11 results about "Gait analysis" patented technology

This utility model discloses a foot pressure gait device for foot assessment, relating to the field of analytical equipment technology. The foot pressure gait device includes: a monitoring module comprising a housing and a data acquisition unit mounted on the housing; and a buffer pad connected to one side of the housing via a connecting part and capable of flipping relative to the housing. When the buffer pad is flipped to a preset position, the buffer pad and the housing are arranged in upper and lower layers. The technical solution provided by this utility model can solve the problem of insufficient constraint between the gait analyzer and the buffer pad, which leads to an increased distance between the buffer pad and the gait analyzer during use, requiring multiple adjustments of the buffer pad.

The application discloses a gait analysis method based on hand-knee-foot cooperative motion coupling, and the method comprises the following steps: step 1, collecting data by using a flexible insole and an inertial knee ring; step 2, unifying the time axis, posture and position of three nodes of hands, feet and knees, establishing a state vector and an observation equation, updating the state vector, calculating the hand-foot phase difference and the neural coordination score, merging the insole features, the knee ring features and the hand ring features to obtain a multi-modal cooperative feature vector and performing verification; and step 3, based on the calculated attention fusion features after verification, inputting the features into a bidirectional LSTM-CNN network to output a Parkinson's disease gait risk probability, a stroke sequelae gait abnormality risk probability and a fall risk probability, and calculating SHAP values of the contribution degrees of the three modalities of hands, knees and feet and performing credibility judgment. The application realizes the balance between high-precision medical-level analysis and low-cost portability application.

This invention provides a gait analysis-based walking stability assessment and early warning system for disabled elderly individuals, belonging to the field of geriatric health monitoring technology. It includes a data acquisition unit for collecting plantar pressure data, limb posture data, and global motion data during the walking process of disabled elderly individuals. This invention identifies trends of declining stability before falls occur through real-time assessment of gait characteristics, achieving proactive early warning rather than passively responding to fall events. This invention is based on an individual gait baseline for assessment, adapting to individual differences among disabled elderly individuals, avoiding assessment errors caused by universal standards, and reducing false alarms and false negatives. This invention combines the advantages of wearable devices and millimeter-wave radar, ensuring both detailed local gait acquisition and coverage of global motion states, reducing environmental interference and blind spots. The combination of millimeter-wave radar and flexible insoles avoids the privacy risks of video surveillance and is suitable for everyday home use.

This invention relates to a small animal gait analysis device and method based on a circular track. The device includes a circular track, a rotation drive module, a semi-enclosed restraint module, an imaging module, and a gait analysis module. The circular track is used for experimental animals to run on. The rotation drive module drives a rotating shaft to rotate, thereby rotating the circular track. The semi-enclosed restraint module confines the experimental animal to a specific imaging area at the bottom of the circular track. The imaging module captures a dynamic optical image sequence formed by the contact between the animal's feet and the track as it runs. The gait analysis module processes the dynamic optical image sequence acquired by the imaging module and performs gait analysis. Compared with existing technologies, this invention can induce experimental animals to run continuously, improving the initiative and spatial accuracy of gait data acquisition, as well as the accuracy of gait parameter analysis.

This invention relates to the field of graph neural network technology, and more particularly to a Parkinson's gait analysis system based on graph neural network pre-training and dynamic directed graph neural networks. The system includes a data acquisition and processing module, a graph neural network module, a dynamic directed graph convolution module, a temporal convolution module, and a graph encoder. By modeling and learning the topological structure of plantar pressure signals, and based on the learned dynamic graph structure, a pre-designed dynamic graph neural network is used to extract the dynamic features of the plantar pressure signals in the spatial domain, and the temporal convolution module is used to extract the temporal features of the plantar pressure signals. Then, based on the topological relationships of the signals, a pre-trained graph encoder is used to map samples of different categories and with different topological relationships into different graph embeddings. This invention is the first to model the dynamic topological structure of plantar pressure signals and utilizes a dynamic directed graph convolution module to fully exploit the spatiotemporal features of the signals, significantly improving the model's classification ability.

This invention discloses a lower limb motion and plantar pressure synchronous acquisition system and joint angle measurement method based on a master-slave architecture. System: The main control board acquires quaternions from five IMUs (Imported Mutual Arrays) for the waist and legs via I2C; the left and right foot slave boards scan a 45-point plantar pressure array and acquire ankle joint IMU data via SPI interface and DMA state machine, packaging the data into frames with CRC16 checksums and uploading them via serial port. The main control board aggregates the data into 118-dimensional data packets and uploads them at 100Hz. Measurement method: The quaternions of the seven IMUs are normalized and converted into Euler angles; a dynamic time warping algorithm is used to align the reference signal; noise is removed using a fourth-order bandpass filter; mixed time-domain and frequency-domain features are extracted; and joint angles are predicted using a Stacking ensemble learning model. This invention achieves high real-time and high-precision synchronous acquisition and analysis of lower limb multimodal motion information, applicable to gait analysis and rehabilitation training.

This utility model discloses a gait behavior acquisition device, relating to the field of gait acquisition technology. It includes a machine base with a support frame, a gait testing platform, an image acquisition device, and a multi-directional adjustment bracket mounted on the machine base. The middle part of the gait testing platform is rotatably connected to the support frame, so that when the middle part of the gait testing platform rotates around a pivot point, the gait testing platform is tilted relative to the machine base. The multi-directional adjustment bracket connects the image acquisition device to the machine base and adjusts the position of the image acquisition device according to the tilt angle of the gait testing platform, ensuring that the image acquisition range is directly facing the gait testing platform. Different tilt states of the gait testing platform simulate different testing environments, enabling the acquisition of gait images under different testing environments during subsequent gait analysis, thus ensuring the integrity of the gait images during gait analysis.

The utility model provides a kind of wearable gait data acquisition equipment, comprising: braces trousers, for wearing on human body;And at least three sensors, respectively being set in different positions of braces trousers, with human body, for collecting gait data, wherein, sensor is three, respectively first sensor, second sensor and third sensor, first sensor is set in the position of braces trousers corresponding hip, second sensor is set in the position of braces trousers corresponding left leg, third sensor is set in the position of braces trousers corresponding right leg.The utility model is set by braces trousers and three sensors, so that equipment is wearable and close to human body, can be well realized gait data acquisition, to carry out gait analysis, in non-invasive case improve the accuracy of knee osteoarthritis diagnosis, simple operation, cost is lower.

The application discloses a kind of based on deep learning and the fusion portable gait analysis system and method of multi-modal data, it is related to medical rehabilitation technical field, including sequentially connected data acquisition layer, AI posture detection layer, post-processing layer, noise filtering layer, gait analysis layer and data output layer.System acquires RGB image, depth image and infrared image synchronously by depth camera, detects human key points and restores three-dimensional coordinates by using deep learning model after pre-processing, automatically divides gait cycle after combining marker point calibration, multi-stage filtering denoising, calculates joint angle, finally outputs motion data, visual video and stores record.The application can realize high-precision, stable and reliable gait analysis, and the system design is portable and easy to use, suitable for rehabilitation evaluation and motion monitoring scene.

This invention provides a rehabilitation training method, apparatus, device, and medium for gait symmetry optimization, specifically relating to the technical field of rehabilitation medical devices. This rehabilitation training method combines visual feedback walking with field-oriented gait analysis (FES), simultaneously collecting gait symmetry parameters during training to calculate gait symmetry; and also utilizes… f NIRS simultaneously monitors activation in the sensorimotor cortex, supplementary motor area, and premotor cortex, calculates the brain activation symmetry index, and dynamically adjusts at least one parameter of the gait training based on the deviation between the gait symmetry index and the brain activation symmetry index to achieve gait symmetry regulation. This combined intervention most effectively improves gait symmetry, and its mechanism is related to simultaneously enhancing activation in the affected side of the cortex and inhibiting excessive compensation in the healthy side, thereby optimizing the activation balance between the cerebral hemispheres. The positive correlation between gait improvement and brain activation symmetry provides evidence for the neural mechanism of this combined gait training modality.