CN-122004750-A - Multi-equipment cooperative rehabilitation evaluation system and method

CN122004750ACN 122004750 ACN122004750 ACN 122004750ACN-122004750-A

Abstract

The application relates to the technical field of health monitoring, in particular to a multi-equipment collaborative rehabilitation evaluation system and method. The non-contact signal transceiver is used for non-contact monitoring gait and physiological signals, the gait acquisition device acquires plantar pressure data, the intelligent bracelet records movement intensity and physiological indexes, and the central processing unit realizes multi-mode data fusion and analysis. The application solves the problems of single-mode sensing limitation, poor scene adaptability and the like through the cooperative work of multiple devices, realizes all-weather and multi-scene health evaluation, improves gait detection precision, and is suitable for rehabilitation evaluation scenes.

Inventors

CHEN YUAN
WU LINJING
LI KAIXIN
LI LINFANG
ZHANG SHUJIE
Zou Huina
WANG CONGYU

Assignees

厦门大学附属心血管病医院(厦门市心脏中心)

Dates

Publication Date: 20260512
Application Date: 20251110

Claims (10)

1. A multi-equipment cooperative rehabilitation evaluation system is characterized by comprising a non-contact signal receiving and transmitting device, a gait acquisition device, an intelligent bracelet and a central processing unit, wherein, The non-contact signal transceiver is used for monitoring gait, exercise posture, heart rate and respiratory frequency of a user in a non-contact mode; the gait acquisition device is internally provided with a pressure sensor array for measuring plantar pressure distribution and gait dynamics characteristics; the intelligent bracelet is integrated with an accelerometer, a gyroscope and a heart rate sensor and is used for monitoring exercise intensity, type and physiological data respectively; The central processing unit is suitable for receiving the data of the non-contact signal receiving and transmitting device, the gait acquisition device and the intelligent bracelet to generate gait events, and human health data are formed through fusion and analysis of the multi-mode data, and rehabilitation evaluation is carried out according to the human health data.
2. The multi-device collaborative rehabilitation assessment system of claim 1, wherein the non-contact signaling device is configured to construct a three-dimensional spatial model by capturing motion trajectories and posture changes of extremities and to provide macroscopic motion parameters for gait analysis.
3. The multi-device collaborative rehabilitation assessment system of claim 2, wherein the gait acquisition device is adapted to detect heel strike and toe-off events and to perform gait event time-stamp calibration in combination with pressure abrupt signals.
4. The multi-device collaborative rehabilitation assessment system according to claim 1, wherein the gait acquisition device is adapted to record plantar pressure distribution via an array of pressure sensors in combination with spatial displacement estimation by a non-contact signaling device to improve the accuracy of pace calculation.
5. The multi-device collaborative rehabilitation assessment system of claim 4, wherein the gait acquisition device is configured to reflect dynamic load of gait using pressure-time integration and to perform load calibration in conjunction with post-motion velocity or intensity correction of a smart bracelet.
6. The multi-device collaborative rehabilitation assessment system of claim 1, wherein the smart band is configured to continuously monitor heart rate variability and heart rate variability of a user to assess the rehabilitation of a cardiovascular system.
7. The multi-device collaborative rehabilitation assessment system according to claim 6, wherein said smart wristband is adapted to record heart rate variability and number of wakefulness during night sleep and to perform sleep quality assessment in combination with respiratory rate data.
8. The multi-device collaborative rehabilitation assessment system of claim 1, wherein the central processing unit is configured to perform gait event calibration in combination with a sudden-change-of-ground signal of the gait acquisition device and periodic characteristics of the contactless signal transceiving device.
9. The multi-device collaborative rehabilitation assessment system according to claim 8, wherein the central processing unit employs timestamp synchronization and signal filtering techniques to achieve clock alignment of different device data and employs a gait event calibration module to calibrate step size, step frequency and swing angle.
10. An assessment method of a multi-device collaborative rehabilitation assessment system according to any one of claims 1-9, comprising the steps of: Monitoring the movement process: The method comprises the following steps that S1, a non-contact signal transceiver monitors gait, movement posture, heart rate and respiratory rate of a user in real time, and captures movement track and posture change of limbs; s2, the gait acquisition device records plantar pressure distribution and gait dynamics characteristics through a pressure sensor array, and accurately positions heel strike and toe off events; S3, the intelligent bracelet records exercise intensity, type and physiological data, and evaluates exercise intensity and calorie consumption; S4, the central processing unit performs space-time synchronous fusion on the data of the radar, the gait acquisition device and the intelligent bracelet, and improves the measurement accuracy of gait parameters and physiological indexes through cross verification and cooperative calculation; recovery monitoring after exercise: s5, continuously monitoring heart rate variation and heart rate variability of a user by the intelligent bracelet, and evaluating the recovery condition of a cardiovascular system; s6, the gait acquisition device records plantar pressure and gait data and analyzes whether the situation of excessive fatigue or insufficient recovery exists or not; S7, the central processing unit calculates the recovery speed by combining heart rate changes before and after exercise, and generates personalized recovery advice; Night sleep monitoring: S8, monitoring abnormal phenomena such as heart rate, respiratory rate and apnea of a user by a non-contact signal transceiver; s9, the intelligent bracelet monitors sleep-related vital signs such as heart rate variability, night awakening times and the like; s10, a central processing unit analyzes the sleep stage of a user by adopting a sleep stage algorithm and evaluates the sleep quality; and S11, performing targeted rehabilitation assessment according to the detected data.

Description

Multi-equipment cooperative rehabilitation evaluation system and method Technical Field The invention relates to the technical field of health monitoring, in particular to a multi-equipment collaborative rehabilitation evaluation system and method. Background With the rapid development of health monitoring technology, gait analysis and cardiovascular monitoring are increasingly used in rehabilitation medicine and health management. Gait is an important feature of human body movement, and analysis of gait can reflect movement capacity and has important significance for early diagnosis and rehabilitation evaluation of diseases. At the same time, the monitoring of cardiovascular physiological signals provides critical data support for disease risk prediction and health management. However, the prior art still has a plurality of defects in realizing accurate, all-weather and multi-scene comprehensive health monitoring. For example, many existing solutions rely on a single modality sensor for gait analysis, such as a camera or sole pressure sensor, which typically capture only a certain aspect of the gait, but cannot fully reflect the key parameters of stride, swing angle, gait symmetry, etc., resulting in limited accuracy and comprehensiveness of the gait analysis. In addition, camera-based systems are susceptible to ambient light, viewing angle and occlusion, performance is degraded in low light or complex environments, and there is a risk of privacy disclosure, and it is difficult to adapt to diverse scenes in the daily life of users The prior art also has obvious defects in adaptability to monitoring scenes. Video-based gait monitoring schemes generally require specific walkways or motion paths, and cannot meet the needs of varied scenes such as home environments, outdoor sites and the like. Meanwhile, the existing equipment has the problem of disconnection between daytime and night monitoring. For example, gait acquisition devices and cameras can provide more accurate gait data during the day, but often cannot be effectively monitored during the night or without shoes, which limits the continuity and integrity of long-term health monitoring. In addition, the prior art is insufficient in the aspect of fusion analysis of gait data and other physiological indexes, only pays attention to certain dimensions of gait, and neglects comprehensive evaluation of important physiological information such as heart rate, respiratory rate and the like, so that the health condition of a user is difficult to comprehensively reflect. The hysteresis of data analysis and processing is also a big and short plate of the prior art. Many existing health monitoring systems, while capable of collecting relevant data, rely on offline or batch processing, lack a real-time feedback mechanism that prevents users from obtaining health assessments and advice during or immediately after exercise. Meanwhile, the existing system has limited intelligent analysis capability and depends on traditional algorithm processing, and lacks of intelligent analysis methods based on deep learning or artificial intelligence, so that the gait abnormality detection prediction capability is insufficient, and personalized rehabilitation schemes and health management suggestions cannot be provided. Disclosure of Invention The application aims to provide a multi-equipment collaborative rehabilitation evaluation system so as to solve the technical problems. The invention adopts the following scheme: a multi-equipment cooperative rehabilitation evaluation system comprises a non-contact signal receiving and transmitting device, a gait acquisition device, an intelligent bracelet and a central processing unit, wherein, The non-contact signal transceiver is used for monitoring gait, exercise posture, heart rate and respiratory frequency of a user in a non-contact mode; the gait acquisition device is internally provided with a pressure sensor array for measuring plantar pressure distribution and gait dynamics characteristics; the intelligent bracelet is integrated with an accelerometer, a gyroscope and a heart rate sensor and is used for monitoring exercise intensity, type and physiological data respectively; The central processing unit is suitable for receiving the data of the non-contact signal receiving and transmitting device, the gait acquisition device and the intelligent bracelet to generate gait events, and human health data are formed through fusion and analysis of the multi-mode data, and rehabilitation evaluation is carried out according to the human health data. Further, the non-contact signal transceiver is configured to construct a three-dimensional spatial model by capturing motion trajectories and posture changes of the extremities and to provide macroscopic motion parameters for gait analysis. Further, the gait acquisition device is adapted to detect heel strike and toe off events and to perform gait event time stamp calibration in combination with the