15 results about "Gait speed" patented technology

The invention relates to a walking aid (1) for an individual comprising a chassis (10), a base (20) that is movable at least vertically with respect to the chassis (10) between a low sitting position and a high walking position and a device (30) for modifying the vertical elevation position of the base (20), characterized in that it is equipped with at least one sensor (60) measuring the displacement of the aid (1) while the individual is walking in order to determine a distance travelled, a sensor (70) measuring a difference in position of at least one moving limb of the individual in order to determine at least one item of data from an amplitude of swinging movement of the limb, a step rate, and an asymmetry of the gait, the sensors (60, 70) being coupled to determine the length of the step, the asymmetry of the length of the steps or the walking speed, and an electronic board executing at least one function from among the retrieval of the data from the set of sensors, the storage of the data, the analysis of the data and the transmission of the data to a processing unit.

The invention provides a personalized step speed self-adaptive control method applied to an ankle-assisted exoskeleton, and the method comprises the steps: installing a feedback device at the bottom of a heel and the bottom of a toe, and detecting the acting force of the sole in real time so as to divide four action stages of single-foot walking; and constructing a conversion relation between a first function characterization action duration and the step speed, and constructing a second function characterization step speed and the driving torque. And during walking, the real-time action duration in the middle stage of the supporting action is obtained, the real-time step speed is calculated through the first function, the real-time driving torque is obtained through the second function, and the ankle exoskeleton assistance is controlled according to the real-time driving torque. The driving torque is dynamically adjusted by sensing the gait and the step speed in real time, so that the assisting power fits the walking state of an individual, and the self-adaptive capacity and the assisting power accuracy under the change of the step speed are remarkably improved; and meanwhile, the method has good individuation and generalization capabilities.

Disclosed is a gait analysis device using artificial intelligence (AI) technology, according to some embodiments of the present invention. The gait analysis device may comprise: a camera unit for capturing an image of the gait of a target user in response to a user interaction through a display unit; a gait analysis unit for generating a gait analysis result obtained by analyzing the gait of the target user on the basis of the captured gait image, wherein the gait analysis result includes at least one of a stride length, a gait speed, a swing angle of both arms, an upper body angle, a hip joint angle of both legs, or a knee angle of the target user; and the display unit for outputting the gait analysis result.

A system and method for collecting and analyzing real-time walking and balance data from mobile device users through an opt-in mechanism integrated with smartphone operating systems (iOS and Android). Users voluntarily share their mobility data, including gait speed, stride length, and balance stability, which is anonymized and analyzed using machine learning algorithms. The system provides personalized health insights, such as real-time warnings about fall risks, and generates population-level health trend reports. Aggregated, anonymized data helps public health organizations and healthcare providers identify emerging trends, such as regional increases in fall risks or correlations between medication use and mobility issues. This dual-purpose system bridges individual health monitoring and population-level public health analysis, offering proactive interventions to improve well-being. The system ensures privacy compliance while enabling early detection and prevention of health risks for both individuals and broader communities.

A system and method for managing medication titration using real-time walking and balance data collected from mobile electronic devices are disclosed. Smartphone sensors, including accelerometers and gyroscopes, capture mobility metrics such as gait speed, stride characteristics, postural stability, and fall risk. The mobility data is integrated with additional physiological metrics and a patient's electronic medical records. Analytical algorithms, including machine-learning models, evaluate changes in mobility to identify correlations between medication usage and functional performance. Based on detected changes, the system generates alerts and recommendations to assist healthcare providers in adjusting medication dosages. The invention supports proactive and personalized medication management, particularly for medications that affect balance or fall risk, and improves patient safety by enabling data-driven clinical decision-making using real-world mobility insights.

The embodiment of the application provides a health assessment system and a health assessment method, and belongs to the technical field of health assessment. The health assessment system comprises an assessment chair assembly, a gait guiding assembly and a footpath. The assessment chair assembly is configured to measure the grip strength, heart rate, leg lifting frequency and sit-to-stand state of a subject. The gait guiding assembly is configured to guide the subject to present a preset gait. The footpath is configured to measure the walking speed of the subject. The health assessment system and the health assessment method of the embodiment of the application have corresponding devices for each test, and do not need to paste test marks, thereby improving the assessment environment, almost not needing the participation of assessment personnel in the normal test process, reducing the labor intensity of the assessment personnel, reducing the human consumption, and the test result is relatively objective.

The invention relates to a lower limb exoskeleton real-time gait recognition method fusing spatio-temporal characteristics, which comprises the following steps: step 1, a subject wears an exoskeleton gait recognition device, and motion data under a variable speed condition is acquired; step 2, preprocessing the collected data; 3, performing real-time gait phase division based on multi-sensor fusion; 4, detecting a zero-speed interval based on an adaptive threshold method, and extracting spatio-temporal characteristic parameters such as step length, gait cycle and step speed; 5, constructing a training set and a test set, performing feature importance analysis by using an SVM, and screening key features; step 6, constructing a hybrid model fusing a GNN module, an improved TCN module and a BiLSTM module to perform gait phase classification on new data; and step 7, comparing and analyzing a classification result with an identification result of a traditional model. The method aims at achieving the gait recognition purposes of high precision, low cost, stability, comfort, convenience and good real-time performance.

The invention discloses an abnormal gait detection method based on a smart phone camera and image convolution, and belongs to the technical field of mobile health. According to the technical scheme, video data of walking of a subject is collected through a camera of a smart phone; preprocessing the video data, and extracting human body key point coordinates; calculating gait parameters based on the key point coordinates, wherein the gait parameters comprise step length, step speed, support phase time, swing phase time and gait symmetry; constructing a multi-dimensional data set containing static space features and dynamic time sequence features; and training the data set by using a deep learning model to identify an abnormal gait type and evaluate the severity of the abnormal gait type. The method has the beneficial effects that the compliance of a patient and the monitoring universality are greatly improved, noise interference in a video acquisition process can be effectively removed, and the generalization ability of a subsequent model is effectively improved; the model can synchronously complete classification of multiple types of abnormal gaits and grading evaluation of severity; the method has superiority, robustness and clinical application potential in a real scene.

The invention discloses a negative Poisson's ratio metamaterial intelligent kneecap integrated with gait monitoring, and belongs to the technical field of sports protection and rehabilitation medical instruments. The knee pad comprises two magic tapes, two monitoring circuits, a negative Poisson's ratio metamaterial inner-layer fabric, an intelligent protector outer wrapping surface and a sensor switch, the kneecap adopts a rectangular perforated negative Poisson's ratio metamaterial structure, the kneecap transversely expands to disperse pressure when joints are bent through the negative Poisson's ratio effect, light weight and high elasticity are achieved in combination with a nylon base material, and the wearing comfort and the joint stability are remarkably improved. The kneecap is integrated with double MPU6050 sensors which are respectively arranged at the middle sections of the thigh and the shank and are used for acquiring inclination angle data of the lower limb around the X axis in real time; based on an angular velocity-linear velocity conversion algorithm, the gait velocity is calculated in combination with a leg geometric model. The knee pad supports Bluetooth transmission of real-time gait information to terminal equipment, is suitable for posture optimization in exercise training and progress evaluation in rehabilitation treatment, and solves the problems that a traditional knee pad is poor in comfort and single in function.

The invention provides a lower limb gait generation method and device based on dynamic motion primitives, and the method comprises the steps: determining model input parameters based on target terrain parameters and target human motion parameters; based on the model input parameters and a pre-generated first dynamic motion primitive model of a target terrain type, a first direction position curve of target lower limb tail end motion is obtained; based on the model input parameters and a pre-generated second dynamic motion primitive model of a target terrain type, a second direction position curve of target lower limb tail end motion is obtained; and generating a target lower limb gait based on the first direction position curve and the second direction position curve of the target lower limb tail end movement. According to the method, on the basis of the dynamic motion primitive model, personalized, continuous and natural lower limb gait curves adapting to different terrain parameters and step speeds are rapidly generated, the human body motion law is met, and accurate trajectory planning input can be provided for equipment such as exoskeletons and intelligent prostheses.

A driving system of a hybrid gait rehabilitation robot include: a driving unit that is connected to a footrest of the gait rehabilitation robot and transmits a driving force such that the robot operates at a preset speed; a speed detection unit that detects a gait speed of an occupant; and a control unit that controls a speed of the driving unit by comparing the detected speed of the speed detection unit with a speed applied by the driving unit. The driving unit transmits power toward the occupant, but the driving force of the occupant is not transmitted to the driving unit.

Embodiments of footwear to optimize propulsion while swimming in the water and walking on land, in particular, amphibious footwear capable of duty on land and in the water as well as versions that are optimized for predominantly use in the water for swimming. The exoskeleton amphibious footwear reduces metabolic demand of walking and swimming while also improving gait speed and swim speed. This is accomplished by assisting plantar flexion during land mode, and assisting dorsiflexion in water mode.

The application provides a humanoid robot gait naturalness intelligent evaluation training method and device, comprising: acquiring corresponding reference force exertion timing records from a historical force exertion timing database for the working condition label, and determining the feasible range of force exertion timing; when the step speed variation amplitude exceeds a preset safety threshold and the friction resistance level is lower than a preset stability level, adjusting the force exertion timing delay; updating the initial force exertion timing using the adjusted force exertion timing delay, and iteratively comparing and correcting the feasible range of the force exertion timing to obtain an optimal take-off force exertion time; generating a force exertion timing dynamic adjustment scheme according to the comparison between the optimal take-off force exertion time and the metatarsal flexion torque peak time; and performing propulsion power and slipping risk evaluation on the force exertion timing dynamic adjustment scheme, and outputting the verified force exertion timing scheme after confirming that the requirements are met.

A sensor system assesses gait speed and, based on the gait speed, stratifies risk into a risk level for a given subject. The given subject may be an older adult or an individual with a chronic disease. The sensor system includes a sensor module that is configured to use radar- based technology to collect data related to a walking subject. A signal processing unit may process the data to obtain the gait speed of the subject. The gait speed of the subject and / or the related risk level may be communicated in real-time using a numeric screen, color-coded visual display and / or auditory feedback.

Training at the proper level of effort is important for athletes whose objective is to achieve the best results in the least time. In running, for example, pace is often monitored. However, pace alone does not reveal specific issues with regard to running form, efficiency, or technique, much less inform how training should be modified to improve performance or fitness. A sensing system and wearable sensor platform described herein provide real-time feedback to a user / wearer of his power expenditure during an activity. In one example, the system includes an inertial measurement unit (IMU) for acquiring multi-axis motion data at a first sampling rate, and an orientation sensor to acquire orientation data at a second sampling rate that is varied based on the multi-axis motion data.