材料科学
腰椎
计算机科学
支持向量机
生物医学工程
蹲下
物理医学与康复
医学
机器学习
人工智能
放射科
作者
Di Liu,Dongli Zhang,Zhuoran Sun,Siyu Zhou,Wei Li,Chengyu Li,Weishi Li,Wei Tang,Zhong Lin Wang
标识
DOI:10.1002/adfm.202113008
摘要
Abstract Lumbar degenerative disease (LDD) refers to the nerve compression syndrome such as neurogenic intermittent claudication and lower limb pain, which disturbs people's daily life, and its incidence increases with age. Traditional diagnosis often employs magnetic response imaging or other imaging examinations. But the radiological data have uncertain clinical correlation and often be overemphasized in clinical decision making. Here, an active‐matrix sensing array (AMSA) is proposed to measure plantar pressure during walking, in order to improve LDD diagnostic processes. An array of piezoelectric sensors with high robustness are assembled. Combined with a support vector machine (SVM) supervised learning algorithm, the system can classify the common human motions of half‐squat, squat, jump, walk and jog with an accuracy up to 99.2%, demonstrating its capability of recognizing personal activities. More importantly, in 62 clinical samples of lumbar degenerative patients, the system can perform an artificial intelligence diagnosis, achieving an accuracy of 100% with an area under receiver operating characteristic curve of 0.998, and also gives out recovery assessments after surgery. Since the personal plantar pressure is also indicative of other diseases, such as diabetes and fasciitis, the system can be extended to other medical aspects, showing a broad impact in biomedical engineering.
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