242 results about "Gait analysis" patented technology

The invention relates to the technical field of intelligent wearable devices, in particular to an intelligent wearable device data-driven old people falling risk real-time assessment system, which executes the following steps of: acquiring acceleration and angular acceleration signals of an old patient based on a wearable device; inputting the acceleration and angular acceleration signals into the reconstructed gravity center motion state space model to obtain a predicted gravity center position; inputting the predicted center-of-gravity position into a gait analysis model to obtain posture and foot position information; inputting the characteristic parameters into a risk analysis model, and outputting an offline fall risk assessment value; calculating a residual strength index based on calf muscle strength; an entropy index based on the compensation reference point and the plantar pressure compensation gravity center is obtained; the method comprises the following steps of: inputting the current falling risk into a risk analysis model, estimating and correcting the current falling risk, outputting the online falling risk, integrating multi-source data such as acceleration, angular velocity, electromyographic signals and the like, and combining an innovative entropy index, so that the system can comprehensively evaluate the balance state of the old people.

The invention relates to the technical field of human body posture detection and gait analysis, and discloses a portable posture detection and gait analysis method and system. The method comprises the steps that a hardware system is built, the hardware system comprises an embedded visual module, a data processing module and an interaction module, and human body motion video streams are collected through the embedded visual module; performing dynamic foreground extraction on the acquired video stream by adopting a background difference method and a self-adaptive illumination compensation algorithm, and outputting an illumination compensation image; according to the output illumination compensation image, the data processing module is used for increasing time sequence constraint loss on the basis of a YoLoV8 model, positioning key points of a human body and constructing a three-dimensional skeleton motion model; according to the method, a dynamic shielding processing engine and a multi-target decoupling mechanism are utilized, the constructed three-dimensional skeleton motion model is tracked based on a hierarchical association degree measurement function, human body motion data can be collected in real time, and accurate evaluation of postures and accurate extraction of gait features in multiple scenes are achieved.

A gait analysis is provided by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on, within or under a floor. The approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. The floor vibration-based gait analysis framework estimates various gait parameters, including temporal parameters (step, stride, stance, swing time) and spatial parameters (step length, width, angle), and extracts gait health indicators (cadence / walking speed, left-right symmetry, gait balance, initial contact type).

The invention discloses a video gait analysis-based asthenia syndrome evaluation method, medium and equipment, and the method comprises the steps: firstly, synchronously collecting gait videos of different visual angles through a plurality of cameras, and extracting 2D joint point sequences of all visual angles through a stacked hourglass network; then, a high-precision 3D joint point time sequence is reconstructed through a multi-view fusion algorithm, depth features are extracted through a residual connection network, and data of all view angles are dynamically weighted and fused based on the shielding rate and the confidence coefficient; performing sliding window segmentation and downsampling on the 3D sequence, and performing action classification by using an LSTM time sequence model; and finally, key motion parameters are extracted from a classification result to calculate a weakness score, and weakness grade evaluation is realized. According to the method, the problem of single-view shielding is solved through multi-view 3D reconstruction, the evaluation precision is remarkably improved by combining dynamic weighted fusion and time sequence modeling, and compared with a traditional method, the method has the advantages of being non-contact, low in cost and high in objectivity, and is suitable for large-scale screening of community and family environments.

The invention discloses a general electromyographic signal processing method and system based on a large self-supervised model. The general electromyographic signal processing method comprises the following steps: step 1, acquiring a multi-source original multi-electrode channel EMG signal X from an electromyographic acquisition device; and finally, performing data unification processing, and finally converting into a space-time activity diagram with a fixed size of 224 * 224. On the basis of the space-time activity diagram and the fatigue state mark, constructing an AEMG for training according to heterogeneous unlabeled EMG data collected by a collection device; performing light-weight Adapter layer fine adjustment on the pre-trained large myoelectricity model to adapt to gesture recognition muscle force regression gait analysis or rehabilitation evaluation downstream tasks; aiming at the problem that the dimension and the structure of myoelectricity data are not matched due to different acquisition devices, acquisition parts and acquisition tasks, original signals are converted into space-time activity diagrams in a unified format through data unification processing, device differences are represented by combining a sensor embedding module, effective alignment of cross-source data is achieved, and the accuracy of the data is improved. And a basis is provided for large-scale data utilization.

The invention relates to the technical field of biomechanical analysis, in particular to a health data input method and system and a storage medium. The method comprises the following steps: collecting multi-modal data of a subject, and carrying out gait event recognition to obtain a gait event mark; bilateral gait synchronous alignment is carried out according to the gait event marks, bilateral gait cycle data are obtained, and the bilateral gait cycle data comprise plantar pressure data, ground force data, joint angle data and motion state data; plantar pressure power processing is carried out according to the plantar pressure data and the ground force data, and plantar power parameters are obtained; estimating body segment inertial parameters of the subject; joint torque energy processing is carried out according to the body segment inertial parameters and the joint angle data, and joint power parameters are obtained; and performing gait acceleration calculation according to the motion state data to obtain a gait stability index. The accuracy, comprehensiveness and convenience of gait analysis are remarkably improved through a biomechanical analysis technology.

The invention provides a gait analysis method and an early warning system based on multi-source data fusion, three types of original signals are collected through a wearable inertial sensor, a plantar pressure insole and an edge calculation camera, and multi-dimensional motion characteristic parameters are fused through wavelet denoising, low-pass filtering, interpolation alignment and data consistency correction, so that the gait analysis method based on multi-source data fusion is realized. And constructing a user individualized gait feature prototype library. The method further adopts a dynamic causal graph network to model a motion relation between joints, determines an abnormal coupling mode based on causal weight and historical reference, and combines activity context and Bayesian rules to generate a dynamic threshold value to realize risk grading early warning, so that heterogeneous data collaborative analysis precision and abnormal gait detection sensitivity are improved, and the method is suitable for large-scale popularization and application. And support is provided for gait health monitoring and personalized risk management and control under multiple scenes.

The invention relates to an intelligent pressure adjusting insole suitable for a diabetic foot patient, which comprises an intelligent insole structure, a pressure monitoring and gait analysis module, an intelligent fluid control module, a wireless data transmission module, an energy management and power supply system and a user interface and data management module, a pressure monitoring and gait analysis module, an intelligent fluid control module, a wireless data transmission module and an energy management and power supply system are integrated in the intelligent insole structure, and an insole of the intelligent insole structure is of a three-layer structure comprising an upper layer, a middle layer and a lower layer. The intelligent pressure adjusting insole suitable for the diabetic foot patient integrates the functions of high-precision pressure monitoring, gait analysis, intelligent fluid pressure adjustment and wireless data transmission, and aims at effectively preventing and relieving foot complications through intelligent means and improving the comfort and health management level of the patient.

The invention discloses a wearable plantar pressure and three-dimensional gait analysis system, and relates to the technical field of gait analysis, the system comprises a wearable in-shoe plantar pressure analysis subsystem and a three-dimensional gait analysis subsystem; the wearable in-shoe plantar pressure analysis subsystem comprises a pressure sensor module, a pressure integration module, an intelligent transmission module, a detection software module, a power supply module, an instantaneous pressure and balance detection module, a standing balance detection module, a balance and gait detection module and a cloud server; the three-dimensional gait analysis subsystem comprises a hardware unit and a software unit; according to the invention, a wearable plantar pressure and three-dimensional gait analysis collaborative system is constructed, bidirectional verification compensation is realized by means of data fusion of two subsystems, and traditional single-dimensional limitation is broken; pressure sensing and a double-view reconstruction technology are fused, data are dynamically captured, and bidirectional error correction is carried out; the limitation of traditional equipment is solved, the evaluation accuracy and robustness are improved, and a quantitative basis is provided for clinical diagnosis, rehabilitation and the like.

The invention relates to a cerebral apoplexy patient gait analysis and personalized rehabilitation correction method and system based on a big data model, and relates to the technical field of medical rehabilitation, a patient feature data set is generated by integrating multi-modal data including gaits, electroencephalogram, myoelectricity and the like, a rehabilitation critical period is determined by using real-time nerve regulation and control and brain-computer interface data, and the rehabilitation accuracy is improved. The system constructs a personalized rehabilitation scheme according to the optimized feature data set and rehabilitation training parameters, the rehabilitation effect is tracked through a rehabilitation progress evaluation module, the scheme relates to data fusion, dimension reduction, correlation analysis and dynamic adjustment of the rehabilitation scheme, the personalization and effectiveness of rehabilitation training are ensured, and the rehabilitation effect is improved. The invention aims to improve the rehabilitation efficiency and life quality of stroke patients.

The invention relates to the technical field of behavior feature recognition, in particular to a beef cattle oestrus state monitoring method based on dynamic gait analysis. According to the method, multi-source data acquisition, such as dynamic gait video and body temperature data, video preprocessing, such as frame extraction, denoising, abnormal frame recognition and restoration, target tracking, dynamic gait characteristic parameter extraction, such as step length, step frequency, step speed, joint angle change and body gravity center offset, and life data acquisition, such as diet, body weight change and drinking water. A large oestrus monitoring and recognition model integrating attitude analysis, behavior analysis, living state analysis and an integration module is constructed, and after training and iterative optimization, accurate judgment of oestrus, non-oestrus and wig oestrus states of beef cattle is achieved in combination with ear tag identity recognition. By monitoring the dynamic gaits and behavior modes of the beef cattle and combining daily life data of the beef cattle, the problem of misjudgment caused by wig oestrus is greatly avoided, and the monitoring accuracy is improved.

The invention discloses an old people lower limb motion function analysis method and system based on the Internet of Things technology, and belongs to the technical field of data analysis, and the method comprises the steps: deploying a data collection device to collect original data, preprocessing the original data to obtain a first data set, dividing the motion into gait cycles through data calculation, step length, step frequency, support time, swing time and joint angle of each gait cycle are calculated and integrated into a second data set, time sequence dynamic features are extracted to obtain a dynamic feature vector, a gait quality score and a gait degradation trend are obtained through model calculation, gait anomaly detection is performed to obtain a gait anomaly recognition result and a gait risk score, and a gait anomaly recognition result is obtained. And constructing a health intervention suggestion generation model. According to the old people lower limb motion function analysis method and system based on the Internet of Things technology, multi-dimensional data from different sources are fused, and a gait analysis model is adjusted in real time by using an adaptive algorithm, so that accurate early warning and personalized analysis are realized.

The invention discloses a music generation and gait analysis method and system based on human body motion information. The method comprises the steps that human body motion information collected by an inertial sensor installed on a human body is acquired; processing the human body motion information to obtain target human body motion information; generating a music instruction according to the target human body motion information and the user input information; sending the music instruction to an electronic music player, and controlling the electronic music player to generate music according to the music instruction and play the music; and performing gait analysis according to music played by the electronic music player to obtain a gait analysis result. Human body motion information is collected through the wearable inertial sensor, personalized and instant music is generated according to the collected human body motion information, and mapping between human body motions and the music is achieved; the gait difference of the human body is reflected through the generated music, and gait analysis is achieved; the device can be used as a rehabilitation treatment tool to enhance sensory movement synchronism and improve patient treatment enthusiasm.

The invention discloses an intelligent management and control system and method for AI glasses, and the system comprises an environment sensing layer which integrates a millimeter-wave radar, an infrared sensor and a UWB positioning module, and senses the information of the environment where the AI glasses are located; the biological characteristic layer is used for authenticating the identity of the user by adopting an iris recognition and gait analysis composite authentication mode; the strategy engine layer comprises a dynamic permission matrix, a compliance rule base and a risk prediction model, and formulates and executes a corresponding control strategy according to environment perception information and a biological characteristic authentication result; the data protection layer is composed of a trusted execution environment and a federated learning framework and is used for guaranteeing the data security in the AI glasses system; and the man-machine interaction layer is provided with a tactile feedback and bone conduction alarm device, interacts with a user and provides alarm information. Through cooperative work of the layers, comprehensive, intelligent and safe management and control of the AI glasses are realized, so that the AI glasses can stably operate in various complex environments, and more reliable services are provided for users.

The invention discloses a Parkinson's disease gait analysis and diagnosis method based on deep learning, and belongs to the technical field of medical signal processing. The method comprises the following steps: acquiring multi-modal time sequence gait data of Parkinson's disease, preprocessing the multi-modal time sequence gait data, and dividing a data set to obtain preprocessed multi-modal time sequence gait data; the multi-modal time sequence gait data at least comprises foot pressure data, lower limb motion acceleration and lower limb swing angular velocity; designing a feature extraction network structure of a multi-channel convolutional neural, extracting features from the multi-modal time sequence gait data, and obtaining useful features from local to global; establishing a Transform feature fusion module, and introducing a multi-head self-attention mechanism to process all input useful feature data in parallel; a softmax classification diagnosis module is provided, Dropout and regularization technologies are adopted to avoid overfitting of data, an Adam optimizer is utilized to optimize model parameters, and a Parkinson's disease gait analysis diagnosis result is obtained.

The invention discloses a mouse visual detection method and system based on visual motion response and gait analysis, and relates to the technical field of animal visual detection. The mouse visual detection method based on visual motion reaction and gait analysis comprises the following steps: arranging eight groups of spatial frequency change or contrast change rotating gratings in a visual motion detection box body, combining every two of the eight groups of spatial frequency or contrast gradients into one stimulation condition, forming four stimulation conditions, and performing visual motion detection on the four stimulation conditions; sequentially stimulating the to-be-tested mouse under four stimulation conditions; the head follow-up of the mouse is monitored in real time through the visual motion reaction system, and the real-time rotation speed of the head is obtained; the moving information of each foot of the mouse subjected to visual motion reaction detection is acquired in the active gait detection box with the red and green mixed light source, and the motion coordination is evaluated. The mouse vision-perception-movement loop can be effectively evaluated by combining the visual movement response and the gait movement.

The invention relates to a lower limb motion intention recognition method based on surface electromyogram (sEMG) fusion features. An sEMG feature fusion model and a genetic radial neural network model are included. The sEMG is widely applied to gait analysis and robot control, and due to the fact that the accuracy rate of intention recognition is low and development of an exoskeleton robot is limited, a genetic radial neural network algorithm based on sEMG multi-feature fusion is provided to achieve accurate recognition of eight lower limb actions. An original sEMG signal is collected through a gait experiment, and time domain, frequency domain and wavelet decomposition signal characteristic parameters of the original sEMG signal are extracted and fused. And using a principal component analysis method to carry out dimension reduction on fusion features, and inputting sEMG fusion feature vectors into a radial basis function neural network optimized by a genetic algorithm to carry out training and identification. According to the method provided by the invention, the sEMG fusion features are utilized to identify the human body lower limb actions, the accurate identification of the lower limb actions is realized in combination with a high-performance neural network algorithm, and support is provided for the application of sEMG signals to an intelligent rehabilitation robot.

The invention discloses a following shooting-based mouse gait analysis method, which comprises the following steps of: obtaining an initial video image of a mouse activity area, and separating a mouse movement area through a preset background model to obtain a first image; calculating a motion vector of the mouse according to the first image to obtain motion trend data; adjusting the shooting angle and the focal length of the camera according to the motion trend data to obtain a second image; according to the second image, adjusting image brightness distribution, eliminating the influence of uneven illumination, and obtaining a third image; based on the third image, extracting contour features of the mouse, and obtaining gait feature data in combination with a pre-established gait model; according to the gait feature data, updating a tracking path of the camera to obtain a fourth image; enhancing the image definition according to the fourth image to obtain a fifth image; based on the fifth image, key joint positions of the mouse are recognized through a deep learning model, and a gait analysis report is generated in combination with gait feature data.

The invention discloses a gait analysis method, system and equipment. The method comprises the following steps: acquiring a pressure value of each measuring point in a sole area in real time when a tested person stands or walks; generating a pressure distribution map and a change curve map based on the acquired pressure values; gait-related parameters are calculated and an electronic report is generated. In order to improve image visibility and analysis precision, a perceptual disturbance guided multi-scale color coding mechanism is adopted, and a high-resolution and vision enhanced pressure distribution diagram is generated through the steps of interpolation expansion, normalization, disturbance enhancement, HSV color wheel mapping and the like. The system also supports two analysis modes of balanced standing and dynamic walking, can output multi-dimensional gait indexes including pressure parameters, gait cycle parameters, pressure center tracks and the like, is matched with a built-in high-density sensor array and a man-machine interaction module of the system, is widely suitable for scenes of rehabilitation medical treatment, sports science, old-age health management and the like, and has wide application prospects. The method has the advantages of high detection precision, visual result, comprehensive analysis and the like.

The present invention relates to a method for creating an individualized machine learning inference model. In accordance with the invention, the aforementioned method and a related system involve a motion capture device adapted to be worn by a user. The motion capture device is adapted to acquire measurements, which are used to compute a first estimate of one or more gait parameters. Next, a database is accessed that contains previously collected observations of gait data and the first estimate is compared to the previously collected observations of gait data. Finally, the subset of previously collected observations of gait data that is most informative for the particular user is identified and the individualized machine learning inference model can be developed using the identified subset of previously collected observations of gait data.

The invention discloses a lumbar postoperative functional recovery evaluation method based on gait analysis, and the method comprises the steps: collecting a multi-modal gait multi-source signal, and building high-quality space-time synchronous multi-modal gait data through standardization processing and abnormity elimination; further extracting multi-point features such as an action sequence, pressure and myoelectricity, and realizing deep association between gait features and clinical evaluation indexes by adopting graph structure fusion and knowledge graph mapping; a graph neural network and an attention mechanism are introduced, the causal relationship between gaits and lumbar function recovery is reasoned, self-supervised dynamic optimization is performed according to individual differences, the objectivity, interpretability and adaptive ability of rehabilitation evaluation can be improved, and the rehabilitation accuracy is improved. And reliable data basis and causal traceability are provided for medical decision and personalized rehabilitation.

The invention discloses a Parkinson's disease patient rehabilitation training method based on video gait analysis, relates to the technical field of image analysis, solves the problem of dependence on subjective evaluation in the prior art, and realizes objective quantitative analysis of a rehabilitation effect. An athletic ability index Xpzs, a gait stability index Yb and a lower limb rehabilitation progress coefficient Ws of a patient are calculated and evaluated to ensure that an evaluation result is more reliable and accurate; meanwhile, an established real-time data feedback mechanism is combined with historical data, the problem that rehabilitation effect monitoring is insufficient is solved, and through regular tracking and effect evaluation, a training scheme and a rehabilitation target are dynamically adjusted, and it is ensured that the rehabilitation progress of a patient is tracked in a whole cycle; besides, the method is high in technical adaptability, personalized analysis can be provided according to illness states and rehabilitation progresses of different patients, the training intensity and the rehabilitation target can be dynamically adjusted, the flexibility and adaptability of a rehabilitation scheme are improved, the method is particularly suitable for patient groups with different illness states, and the rehabilitation effect is improved.

The invention belongs to the technical field of intelligent shoes, and particularly relates to an intelligent anti-falling shoe. The system is composed of a setting module, a recognition module, a reminding and judging module, an uploading and storing module and a positioning and calling module, the setting module inputs basic information of a shoe wearer, and activity areas, activity time and emergency contacts are set in a personalized mode; the recognition module is integrated with a high-precision sensor, accurately recognizes road surface accumulated water, obstacles and road surface unevenness, and automatically sends a prompt to a medical worker when a shoe wearer exceeds a preset range in combination with a set activity area and activity time; by integrating a plurality of module structures, the system has multiple functions of environment recognition, gait analysis, emergency call and the like, and comprehensive protection from prevention to emergency treatment is realized.

According to the gait event detection method based on the built-in sensor of the smart phone and deep learning, diversified gait features can be accurately understood and recognized, high efficiency and convenience are achieved, interference on real gait information is effectively reduced, a more reliable gait event detection result is ensured, and the gait event detection efficiency is improved. And a widely used tool is provided for gait analysis in a non-laboratory environment. The method comprises the following steps: collecting real-time acceleration, angular velocity and equipment rotation angle data generated during walking through a built-in sensor of the smart phone; noise in the original data collected by the sensor is removed; excluding sensor data with small fluctuation amplitude; constructing an input and output data pair by using a sliding window mode; modernTCN is used as a basic model architecture, and a bidirectional gating cycle unit BiGRU and a cross attention mechanism are introduced; training a model by using the constructed data set; and inputting real-time data collected by a built-in sensor of the smart phone into the trained model, and outputting a corresponding gait event detection result by the model.

The invention belongs to the field of auxiliary diagnosis, and discloses a gait analysis and image recognition technology-based diagnosis auxiliary system, which comprises a wearable sensor module, a video shooting module, a recognition module and an auxiliary diagnosis module, the wearable module comprises a plurality of sensors used for acquiring monitoring data of the position where the wearable module is located and sending the monitoring data to the recognition module and the image shooting module. The video shooting module comprises a control unit, a shooting unit and a transmission unit; the control unit is used for acquiring a shooting frame rate based on the monitoring data; the shooting unit is used for shooting the subject based on the shooting frame rate to obtain a video containing action information of the subject; the transmission unit is used for sending the video obtained by the shooting unit to the identification module; the identification module is used for identifying the monitoring data video and acquiring a first action parameter and a second action parameter of the action of the subject; the auxiliary diagnosis module is used for obtaining an auxiliary diagnosis result based on the first action parameter and the second action parameter.

The invention provides a gait analysis and diagnosis method and device, electronic equipment and a readable storage medium, and relates to the field of artificial intelligence. The method comprises the following steps of: performing analysis and judgment according to the specific condition of a tested object to obtain a proper data sampling method, recording the tested object by using a monocular camera or a multi-view camera system, predicting a three-dimensional key point coordinate track of the motion of the tested object by using a visual model corresponding to the shooting method, and determining the three-dimensional key point coordinate track according to the predicted three-dimensional key point coordinate track. And performing motion reconstruction by using the coordinate track of the key point to obtain a diagnosis basis, and performing comprehensive analysis and diagnosis on the tested object through an artificial intelligence algorithm.

The utility model discloses an animal gait analysis device, and belongs to the technical field of medical research. Comprising a limb marking cage and a linear gait analysis footpath, and the gait analysis footpath is connected behind the limb marking cage; the limb marking cage comprises a supporting bottom frame, a left surrounding plate, a right surrounding plate, a front lifting door, a rear lifting door and a cover plate, a hollow cavity is defined by the supporting bottom frame, the left surrounding plate, the right surrounding plate, the front lifting door, the rear lifting door and the cover plate, a vertically-through hole shaped like a Chinese character'tian 'is formed in the supporting bottom frame, and a groove position used for containing ink is formed below the hole shaped like the Chinese character'tian'. A drawing plate is movably arranged on the hole shaped like the Chinese character'tian '; the gait analysis footpath comprises a bottom plate, a surrounding plate, a front door plate and a rear door plate, the bottom plate, the surrounding plate, the front door plate and the rear door plate define a hollow cavity, an inlet is formed in the front door plate, and an outlet is formed in the rear door plate. It can be guaranteed that an animal walks linearly during a gait experiment, and gait confusion of an operative limb and other limbs is avoided.

The invention discloses comprehensive evaluation and training equipment for lower limb rehabilitation of a patient. The comprehensive evaluation and training equipment comprises an evaluation and training platform and a data processing and displaying device, the upper side of the evaluation and training platform is provided with a gravity balance evaluation area and a plantar pressure point analysis and gait analysis area. The device has multiple purposes, target data set in a program of the processing device are fed back to the processing device through the sensor, so that the training purpose is achieved, and the training result of a patient can be recorded more accurately and completely while the labor cost can be greatly reduced; the data uploaded by the sensor is more accurate compared with the visual inspection of the traditional rehabilitation therapist, meanwhile, the pressure and time numerical values can be displayed more clearly through the display of the software system to remind and stimulate the patient, and the training record of each time is uploaded to facilitate the diagnosis and scheme adjustment; the land occupation of rehabilitation equipment is reduced, the treatment level and the use efficiency are improved, and the application scene is expanded.

The invention discloses a flexible sensor-based stroke falling early warning method and a related device. The method comprises the following steps: according to acceleration, angular velocity, pressure, heart rate and myoelectricity data of a patient; calculating a resultant acceleration, resultant angular velocity and resultant pressure time sequence collected by the waist flexible sensor and each ankle flexible sensor according to the acceleration, angular velocity, pressure, heart rate and myoelectricity data of the patient; calculating a stability coefficient of the waist acceleration sequence and the normal gait template sequence and a balance coefficient between the left and right ankle acceleration sequences according to a multi-scale DTW algorithm; fusing the stability coefficient, the balance coefficient and the pressure distribution data, the heart rate data and the myoelectricity data collected by the flexible sensor to form a multi-modal feature vector; and inputting the multi-modal feature vector into an ANFIS tumble model, and predicting the tumble risk probability of the patient. According to the method, multi-modal data fusion and gait analysis are carried out through fuzzy logic and the neural network, and the accuracy of patient falling risk prediction is improved.

The invention discloses a six-dimensional force measurement matrix system and method for multi-terrain gait analysis of a humanoid robot. The system comprises a modular terrain simulation platform, a six-dimensional force measurement matrix module, a data acquisition and synchronization module and robot gait performance analysis software. The modular terrain simulation platform comprises a rigid leveling module, a slope module and a step module; the six-dimensional force measurement matrix module comprises a plurality of six-dimensional force measurement plates; the data acquisition and synchronization module acquires synchronous matrix data of each six-dimensional force measurement plate; and the robot gait performance analysis software calculates key kinetic parameters and time-space parameters and generates a comprehensive report. According to the invention, three terrains of a rigid flat ground, a slope and steps can be simulated, force and torque of the humanoid robot walking on the terrains are continuously and synchronously acquired through the measurement matrix, and comprehensive, real and accurate evaluation of gait functions is realized. And high-precision and multi-dimensional quantitative evaluation data are provided for gait control algorithm development, dynamic performance calibration and mechanical foot design of the robot.