Explication of Extrinsic Forearm Muscles On the Classification of Thumb Position Using High-Density Surface Electromyogram

M. M. Suhaimi, A. S. Ghazali, A. Jazlan, S. N. Sidek
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Abstract

Muscles for hand functions and movements play a major role in basic daily activities such aswriting and lifting objects. The main digit of the finger in differentiating the hand gesture is the thumb and its main muscles are intrinsic muscles. However, for transradial amputees, despite the loss of access to the intrinsic muscles, any information from the extrinsic muscles would be paramount and non-negotiablein creating a perfect hand prosthesis. As such, the research is dedicated to study the relationship between extrinsic muscles located athuman’sforearm to characterize the actual thumb attitudes.A 64-channel HD-sEMGrecording device together with a thumb force measuring platform wasutilizedtocollect the required signals from 17 participants at several thumb angle positions namely zero-degrees, thirty-degree, sixty-degrees, and ninety-degree. For each position, the participants were required to place their thumbs on top of a load cell at relax (no force at all) and contact (30% of their individual Maximum VoluntaryContraction or known as MVC) conditions repetitively by following a designated trajectory. Feature extraction was performed by calculating the Root Mean Square (RMS) values of the HD-sEMG data collected from each channel. Six different classifiers have been used to classify the relationship between the forearm HD-sEMG and the corresponding thumb positions.As a result, LazyIBK obtained the highest correctly classified instances with 81.05%. The finding is significant in developing a dedicated control framework for a prosthetic hand for tansradial amputees that can operate as closely as normal
前臂外源性肌肉在拇指体位分类中的高密度面肌电图解释
手部肌肉的功能和运动在基本的日常活动中起着重要作用,比如写字和举起物体。鉴别手势的主要手指是拇指,其主要肌肉是内在肌肉。然而,对于经桡骨截肢者来说,尽管失去了接触内在肌肉的机会,但来自外在肌肉的任何信息对于创造一个完美的假肢都是至关重要的。因此,该研究致力于研究位于人类前臂的外部肌肉之间的关系,以表征实际的拇指态度。使用64通道hd - semg记录设备和拇指力测量平台收集17名参与者在几个拇指角度位置(0度,30度,60度和90度)所需的信号。对于每个位置,参与者都被要求在放松(完全没有力)和接触(个人最大自愿收缩或MVC的30%)条件下,按照指定的轨迹重复地将他们的拇指放在称重传感器的顶部。通过计算从每个通道收集的HD-sEMG数据的均方根(RMS)值来进行特征提取。六种不同的分类器被用来分类前臂HD-sEMG和相应拇指位置之间的关系。结果,LazyIBK获得的正确分类实例最高,为81.05%。这一发现对于开发用于经桡骨截肢者的假肢专用控制框架具有重要意义,该框架可以像正常操作一样紧密
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