obtain a standardized early sequence; inputting the standardized early sequence into a trained physical perception double-flow graph convolution network to obtain an initial fall probability of the person to be detected; calculating physical state space features, and combining the initial fall probability to perform hierarchical physical constraint posterior checking to obtain a frame determination result; and issuing a fall warning information when a plurality of continuous frames are determined as falling. The application can warn before falling occurs, and can also maintain high recall rate and high accuracy.

The invention provides a safety supervision system and method for elderly people living alone based on YOLOv11, and belongs to the technical field of intelligent monitoring, and the system comprises a camera module, a behavior recognition module, an anomaly detection and early warning module, a front posture distinguishing module and an alarm module. According to the method, a behavior recognition function of a YOLO11n-pose model is utilized, a frame interval detection frame height comparison algorithm and time analysis are combined, behaviors of the elderly living alone are monitored and recognized, whether the elderly are in a safe state or not is judged, the abnormal behavior that the elderly fall down or do not go into a room for a long time is found in time, and an alarm is given. According to the YOLOv11-based safety supervision system and method for the elderly people living alone, the activity state of the elderly people in a living area is monitored by deploying the camera, behaviors of the elderly people are recognized in real time in a high-precision and low-delay manner, various abnormal scenes are covered, an abnormal alarm is generated, an activity log is recorded, and the safety guarantee level of the elderly people living alone is effectively improved.

The invention discloses a wearable falling alarm for the old, and belongs to the technical field of alarms, the alarm is worn on the waist or the arm of the old, and whether the alarm is started or not is controlled through sliding of conductive metal beads in a trigger; the conductive metal bead in the trigger slides under the action of kinetic energy generated when the conductive metal bead falls down or the gravity of the conductive metal bead to enable two contacts of the trigger to be communicated, so that a power supply of the alarm unit is switched on to give an alarm to attract attention of outsiders and provide help, and the old people who fall down can be rescued as soon as possible.

The invention belongs to the technical field of anti-falling devices. In order to solve the problem that an existing anti-falling device is poor in use effect, the walking aid for preventing the old people from falling is provided and comprises a movable base, a height adjusting connecting rod, a stability maintaining mechanism and a waist surrounding belt. The upper end of the height adjusting connecting rod is connected with the stability maintaining mechanism, and the lower end is connected with the movable base. Through the stability maintaining mechanism, when an old person falls forwards, the old person can be pulled backwards, two arms of the old person are supported, the body balance of the old person is maintained, when the old person falls backwards, the back of the old person can be supported, and the purpose of maintaining the body balance is achieved; the walking aid provided by the invention can maintain the body balance of the old people when the old people fall forwards or backwards, and the use effect is good.

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.

The invention belongs to the field of physiological signal recognition, and provides a kinematics gait monitoring-based old people fall early warning method, which comprises the following steps of: acquiring six-degree-of-freedom gait data of hip, knee and ankle flexion and extension, inward and outward rotation, inward and outward turning and displacement when dominant and non-dominant sides of a three-dimensional motion capture system do flat, upward and downward movement; removing 50Hz electric power interference through notch filtering, reserving gait periodic characteristics through six-order Butterworth low-pass filtering, and adapting to neural network input after transposition; a shallow waveform is extracted through a convolution module, global features are captured through a residual module, high-risk moment features are enhanced through a time attention module, and multi-state features are output; after fusion, full-connection layer classification is carried out to realize real-time fall risk judgment; a gait stage or joint region of interest of the model is visualized based on back propagation. An attention mechanism is introduced to crack a deep learning black box, the model performance, generalization and interpretability are improved, and home and clinical safety monitoring of old people is assisted.

The invention provides an emergency rescue path planning method and system based on old people falling detection, and relates to the technical field of path planning, and the method comprises the steps: collecting real-time video stream data in different regions of a pension community; inputting the real-time video stream data into a tumble detection model, and outputting an old man tumble detection result; when the old people fall down, determining accurate three-dimensional coordinates of the falling old people in a pre-constructed binocular SLAM map through a virtual binocular-scale consistency constraint algorithm; according to the accurate three-dimensional coordinates, semantic information in the binocular SLAM map and contextual information of rescuers, a Pareto optimal rescue path set is planned through a multi-objective optimization search algorithm; and introducing a dynamic weight vector, performing dynamic weighting operation on each Pareto optimal rescue path in the Pareto optimal rescue path set, and selecting the Pareto optimal rescue path with a minimum dynamic weighting result as a final rescue path.

The application relates to an intelligent voice interaction method and system for old people fall rescue, and relates to the field of fall detection, which comprises the following steps: 100, collecting a radar signal; 101, identifying an installation height from the radar signal; 102, determining a monitoring area based on the installation height, and identifying an old people position from the radar signal; 103, comparing the old people position and the monitoring area to determine a monitoring type; 104, determining an old people posture based on the monitoring type; 105, determining a continuous duration according to the old people posture, and determining a monitoring duration based on the old people posture; 106, comparing the continuous duration and the monitoring duration to determine an old people state; 107, generating and sending voice inquiry information in response to the old people state. The application has the effects of improving the accuracy of a fall detector in identifying old people fall and more accurately detecting old people fall.

The invention discloses a community power distribution control and elderly anti-falling linkage system and method based on the Internet of Things. The system comprises an elderly anti-falling monitoring subsystem (bracelet, 5G positioning and falling detection prediction), a community power distribution control subsystem (cloud side monitoring, cloud storage and AI power distribution processing) and a linkage control center (data interaction, strategy library and instruction issuing). The method comprises the steps of system initialization networking, drop resistance and power distribution data acquisition, fall detection and prediction, linkage triggering judgment, strategy matching generation, power distribution and rescue instruction execution, process monitoring recording, system reset and the like, and deep linkage of the two is achieved. According to the scheme, the splitting problem in the prior art is solved, the rescue response efficiency and safety are remarkably improved, the falling risk is reduced, resource utilization is optimized, and the method is suitable for aging community safety management.

This invention discloses an intelligent emergency alarm device for preventing falls in the elderly, specifically relating to the field of mobility aids. The device includes a wheelchair, two multi-functional emergency audible and visual alarm lights (capable of illumination and alarm), two adjustment mechanisms, two armrest assemblies respectively mounted on both sides of the wheelchair, a moving mechanism, a transparent window, an operating screen, and an information display showing user information. By incorporating a moving mechanism within the wheelchair armrest assembly, using a motor to drive a screw and a slider, the information display can be quickly pushed directly below the transparent window. Furthermore, the fall detection module is wirelessly linked with the moving mechanism and alarm lights. When an elderly person falls, the information display simultaneously shows rescue information, and the alarm lights simultaneously trigger an audible and visual alarm, allowing nearby personnel to promptly obtain crucial rescue information and detect the fall.

The invention discloses an old man falling early warning method and system based on multi-dimensional detection, and relates to the field of old man falling early warning. The method comprises the steps of collecting multi-dimensional data in a low-power-consumption mode through an old person bracelet, a common camera and an environment sensor; the edge layer automatically identifies and switches a daytime study and judgment mode and a night response mode according to time and activity intensity; in the daytime mode, high-risk targets are screened through dynamic risk coefficient calculation and camera key point tracking analysis is carried out, and in the night mode, a mattress sensor triggers immediate response; and finally, generating a grading task according to a research and judgment result, sending the grading task to the nearest nursing worker for execution based on Bluetooth positioning, and meanwhile, optimizing the model through feedback. According to the method, the dependence of the system on high-precision hardware and computing power is effectively reduced, the cost is low, the reliability is high, false alarms are remarkably reduced, and privacy is protected; the conversion from passive alarm to active intervention is realized, the falling risk is blocked from the source through man-machine cooperation, and the early warning efficiency is obviously improved.

This invention discloses a safety protection method, system, and storage medium based on radar monitoring, belonging to the field of fall detection technology. The method includes radar detection of the indoor environment and determination of the elderly person's walking path; based on different walking paths, the environment is analyzed, and different warning signals are given; radar detects the elderly person's posture to determine if there is a tendency to fall; if there is a tendency to fall, a posture correction method is given based on this tendency; radar detects changes in the elderly person's movements to determine if a fall has occurred; if the elderly person falls, an indoor camera is activated to take a photo of the elderly person and upload it to the cloud; the elderly person's body information is also detected by radar and uploaded to the cloud; simultaneously, a distress signal is sent to family members or a hospital. This invention uses a correction device worn by the elderly person to correct their posture, thereby preventing falls during walking and avoiding the subsequent burden caused by falls.

The invention relates to an improved YOLOv8-based indoor old people tumble detection method, which comprises the steps of obtaining a UMFD tumble data set, and dividing the UMFD tumble data set into a training data set and a test data set; the method comprises the following steps: constructing an indoor old people tumble detection model based on YOLOv8n, replacing BottleNeck in a C2f module in a YOLOv8n network backbone network with a BottleNeck-ODConv module on the basis of the YOLOv8n network backbone network, fusing the C2f module in a neck network by using a Faster Net module, and fusing an LSKA attention mechanism in SPPF; and finally, an SEAM attention mechanism is embedded into the front end of the detection head. And training and optimizing the model by using the training set, and testing the test set to obtain an indoor old man falling detection model. According to the model, false detection is remarkably reduced in a complex environment, and balance between accuracy and light weight is realized.

The invention relates to the technical field of pressure detection, and provides an old man anti-falling system based on gait assessment, which comprises a data processor, and the data processor is used for executing the following processing strategies: detecting the plantar pressure of the old man in the walking process, and obtaining the gait instability degree of the old man in each single foot support period according to the change of the plantar pressure; according to the gait instability degree and the plantar pressure difference and the time interval difference between the adjacent single-foot supporting periods, the unbalance out-of-control degree of the old person is obtained; the related conditions of the gait instability degree and the walking speed change of the old people in the walking process are determined, and the response delay degree of the old people is obtained in combination with the change trend of the gait instability degree; the imbalance out-of-control degree and the response delay degree are fused to obtain the old man falling risk value, the accuracy of obtaining the old man falling risk value is improved, and then the accuracy and reliability of old man falling risk early warning are improved.