Body area flexible sensing detection system with non-intervention monitoring and method thereof

Abstract

The invention relates to a body area flexible sensing detection system with non-intervention monitoring and a method thereof. The body area flexible sensing detection system comprises a flexible sensor, an electronic fabric worn on a user, a wearable reader and a cloud platform, the flexible sensor is integrated on the electronic fabric and is used for detecting motion data of a user and sending the data to the wearable reader; the wearable reader is used for supplying energy to the electronic fabric through NFC and sending motion data detected by the flexible sensor to the cloud platform; and the cloud platform is used for analyzing the motion data to obtain an analysis result. By highly integrating NFC power supply with a series of advanced technologies such as a Bluetooth communication module and a flexible sensor, and fusing a machine learning technology, non-intervention and high-precision monitoring of cerebral palsy conditions is successfully realized, and an analysis result can be continuously and quickly output in real time according to the posture condition of a user; and brand new technical support is provided for rehabilitation treatment of children with cerebral palsy.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a body domain flexible sensing detection system and method for non-intervention monitoring. Background Art

[0002] Early diagnosis of cerebral palsy requires detection of movement posture. Posture monitoring of children with cerebral palsy is mainly achieved through gait monitoring devices such as insoles and floor mats, wearable acceleration monitoring devices such as watches and chest straps, 3D capture devices such as foot mirrors and cameras, and auxiliary walking devices such as wheelchairs and exoskeletons. Among them, comfortable wearing, instant feedback and monitoring without intervention are the three optimization directions of the current monitoring system.

[0003] The 4DBODY system based on structured light illumination method monitors the movement of patients with cerebral palsy. This technology scans the movement of patients with cerebral palsy through a 4DBODY scanner with a recording frequency of 120 Hz, a spatial resolution of 1 mm, and an error of 0.5 mm, and outputs a series of point clouds. These point clouds can represent the body surface in motion and accurately reconstruct the body movement of patients with various types of motor dysfunction for measurement.

[0004] The wearable activity monitors AMP 331 and Dynaport Minimod are a combination of inertial sensors worn in a sleeve attached to the right calf above the ankle, and Minimod consists of three orthogonally mounted accelerometers worn on the lower back. These lightweight and compact devices allow researchers to measure walking distance and number of steps of children with cerebral palsy to diagnose the condition.

[0005] R.Fadhlillah et al. designed a real-time monitoring system for children with cerebral palsy using IoT and wearable devices, measuring various parameters, such as heart rate and muscle contraction, and transmitting them from the gateway to the Google Firebase cloud storage platform through a microcontroller. The system uses the ESP8266 wireless module to transmit data between the microcontroller and Android via WIFI, and forwards the data to the server on the Android phone. The heart rate information and muscle contraction of children with cerebral palsy can be accessed instantly through the Android phone, achieving instant feedback for cerebral palsy data monitoring.

[0006] The current problems of various systems can be summarized into the following four aspects: First, the 4DBODY system uses fixed power supplies and equipment, which will limit children's activity scenes and affect their feelings and movements. The movement postures of children in restricted scenes may be different from those in daily life, which will make the diagnosis of children's illness inconsistent with their daily performance. Second, although wearable activity monitoring devices can enter daily environments, they cannot simultaneously realize a distributed sensor system in the body, and cannot collect posture data of children with cerebral palsy more comprehensively to ensure the reliability of statistical data. Third, the monitoring system implemented by R. Fadhllah et al. still stays at collecting the required data. Limited by the clinical environment, it requires subsequent data processing or diagnosis based on doctor's experience, and cannot provide real-time and continuous feedback based on the children's posture status. Fourth, most monitoring systems use hard sensors worn outside the body of children with cerebral palsy. The cables and restraints may affect the body movements of children with cerebral palsy, thereby interfering with the natural activities of children with cerebral palsy, and it is impossible to obtain real monitoring data. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide a body domain flexible sensing detection system and method for non-intervention monitoring, which can comprehensively collect the user's real posture data, ensure the reliability of statistical data, and can quickly feedback the results according to the user's posture status in real time and continuously.

[0008] In order to achieve the above object, the present invention adopts the following technical solution: A body domain flexible sensing detection system for non-intervention monitoring, comprising a flexible sensor, an electronic fabric for being worn on a user, a wearable reader and a cloud platform; Flexible sensors integrated into electronic fabrics to detect user motion data and send the data to a wearable reader; The wearable reader is used to power the e-textile via NFC and send the motion data detected by the flexible sensor to the cloud platform; The cloud platform is used to analyze the motion data to obtain analysis results.

[0009] Furthermore, the electronic fabric and flexible sensors communicate wirelessly via Bluetooth to build an intervention-free body-area sensing system.

[0010] Furthermore, the flexible sensor includes a muscle tension sensor, an acceleration sensor and a strain sensor.

[0011] Furthermore, the measurement strain range of the strain sensor is 0 to 300%, corresponding to the joint angle of 0° to 110°; the sampling frequency of the acceleration sensor is 100 Hz, the sampling accuracy is 0.15 m / s2, and the maximum measurable acceleration of the acceleration sensor is 19.6 m / s.

[0012] A method for body-area flexible sensing detection without intervention monitoring comprises the following steps: Wearing the electronic fabric on the user, using the flexible sensor integrated in the electronic fabric to detect the user's motion data and send the data to the wearable reader; The wearable reader powers the electronic fabric and sends the motion data detected by the flexible sensor to the cloud platform; The cloud platform analyzes the motion data to obtain analysis results.

[0013] Furthermore, a plurality of energy supply nodes are arranged on the electronic fabric, and the energy supply nodes utilize electromagnetic response function to perform NFC energy transmission with the wearable reader, and the energy supply nodes utilize electronic fabric to transmit energy, thereby realizing efficient wireless energy transmission within the body range.

[0014] Furthermore, the wearable reader is wirelessly connected to the cloud platform via WIFI.

[0015] Furthermore, the cloud platform uses a machine learning algorithm to train a big data model based on the motion data detected by the flexible sensor, analyzes the data obtained by the flexible sensor using the big data model, and outputs real-time analysis results.

[0016] In general, the present invention has the following advantages: The present invention integrates flexible sensors and related working circuits into electronic fabrics with near-field relay functions, realizes the power supply of the electronic fabrics by the wearable reader through NFC, and uses Bluetooth as wireless communication to build a non-interventional body sensing system, which can comprehensively collect the user's real posture data and ensure the reliability of statistical data. The data obtained by the flexible sensor is transmitted to the cloud in real time for building a health database, and is further analyzed using a machine learning algorithm, which can quickly output the analysis results in real time and continuously according to the user's posture status. The present invention successfully realizes non-interventional and high-precision monitoring of cerebral palsy by highly integrating a series of advanced technologies such as NFC power supply and Bluetooth communication modules, flexible sensors, and integrating machine learning technology, providing new technical support for the rehabilitation treatment of children with cerebral palsy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the work flow chart of the present invention.

[0018] Figure 2 It is the NFC antenna and rectification and filtering circuit.

[0019] Figure 3 For the voltage stabilizing circuit.

[0020] Figure 4 This is the circuit diagram of the first core board connector.

[0021] Figure 5 This is the circuit diagram of the second core board connector. DETAILED DESCRIPTION

[0022] The present invention will be described in further detail below.

[0023] like Figure 1 As shown, a body domain flexible sensing detection system for non-intervention monitoring includes a flexible sensor, an electronic fabric for being worn on a user, a wearable reader and a cloud platform; Flexible sensors integrated into electronic fabrics to detect user motion data and send the data to a wearable reader; The wearable reader is used to power the e-textile via NFC and send the motion data detected by the flexible sensor to the cloud platform; The cloud platform is used to analyze the motion data to obtain analysis results.

[0024] The electronic fabric system of the present invention is as follows: the prepared liquid metal composite fiber is sewn into a specific pattern on the clothing, the electromagnetic response functional pattern is used to perform NFC energy transmission at the energy supply node, and the electronic fabric is used to transmit energy between the energy supply nodes, thereby realizing efficient wireless energy transmission within the body range.

[0025] Data communication: Use low-power Bluetooth for continuous wireless communication between sensors and repeaters, and transmit data via WIFI to the cloud platform for storage for doctors and artificial intelligence analysis.

[0026] Data collection: Accelerometers, strain sensors, and muscle tension sensors are used for data collection. The strain sensor can measure strains ranging from 0 to 300%, corresponding to joint angles of 0° to 110°. The sampling frequency of the accelerometer is 100Hz, the sampling accuracy is 0.15m / s2, and the maximum measurable acceleration of the accelerometer is 19.6m / s.

[0027] For related circuits, please refer to Figure 2-Figure 5 .

[0028] Data processing: Using machine learning algorithms such as random forests, the data of cerebral palsy patients are trained into a big data model. The acquired data is analyzed using the model to provide instant analysis results, which can assist medical staff in quickly understanding the condition of the patient.

[0029] In the prior art, taking the 4DBODY system as an example, although the system can accurately measure the patient's motion data, it severely limits the range of children's activities because of its fixed design of wired active transmission. This design not only affects the accuracy of the monitoring data, but also cannot truly reflect the movement status of children in daily life. Looking at devices such as AMP 331 and DynaportMinimod, although they have indeed improved in portability, making it more convenient for children to wear and use. However, the singleness of the monitoring data type has become their fatal weakness. Neither AMP331 nor DynaportMinimod can comprehensively evaluate the user's motion status, physiological indicators, etc. The real-time monitoring system constructed by R.Fadhlillah et al. has solved the problem of instant data acquisition to a certain extent, but it cannot immediately feedback diagnostic information. This means that doctors and family members cannot understand the changes in children's condition in the first place, thus missing the best time for intervention. In addition, the application of the system is also limited to the clinical environment and cannot meet the monitoring needs of children in multiple scenarios such as daily life and school. What is more serious is that most existing wearable devices use hard sensors, which not only affects the wearing comfort of the device, but also restricts the activities of children with cerebral palsy, thereby interfering with the monitoring results. At the same time, the effective combination of sensors and body-area sensor networks is still blank, and problems such as insufficient power supply and data transmission loss occur frequently, which seriously affects the reliability and practicality of the equipment.

[0030] In response to the above problems, the present invention has shown significant advantages in technical means. The present invention uses electronic fabrics and flexible sensors as the core components of monitoring. This innovative design not only greatly improves the wearing comfort and non-intervention of the device, but also ensures the accuracy and reliability of the monitoring data. The softness and breathability of the electronic fabrics allow children to wear them for a long time without feeling uncomfortable, while the sensitivity and stability of the flexible sensors ensure the accuracy and reliability of the monitoring data.

[0031] Specifically, the present invention uses NFC magnetic field coupling power supply and Bluetooth wireless transmission technology, collects magnetic field energy from electronic fabrics through magnetic field coupling technology, and realizes wireless passive communication and power supply. This design completely gets rid of the constraints of cables, allowing children to be monitored in a completely natural state, thereby ensuring the authenticity, reliability and stability of the data. In addition, the present invention also constructs a body-distributed sensor monitoring network by integrating a variety of flexible sensors such as accelerometers, strain sensors and muscle tension sensors. This network can comprehensively and accurately monitor children's movement status, physiological indicators, etc., providing doctors and rehabilitation therapists with a more comprehensive and accurate evaluation basis.

[0032] A body domain flexible sensing detection method without intervention monitoring, using a body domain flexible sensing detection system without intervention monitoring, comprising the following steps: Wearing the electronic fabric on the user, using the flexible sensor integrated in the electronic fabric to detect the user's motion data and send the data to the wearable reader; The wearable reader powers the electronic fabric and sends the motion data detected by the flexible sensor to the cloud platform; The cloud platform analyzes the motion data to obtain analysis results.

[0033] Specific testing process: After the patient puts on the testing equipment, he / she operates the wearable reader (such as a mobile phone) to wirelessly power the testing equipment via NFC and establish a Bluetooth connection to receive monitoring data; then the acceleration sensor, joint angle sensor and muscle tension sensor integrated in the electronic fabric will begin to collect relevant data of the patient, and the collected data will be sent to the mobile phone software via Bluetooth for reading. After receiving the relevant data, the mobile phone software will upload it to the cloud platform for storage; after the test is completed, the big data model is used to analyze the relevant data stored in the cloud platform and obtain the analysis results.

[0034] The present invention has the following advantages: 1. Comfortable fit of wearable devices. Due to the limitations of materials and design, traditional sensors often feel uncomfortable when they fit the human body, and may even cause inaccurate data collection due to loose fit. However, the flexible sensor used in the present invention can fit tightly on the surface of the human body due to its unique softness and bendability, thereby more realistically reflecting the relevant data indicators of the human body.

[0035] 2. The present invention integrates flexible sensors with low-power Bluetooth and NFC modules to form wireless passive sensor nodes. Traditional sensor nodes are often bulky and restrict movement because they require external power supplies or data cables. The wireless passive sensor nodes of the present invention achieve energy transmission through the NFC module without the need for an external power supply; at the same time, the application of low-power Bluetooth also greatly reduces the energy consumption of the node, thereby extending the continuous working time. This miniaturized and lightweight design not only improves the portability of the present invention, but also allows it to be separated from the clinical environment and continuously monitor the daily activities of children with cerebral palsy.

[0036] 3. The present invention uses near-field relay functional fabric as a carrier, which enables the present invention to reduce restrictions on the range of children's activities and reduce human intervention. Traditional monitoring equipment often requires children to be within a specific range to collect data. The near-field relay functional fabric of the present invention can integrate the monitoring and collection functions into children's clothing, thereby achieving unlimited and interference-free data collection. This design not only improves the accuracy and continuity of data collection, but also allows children to receive comprehensive health monitoring and care while enjoying free activities.

[0037] Fourth, the present invention uses machine learning technology to process multimodal data. The application of this technical means enables the present invention to provide more comprehensive reference opinions in real time. Traditional monitoring equipment can often only collect single or limited data indicators, which is difficult to fully reflect the health status of children, and can only obtain real-time data, requiring medical staff to judge or post-process the data. The present invention integrates multiple sensors to collect multimodal data, and uses machine learning technology to deeply mine and analyze these data, so as to provide instant feedback of comprehensive analysis results for doctors and family members to refer to at any time.

[0038] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A body-area flexible sensing detection system for non-intervention monitoring, characterized in that: Includes flexible sensors, electronic fabrics for wear on the user, wearable readers and cloud platforms; Flexible sensors integrated into electronic fabrics to detect user motion data and send the data to a wearable reader; The wearable reader is used to power the e-textile via NFC and send the motion data detected by the flexible sensor to the cloud platform; The cloud platform is used to analyze the motion data to obtain analysis results.

2. The body-area flexible sensing detection system for non-intervention monitoring according to claim 1, characterized in that: Electronic fabrics and flexible sensors communicate wirelessly via Bluetooth to build an intervention-free body-area sensing system.

3. The body-area flexible sensing detection system for non-intervention monitoring according to claim 1, characterized in that: Flexible sensors include muscle tension sensors, acceleration sensors and strain sensors.

4. The body-area flexible sensing detection system for non-intervention monitoring according to claim 3, characterized in that: The measurement strain range of the strain sensor is 0~300%, corresponding to the joint angle of 0°~110°; the sampling frequency of the acceleration sensor is 100Hz, the sampling accuracy is 0.15m / s2, and the maximum measurable acceleration of the acceleration sensor is 19.6m / s.

5. A body area flexible sensing detection method without intervention monitoring, characterized in that: A body domain flexible sensing detection system for non-intervention monitoring according to any one of claims 1 to 4 is used, comprising the following steps: Wearing the electronic fabric on the user, using the flexible sensor integrated in the electronic fabric to detect the user's motion data and send the data to the wearable reader; The wearable reader powers the electronic fabric and sends the motion data detected by the flexible sensor to the cloud platform; The cloud platform analyzes the motion data to obtain analysis results.

6. The method for body-area flexible sensing detection without intervention monitoring according to claim 5, characterized in that: There are multiple energy supply nodes on the electronic fabric. The energy supply nodes use electromagnetic response function to transmit NFC energy with wearable readers. The energy supply nodes use electronic fabrics to transmit energy, thereby realizing efficient wireless energy transmission within the body range.

7. The method for body-area flexible sensing detection without intervention monitoring according to claim 5, characterized in that: The wearable reader is wirelessly connected to the cloud platform via WIFI.

8. The method for body-area flexible sensing detection without intervention monitoring according to claim 5, characterized in that: The cloud platform uses machine learning algorithms to train a big data model based on the motion data detected by the flexible sensors, analyzes the data obtained by the flexible sensors using the big data model, and outputs real-time analysis results.

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