An All-in-One Array of Pressure Sensors and sEMG Electrodes for Scoliosis Monitoring

被引:0
作者
Fan, Weizhe [1 ]
Wang, Shenglong [1 ,2 ]
Li, Qingyang [1 ]
Ren, Xiarong [1 ]
Zhang, Chengcheng [1 ]
Wang, Hanyue [1 ]
Li, Murong [1 ]
Yang, Weiqing [1 ,3 ]
Deng, Weili [1 ]
机构
[1] Southwest Jiaotong Univ, Sch Mat Sci & Engn, Key Lab Adv Technol Mat, Minist Educ, Chengdu 610031, Sichuan, Peoples R China
[2] Southwest Jiaotong Univ, Sch Chem, Chengdu 610031, Sichuan, Peoples R China
[3] Southwest Jiaotong Univ, Res Inst Frontier Sci, Chengdu 610031, Sichuan, Peoples R China
关键词
brace; cobb angle; pressure sensor; scoliosis monitoring; sEMG electrode; IDIOPATHIC SCOLIOSIS; POLYURETHANE SPONGE; NETWORK;
D O I
10.1002/smll.202404136
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient's recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6 kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100 000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment. This article introduces an all-in-one array of pressure sensors and sEMG electrodes for scoliosis monitoring, offering high sensitivity, a broad detection range, and exceptional stability. Combined with machine learning, it can recognize human motion and predict scoliosis. This study is expected to provide real-time insight into the effectiveness of brace therapy and patient progress. image
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页数:8
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