Wearable walking monitoring and falling prevention device for old people

Abstract

The invention relates to the technical field of medical instruments, in particular to a wearable elderly walking monitoring and anti-falling device, which comprises an environment sensing module, a state sensing module, an intelligent decision module, an interactive execution module and a communication and emergency module, and is characterized in that the environment sensing module is used for collecting pavement environment information in the forward direction of a user in real time; the state sensing module is used for monitoring gait and body posture data of a user in real time; the intelligent decision-making module is in signal connection with the environment sensing module and the state sensing module and is used for receiving the pavement environment information and the gait and body posture information and carrying out fusion processing so as to evaluate the falling risk and generate corresponding prompt and navigation instructions. The method is used for carrying out real-time fusion analysis on data, early warning is triggered in the tumble risk germination stage, passive tumble consequence handling is upgraded to active risk intervention, the intelligent early warning capability of the device is improved, and the tumble risk is reduced.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, specifically to a wearable walking monitoring and fall prevention device for the elderly. Background Technology

[0003] Existing technologies, such as some smart safety belts, not only detect falls in the elderly but also incorporate a miniature airbag to cushion falls and prevent injuries such as hip fractures. These products are primarily designed for the elderly and are suitable for nursing homes, community healthcare settings, and other similar settings. They can also be used in high-risk occupational safety and military training.

[0004] While the aforementioned products can protect the hips, they have some shortcomings in terms of fall prevention and intelligent early warning. Specifically, the products lack the ability to perceive environmental risks such as road obstacles and slippery surfaces, making it difficult to cope with complex walking scenarios. Furthermore, gait monitoring remains at the level of basic data collection, without in-depth analysis of its correlation with fall risk, and cannot provide personalized safety prompts. Therefore, it is necessary to propose a highly intelligent wearable elderly walking monitoring and fall prevention device. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a wearable elderly walking monitoring and fall prevention device that performs real-time data fusion analysis and triggers early warnings at the initial stage of fall risk. This upgrades the device from passively responding to the consequences of falls to proactively intervening in risks, improving its intelligent early warning capabilities and reducing the risk of falls.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A wearable elderly walking monitoring and fall prevention device, comprising an environmental sensing module and a state sensing module, wherein the environmental sensing module is used to collect road environment information in the user's forward direction in real time; the state sensing module is used to monitor the user's gait and body posture data in real time; and further comprising the following modules:

[0007] The intelligent decision-making module is connected to the environmental perception module and the state perception module respectively. It is used to receive road environment information and gait and body posture information, and perform fusion processing to assess the risk of falling and generate corresponding prompts and navigation instructions.

[0008] The interactive execution module is connected to the intelligent decision-making module and is used to provide prompts to the user based on prompts and navigation instructions;

[0009] The communication and emergency module is connected to the intelligent decision-making module and is used to send a help message to a preset contact when a fall risk is detected.

[0010] Furthermore, the environmental perception module includes a camera, LiDAR, and a light sensor;

[0011] Cameras are used to identify water stains and obstacles on the road surface; lidar is used to measure the distance to obstacles and the smoothness of the road surface; and light sensors are used to detect the intensity of ambient light.

[0012] Furthermore, the state perception module includes an inertial measurement unit and a pressure sensor;

[0013] The inertial measurement unit is used to measure the user's acceleration and angular velocity; the pressure sensor is located inside the insole to monitor the pressure distribution on the sole of the foot.

[0014] Furthermore, the intelligent decision-making module analyzes the following steps:

[0015] S1. Based on the road environment information detected by the environmental perception module and the gait and body posture information detected by the state perception module, perform weighted fusion to calculate the comprehensive fall risk value; and set the classification threshold for the comprehensive fall risk.

[0016] S2. When the overall fall risk value exceeds the first threshold, an early warning instruction is generated;

[0017] S3. When the overall fall risk value exceeds the second threshold, a navigation correction instruction is generated;

[0018] S4. When a user is detected to have triggered a fall risk, an emergency help command is generated.

[0019] Furthermore, the interaction execution module includes an augmented reality projector, bone conduction headphones, and a haptic feedback mechanism;

[0020] Augmented reality projectors are used to project virtual navigation paths and risk signs into the user's field of vision; bone conduction headphones are used to play voice prompts; and haptic feedback mechanisms are used to provide haptic feedback for orientation.

[0021] Furthermore, the communication and emergency module includes a mobile communication unit, a positioning unit, and a voice call unit;

[0022] The mobile communication unit is used to send text messages and make voice calls; the positioning unit is used to obtain the user's location; and the voice call unit is used to establish a voice channel with emergency contacts after a fall occurs.

[0023] Furthermore, the communication and emergency module also includes a video connection unit, which is used to activate the camera after sending a distress message and transmit on-site audio and image information to a preset contact person.

[0024] Furthermore, the intelligent decision-making module is also used to learn the user's walking habits based on historical data from the environmental perception module and the state perception module, and adjust the overall fall risk assessment threshold.

[0025] Furthermore, it also includes a hat, with an augmented reality projector fixedly connected to the top of the hat, bone conduction headphones fixedly connected to the bottom of the hat, and a haptic feedback mechanism located inside the hat; the camera, lidar, and light sensor are all fixedly connected to the outer wall of the hat.

[0026] Furthermore, the haptic feedback mechanism includes a controller and a drive unit. The drive unit is fixedly connected to the outer wall of the cap. An incomplete gear is fixedly connected to the output shaft of the drive unit. Several secondary gears are circumferentially meshed with the incomplete gear. All secondary gears are rotatably connected to the inner wall of the cap. An eccentric wheel is fixedly connected to the side of the secondary gears away from the drive unit. The controller is used to control the operation of the drive unit according to environmental risk factors and gait risk factors.

[0027] The above approach has the following beneficial effects:

[0028] 1. This solution collects information through an environmental perception module and a state perception module, breaking through the limitations of traditional single-dimensional monitoring and achieving dual perception of external environmental risks and individual gait characteristics. The environmental perception module integrates cameras, lidar, and light sensors to identify potential hazards such as road obstacles, slippery surfaces, and lighting. The state perception module uses an inertial measurement unit and plantar pressure sensors to capture gait anomalies such as stride length, center of gravity shift, and plantar pressure distribution. The intelligent decision-making module performs real-time fusion analysis of the two types of data, triggering early warnings at the initial stage of fall risk, upgrading from passively responding to the consequences of falls to proactively intervening in risks. This proactive prevention mechanism can effectively reduce the risk of accidental falls for the elderly when walking in complex environments, and is suitable for daily activity scenarios such as communities, homes, or parks, providing comprehensive safety protection for the elderly when traveling independently.

[0029] 2. This solution integrates augmented reality projection, bone conduction headphones, and a haptic feedback mechanism into the interactive execution module, forming a visual, auditory, and tactile warning system. The augmented reality projector projects navigation paths and risk signs into the field of vision; the bone conduction headphones ensure clear and audible voice prompts in noisy environments without blocking external sounds, guaranteeing auditory safety; the haptic feedback mechanism transmits the location of risks through the vibration of eccentric wheels within the head-mounted structure. This multimodal interactive design fully considers the differences in the perceptual abilities of the elderly, reducing the limitations of single-prompt methods. Simultaneously, the intelligent decision-making module adjusts the warning threshold by learning from historical user data, matching the warning intensity and frequency with individual gait habits and risk tolerance, reducing interference from excessive warnings and improving the long-term acceptance and compliance of the elderly.

[0030] 3. This solution utilizes the adaptive learning capability of the intelligent decision-making module to continuously analyze historical data from the environmental perception and state perception modules, gradually building a user-specific walking habit model. This makes risk assessment more aligned with individual circumstances, reducing user aversion caused by oversensitivity and preventing the omission of high-risk behaviors due to fixed thresholds. With long-term use, it can become a dedicated safety assistant for the elderly, adjusting protective strategies in real time based on changes in their physical condition to achieve dynamic safety management.

[0031] 4. This solution achieves integrated component design through a worn hat, fixing environmental sensing components such as cameras, LiDAR, and light sensors to the outer wall of the hat, while integrating interactive components such as augmented reality projectors, bone conduction headphones, and haptic feedback mechanisms into the head-mounted structure, forming a compact device with multiple uses. The haptic feedback mechanism uses a mechanical structure combining incomplete gears and eccentric wheels, transmitting directional information by controlling the vibrations of the eccentric wheels in different directions. The lightweight head-mounted structure and ergonomic design ensure comfort during extended wear, allowing the elderly to naturally adapt to the device during daily activities without the need for deliberate adjustments.

[0032] 5. The communication and emergency module of this solution not only enables emergency calls for help after a fall, but also constructs a multi-faceted rescue system through video connection, real-time positioning, and voice calls. When a fall is detected, the system automatically activates BeiDou positioning and sends a help message containing location information. Simultaneously, the video connection unit transmits the scene footage to a pre-set contact person, allowing family members or rescuers to intuitively assess the injury and environment and quickly formulate a rescue plan. In daily use, the system can synchronize gait data and environmental risk records to family members' terminals, allowing children to remotely understand the elderly's walking habits and potential risks, combining passive rescue with proactive care. This linkage mechanism not only improves the efficiency of fall rescue but also alleviates the psychological anxiety of the elderly when traveling independently, while reducing the burden of family care. Attached Figure Description

[0033] Figure 1 This is a structural block diagram of the wearable elderly walking monitoring and fall prevention device of the present invention.

[0034] Figure 2 This is an isometric drawing of the wearable elderly walking monitoring and fall prevention device of the present invention.

[0035] Figure 3 for Figure 2 Middle lateral sectional view.

[0036] Figure 4 for Figure 3 Axonometric drawing of the haptic feedback mechanism.

[0037] The reference numerals in the accompanying drawings include: 1. Hat; 2. Augmented reality projector; 3. Bone conduction headphones; 4. Camera; 5. LiDAR; 6. Drive unit; 7. Incomplete gear; 8. Secondary gear; 9. Eccentric wheel. Detailed Implementation

[0038] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0040] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0041] The following detailed description illustrates the specific implementation method:

[0042] The basic implementation examples are as follows: Figures 1-4 As shown: A wearable walking monitoring and fall prevention device for the elderly includes an environmental sensing module, a status sensing module, an intelligent decision-making module, an interactive execution module, and a communication and emergency module. The functions of each module are as follows:

[0043] The environmental perception module is used to collect road environment information in the direction the user is traveling in real time. Specifically, the environmental perception module includes a camera 4, a lidar 5, and a light sensor. The camera 4 is used to identify water stains and obstacles on the road surface. The lidar 5 is used to measure the distance to obstacles and the smoothness of the road surface. The light sensor is used to detect the ambient light intensity.

[0044] In this embodiment, camera 4 uses a 1 / 2.3-inch back-illuminated CMOS sensor (1280×720 resolution, 30fps), integrating an infrared cutoff filter and an 850nm infrared fill light, supporting day / night dual-mode switching. The autofocus algorithm, optimized for elderly scenarios, can clearly capture the outlines and texture details of road surface water stains (identified through color contrast and reflective features) and obstacles (such as steps, manhole covers, pets, etc.).

[0045] The LiDAR 5 uses a solid-state micro-mirror type LiDAR (wavelength 905nm, detection range 0.1m-10m, angular resolution 0.5°×0.5°). It measures the distance to objects in front using the Time-of-Flight (ToF) principle and outputs point cloud data, which can calculate road surface smoothness (based on standard deviation analysis of point cloud height difference) and obstacle three-dimensional dimensions in real time.

[0046] The light sensor uses a digital ambient light sensor (model APDS-9960) with a spectral response range of 300nm-1100nm and a dynamic measurement range of 0.01lux-65535lux. It features built-in automatic gain control (AGC) and integration time adjustment functions, enabling stable output of light intensity data in both strong and low light environments, providing a basis for adjusting camera 4 exposure parameters and prioritizing risk warnings.

[0047] The state perception module is used to monitor the user's gait and body posture data in real time; specifically, the state perception module includes an inertial measurement unit and a pressure sensor; the inertial measurement unit is used to measure the user's acceleration and angular velocity to calculate body posture; the pressure sensor is set in the insole to monitor the pressure distribution on the sole of the foot.

[0048] In this embodiment, the inertial measurement unit (IMU) is a nine-axis IMU module (MPU-9250), which integrates a three-axis accelerometer (range ±16g, resolution 16bit), a three-axis gyroscope (range ±2000° / s, resolution 16bit), and a three-axis magnetometer (range ±4800μT). The sampling frequency is 100Hz. At the same time, the acceleration and angular velocity data are fused by Kalman filtering to realize the real-time calculation of body attitude angles (pitch angle, roll angle, yaw angle) (accuracy ±0.5°).

[0049] The plantar pressure sensor employs a flexible capacitive pressure sensing array (16 sensing units, 0.2mm thick and 8mm in diameter), encapsulated in medical-grade silicone and embedded in the insole at five key biomechanical regions: the forefoot, two arches, three heels, and six inner and outer edges. Each sensor has a measurement range of 0-1000kPa, a sensitivity of 0.5kPa, a sampling frequency of 50Hz, supports data transmission via Bluetooth Low Energy (BLE 5.0), and has a battery life of >8 hours.

[0050] The intelligent decision-making module is connected to both the environmental perception module and the state perception module. It receives road environment information and gait and body posture information, performs fusion processing to assess fall risk, and generates corresponding prompts and navigation instructions. The intelligent decision-making module also learns the user's walking habits based on historical data from the environmental perception module and the state perception module, adjusting the overall fall risk assessment threshold accordingly.

[0051] Specifically, the intelligent decision-making module's analysis steps are as follows:

[0052] S1. Based on the road environment information detected by the environmental perception module and the gait and body posture information detected by the state perception module, perform weighted fusion to calculate the comprehensive fall risk value; and set the classification threshold for the comprehensive fall risk.

[0053] S2. When the overall fall risk value exceeds the first threshold, an early warning instruction is generated;

[0054] S3. When the overall fall risk value exceeds the second threshold, a navigation correction instruction is generated;

[0055] S4. When the overall fall risk value exceeds the third threshold, an emergency help command is generated.

[0056] In this embodiment, the risk factors are quantified as follows:

[0057] Environmental risk factor E transforms the data output by the environmental sensing module into quantifiable indicators, including:

[0058] Obstacle risk The obstacle distance d measured by LiDAR 5 is quantified using an exponential function, and its formula is as follows:

[0059] (1)

[0060] In the formula, d is in meters (m). When d = 0, =0.78, when d=3, Approaching 0;

[0061] Road surface unevenness risk The formula for calculating the standard deviation σ of point cloud height difference is as follows:

[0062] (2)

[0063] In the formula, σ is in meters (m), and when σ > 0.1m, =1, which means it is judged as uneven;

[0064] Light risk Based on optical sensor data, quantization is performed using a piecewise function, and the determination method is as follows:

[0065] When the light intensity is <20 lux, =1, 20-500 lux, =0.2, >500 lux, =0.5.

[0066] The formula for calculating the comprehensive environmental risk value is as follows:

[0067] (3)

[0068] Gait risk factors G are fused with posture and stress data from the state perception module, including:

[0069] Posture imbalance risk Pitch angle calculated based on IMU With roll angle The calculation is performed using the following formula:

[0070] (4)

[0071] In the formula, >15° or >20° =1;

[0072] Plantar pressure distribution risk The offset (Δx, in cm) of the COP trajectory at the center of plantar pressure is calculated using the following formula:

[0073] (5)

[0074] In the formula, when Δx > 5cm, =1;

[0075] Gait stability index The normalized value is determined based on the output of the Long Short-Term Memory (LSTM) model; specifically, for example, by combining the results of personalized training and fine-tuning with the user. The stability-instability boundary is set at 0.6 (i.e., A gait score <0.6 is considered stable, while a gait score ≥0.6 triggers a risk warning. This threshold will be continuously optimized based on the user's historical data (e.g., if the gait is stable for a long time, it will be increased to 0.65, and if there are frequent imbalances, it will be decreased to 0.55).

[0076] The formula for calculating the comprehensive gait risk score is as follows:

[0077] (6)

[0078] Overall Risk Value The dynamic weighted fusion method is used, and its calculation formula is as follows:

[0079] (7)

[0080] In the formula, Environmental adaptability coefficient (home scenario) =0.3, outdoor scene =0.6, automatically switching between the positioning unit and the scene recognition model.

[0081] In this embodiment, the mapping between the grading threshold and the instruction is specifically, for example, setting the first threshold as R1, the second threshold as R2, and the fall threshold as R3.

[0082] The warning command (R1 < R ≤ R2) triggers a lightweight prompt, which is output in combination: bone conduction headphones 3 play contextual voice (such as "There is a step 3 meters ahead", "The ground is slippery, please slow down"); haptic feedback mechanism vibrates in the corresponding risk direction (frequency 2Hz, lasting 0.5s); augmented reality projector 2 projects a yellow risk sign (such as the outline of the obstacle is highlighted, with a diameter of 5cm).

[0083] The navigation correction command (R>R2) initiates active intervention, superimposed with path planning and gait guidance: the augmented reality projector projects green arrows to guide the path (arrow length 30cm, interval 50cm, arrow direction dynamically adjusted according to the safe area detected by the LiDAR 5); the bone conduction headphones 3 provide real-time gait guidance (e.g., "Please adjust to the left by 0.5 meters", "Stance reduced to 30cm"); the haptic feedback mechanism vibrates at high frequency (5Hz) corresponding to the risk direction, reinforcing the warning.

[0084] Emergency assistance command (fall detection) is triggered by multi-condition fusion: Hardware trigger: IMU detects vertical acceleration < -1.5g (weightlessness) and sudden change in posture angle > 45°; Software verification: Plantar pressure sensor detects that the pressure of both feet drops to zero (lasting > 1s) and R > R3; Command output: Immediately start the assistance process.

[0085] The interactive execution module is signal-connected to the intelligent decision-making module and is used to provide users with audio, visual, and tactile prompts based on prompts and navigation instructions. Specifically, the interactive execution module includes an augmented reality projector 2, bone conduction headphones 3, and a tactile feedback mechanism. The augmented reality projector 2 is used to project virtual navigation paths and risk signs into the user's field of vision. The bone conduction headphones 3 are used to play voice prompts. The tactile feedback mechanism is used to provide directional tactile feedback.

[0086] In this embodiment, the augmented reality projector 2 uses a miniature DLP projection module (resolution 854×480, brightness 30 lumens), supports short-throw projection from 0.3 to 2 meters, and the projected image size is adapted to the user's field of vision. It has a built-in ambient light adaptive algorithm that adjusts the projection contrast based on light sensor data to ensure that the virtual navigation path (green arrow, 2cm wide) and risk markers (red exclamation mark, 5cm in diameter) are clearly visible under complex lighting conditions.

[0087] The Bone Conduction Headphones 3 uses an open-back oscillator unit (frequency response 20Hz-20kHz, sensitivity 105dB), using bone conduction to avoid blocking the ear canal. It also integrates dual-microphone noise reduction (ENC environmental noise reduction technology), improving voice prompt clarity by 40% in noisy environments (such as a farmers market). It has an 8-hour battery life and supports Type-C fast charging (10 minutes of charging provides 2 hours of use).

[0088] The haptic feedback mechanism supports multiple vibration modes: low frequency (100Hz, warning), medium frequency (200Hz, direction indication), and high frequency (300Hz, emergency warning). By combining vibration intensity (weak / medium / strong) and duration (0.2s / 0.5s / 1s), it can distinguish various risks such as "obstacles to the left front" and "slippery to the right".

[0089] The communication and emergency module is signal-connected to the intelligent decision-making module and is used to send a distress message to a preset contact when a fall risk is detected. Specifically, the communication and emergency module includes a mobile communication unit, a positioning unit, a voice call unit, and a video connection unit; the mobile communication unit is used to send text messages and make voice calls; the positioning unit is used to obtain the user's location; the voice call unit is used to establish a voice channel with the emergency contact after a fall event occurs. The video connection unit is used to activate camera 4 after sending the distress message and transmit on-site audio and image information to the preset contact.

[0090] In this embodiment, the mobile communication unit supports 4G full network connectivity and 5G NSA dual-mode communication, and adopts a dual-SIM card redundancy design (primary operator card + backup IoT card) to ensure automatic network switching when the signal of a single operator is weak. SMS messages are sent with higher priority than voice calls, and the distress message includes latitude and longitude, time of fall, and risk level (e.g., severe fall - unconscious risk), and uses an encrypted format to prevent information leakage.

[0091] The positioning unit integrates GPS, BeiDou, and base station positioning technologies, achieving an accuracy of 1 meter in open outdoor environments and improving indoor accuracy to 3 meters with Wi-Fi assistance. It also features a built-in positioning drift correction algorithm that automatically activates historical trajectory filtering when a position change exceeding 5 meters per second is detected, preventing positioning deviations caused by signal reflection.

[0092] The video connection unit supports 720p / 30fps real-time video transmission (H.265 encoding) with a latency of less than 2 seconds, and automatically downgrades to 480p when network bandwidth is insufficient. Camera 4 supports infrared night vision (effective distance of 3 meters) to ensure clear images of the scene when a fall occurs at night; the video stream is preferentially stored locally (maximum support for 10 minutes of buffering) and automatically retransmitted to the emergency contact terminal after the network is restored.

[0093] Multi-level assistance process: When a fall is detected, the system sends an assistance text message containing location information to the preset contact 1 (children) after a 10-second countdown (to avoid accidental touches); if no answer is received within 5 seconds, a voice call will be automatically dialed; if no answer is received within 15 seconds, the call will be forwarded to contact 2 (community grid worker); if there is still no response, the community emergency platform will be triggered to alarm (linking the street office or 120).

[0094] Daily safety monitoring: Family members can remotely view today's gait health report (including steps, number of risk events, and percentage of abnormal gait). When the system detects a 50% increase in risk events for three consecutive days, it will automatically send an "attention reminder" to the family member's APP to intervene in potential fall risks in advance.

[0095] In another embodiment, combined Figures 2-4 As shown, this embodiment also includes a cap 1, an augmented reality projector 2 fixedly attached to the top of the cap 1, a bone conduction earphone 3 fixedly attached to the bottom of the cap 1, and a haptic feedback mechanism disposed inside the cap; a camera 4, a lidar 5, and a light sensor are all fixedly attached to the outer wall of the cap 1.

[0096] The tactile feedback mechanism includes a controller and a drive unit 6. In this embodiment, the drive unit 6 is a motor, which is screwed and fixedly connected to the outer wall of the cap 1. The output shaft of the drive unit 6 is keyed and connected to an incomplete gear 7. The incomplete gear 7 is circumferentially meshed with several secondary gears 8, all of which are rotatably connected to the inner wall of the cap 1. An eccentric wheel 9 is screwed and fixedly connected to the side of the secondary gear 8 away from the drive unit 6. The controller is used to control the operation of the drive unit 6 based on environmental risk factors and gait risk factors. In this embodiment, a protective layer is also integrated into the inner wall of the cap 1, and the incomplete gear 7, secondary gears 8, and eccentric wheels 9 are all placed inside the protective layer.

[0097] The specific implementation process is as follows: The controller, as the brain of the haptic feedback mechanism, receives risk orientation coding signals (such as "obstacle to the left front", "slippery road surface on the right side", etc.) from the intelligent decision module. In this embodiment, the risk signal is decomposed into orientation coordinates (such as the X / Y axis offset in the three-dimensional coordinate system, with left being negative and right being positive, front being positive and back being negative) and risk level (levels 1-3, corresponding to weak / medium / strong vibrations). The controller calls the preset gear-eccentric wheel 9 mapping table according to the orientation coordinates and sets the speed of the drive component 6 (level 1 = 500 rpm, level 2 = 1000 rpm, level 3 = 1500 rpm) and duration (0.2s-1s) according to the risk level. If there are multiple directional risks (such as left front + right rear), the controller triggers vibrations in sequence according to priority (front > left and right sides > rear), with an interval of 0.1s, to avoid signal superposition and user confusion.

[0098] by Figure 4 For example, when an obstacle is directly in front of the walking direction, the motor first drives the incomplete gear 7 to mesh with the upper auxiliary gear 8. Then, the motor is controlled to reciprocate, causing the incomplete gear 7 to keep the upper auxiliary gear 8 rotating, while the other auxiliary gears 8 remain stationary. The auxiliary gears 8 drive the eccentric wheel 9 to rotate, generating vibration, thus indicating that there is an obstacle directly in front. When the obstacle is to the left front of the walking direction, using the plane where the incomplete gear 7 is located as a reference, the motor drives the incomplete gear 7 to mesh with the upper and right auxiliary gears 8 (i.e., the state shown in the figure). Then, the motor is controlled to reciprocate, causing the incomplete gear 7 to mesh and rotate only with these two auxiliary gears 8, while the other auxiliary gears 8 remain stationary. This method is used to provide a warning. By analogy, multi-directional tactile feedback warnings can be achieved, improving the practicality of the device.

[0099] This embodiment uses a single motor to drive the incomplete gear 7 to selectively mesh with the secondary gears 8 in different directions. The reciprocating motion of the motor controls the meshing state of each secondary gear 8, causing the secondary gear 8 in the corresponding direction to drive the eccentric wheel 9 to rotate and generate vibration. This achieves tactile warning of obstacles in multiple directions, such as directly in front or to the left front. This simplifies the drive structure of multi-powered warning devices, reduces device complexity and cost, and ensures the accuracy of the warning direction and response efficiency through the mechanical linkage of gear meshing. This effectively improves the practicality and reliability of multi-directional tactile feedback reminders.

[0100] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A wearable walking monitoring and fall prevention device for the elderly, comprising an environmental sensing module and a state sensing module, wherein the environmental sensing module is used to collect real-time road environment information in the user's walking direction; and the state sensing module is used to monitor the user's gait and body posture data in real-time; characterized in that, It also includes the following modules: The intelligent decision-making module is connected to the environmental perception module and the state perception module respectively. It is used to receive road environment information and gait and body posture information, and perform fusion processing to assess the risk of falling and generate corresponding prompts and navigation instructions. The interactive execution module is connected to the intelligent decision-making module and is used to provide prompts to the user based on prompts and navigation instructions; The communication and emergency module is connected to the intelligent decision-making module and is used to send a help message to a preset contact when a fall risk is detected.

2. The wearable elderly walking monitoring and fall prevention device according to claim 1, characterized in that, The environmental perception module includes a camera (4), a lidar (5), and a light sensor; The camera (4) is used to identify water stains and obstacles on the road surface; the lidar (5) is used to measure the distance to obstacles and the smoothness of the road surface; the light sensor is used to detect the ambient light intensity.

3. The wearable elderly walking monitoring and fall prevention device according to claim 2, characterized in that, The state perception module includes an inertial measurement unit and a pressure sensor; The inertial measurement unit is used to measure the user's acceleration and angular velocity; the pressure sensor is located inside the insole to monitor the pressure distribution on the sole of the foot.

4. The wearable elderly walking monitoring and fall prevention device according to claim 3, characterized in that, The intelligent decision-making module's analysis steps are as follows: S1. Based on the road environment information detected by the environmental perception module and the gait and body posture information detected by the state perception module, perform weighted fusion to calculate the comprehensive fall risk value. And set a tiered threshold for comprehensive fall risk; S2. When the overall fall risk value exceeds the first threshold, an early warning instruction is generated; S3. When the overall fall risk value exceeds the second threshold, a navigation correction instruction is generated; S4. When the overall fall risk value exceeds the third threshold, an emergency help command is generated.

5. The wearable elderly walking monitoring and fall prevention device according to claim 4, characterized in that, The interactive execution module includes an augmented reality projector (2), bone conduction headphones (3), and a haptic feedback mechanism; Augmented reality projector (2) is used to project virtual navigation paths and risk signs into the user's field of vision; bone conduction headphones (3) are used to play voice prompts; Haptic feedback mechanisms are used to provide haptic feedback regarding orientation.

6. The wearable elderly walking monitoring and fall prevention device according to claim 5, characterized in that, The communication and emergency module includes a mobile communication unit, a positioning unit, and a voice call unit; The mobile communication unit is used to send text messages and make voice calls; the positioning unit is used to obtain the user's location; and the voice call unit is used to establish a voice channel with emergency contacts after a fall occurs.

7. The wearable elderly walking monitoring and fall prevention device according to claim 6, characterized in that, The communication and emergency module also includes a video connection unit, which is used to activate the camera (4) after sending a request for help and transmit the on-site audio and image information to the preset contact person.

8. The wearable elderly walking monitoring and fall prevention device according to claim 7, characterized in that, The intelligent decision-making module is also used to learn users' walking habits based on historical data from the environmental perception module and the state perception module, and adjust the overall fall risk assessment threshold.

9. The wearable elderly walking monitoring and fall prevention device according to claim 8, characterized in that, It also includes a hat (1), an augmented reality projector (2) fixedly connected to the top of the hat (1), a bone conduction headphone (3) fixedly connected to the bottom of the hat (1), a haptic feedback mechanism set inside the hat (1); a camera (4), a lidar (5) and a light sensor are all fixedly connected to the outer wall of the hat (1).

10. The wearable elderly walking monitoring and fall prevention device according to claim 9, characterized in that, The tactile feedback mechanism includes a controller and a drive (6). The drive (6) is fixedly connected to the outer wall of the cap (1). The output shaft of the drive (6) is fixedly connected to an incomplete gear (7). The incomplete gear (7) is circumferentially meshed with several secondary gears (8). The secondary gears (8) are all rotatably connected to the inner wall of the cap (1). An eccentric wheel (9) is fixedly connected to the side of the secondary gears (8) away from the drive (6). The controller is used to control the operation of the drive (6) according to environmental risk factors and gait risk factors.

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