Research On Printed Electronics-based Rehabilitation Training Glove Technology

Sensors with flexible materials or structures can be applied to different surface shapes and structures.They are widely used in healthcare,sports monitoring and smart homes.There has been many research activities in sensor’s high sensitivity and stability as well as signal acquisition platforms,but the scope of application is usually narrow,focusing on signal change testing and neglecting sensing system establishment.This study presents a portable printed electronics-based hand posture capture system that combines low-cost disposable consumables with reusable hardware devices by printing sensor devices on a flexible substrate to form a highly integrated composite circuit.The control circuit is designed to receive sensor signals and use Bluetooth technology for near-field transmission to an Android device for simultaneous display of 3D models of the hand gestures.This system can be applied it to occupational hand trainings for hemiplegic patients.The main contents of this research are as follows:Design disposable sensing gloves based on the human hand to capture the necessary motion characteristics.Use screen printing technology to print sensor devices and circuits on flexible substrates such as nitrile rubber gloves,PET films,and polyester fabrics.Analyze the film thickness and printed circuit conductivity.When applying strains of 5%,10% and 15% at the frequency of 100 Hz,the synchronous change rate of the sensor resistance are about 5%,8%and 11%,respectively.The changes are distinguishable and stable,showing good sensitivity and strain cycling performance.Taking into account practical application indicators such as flexibility and production cost,polyester fiber fabric is chosen as the printing substrate for the sensing gloves.Starting from the hardware requirements of the sensing gloves,design the circuit system and select electronic components.The ESP32-S,which has both traditional Bluetooth and BLE,is used as the control core of the circuit system.Peripherals are designed around the control core and sensor interfaces.After assembling the circuit by surface mounting and testing its conductivity,the sensor data acquisition and Bluetooth transmission programs are debugged.A wristwatch-style wearable structure is designed using modeling software,and the components are assembled for wearing tests of the sensing gloves.Design and develop an Android-based interactive software for the sensing gloves,including functions such as program startup,Bluetooth device searching and connection,data transmission,and synchronized display of the hand’s 3D model.The stable connection between hardware and software is ensured by using traditional and low-power dual-mode Bluetooth.Data transmission integrity is guaranteed through segmented transmission of a single packet,and sensor signal conversion is achieved through interval mapping using zero-based relative scaling.A multi-degree-of-freedom 3D model is built with referencing the human hand,and the corresponding skeletal structure is added.After skinning and weight adjustments are performed,texture mapping is applied to the model surface to achieve better display effects.Finally,design and research are carried out on the hand occupational training system for hemiplegic patients based on the system developed in this study,both hardware and software.Tests are conducted in the stability,real-time response,and cyclic sampling capability of the sensing gloves.Extract data features of common gestures and perform preprocessing operations.Classify and predict using SVM,random forest,and multi-layer neural network algorithms.A hand training system for stroke patients has been designed based on their potential usage needs,using a random forest with an accuracy of approximately 99% as the core model for classification and prediction.Provide diversified and user-friendly training programs,integrate training instructions with synchronized feedback of hand movements into the same display interface to meet the needs of patients for home training and to improve training concentration.Automatically generate data reports to visually reflect the training situation and provide data support for medical staff to understand patients.The hand posture capture system designed in this article is inexpensive and easy to manufacture,and has enormous potential in monitoring the medical rehabilitation of upper limb movement disorders.After further reducing the cost of the sensing gloves and separating them from reusable control circuits,they can also be used in the fields of biomedicine,chemical protection,and other areas to avoid cross-contamination of biological materials.It also provides more diverse options for remote medical practices.