Esposito Daniele, Andreozzi Emilio, Gargiulo Gaetano D, Fratini Antonio, D'Addio Giovanni, Naik Ganesh R, Bifulco Paolo
Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.
Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy.
Front Neurorobot. 2020 Jan 17;13:114. doi: 10.3389/fnbot.2019.00114. eCollection 2019.
Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.
人机接口（HMIs）被广泛应用于各种领域，从残疾辅助设备到远程操作和游戏控制器。在本研究中，提出了一种用于手势识别的新型压阻式传感器阵列臂带。该臂带仅包含三个针对特定前臂肌肉的传感器，旨在区分八种手部动作。每个传感器由一个带有专用机械耦合器的力敏电阻（FSR）制成，旨在感知收缩过程中的肌肉肿胀。该臂带设计得便于任何用户佩戴且可调节，并在10名志愿者身上进行了测试。通过不同的机器学习算法对手势进行分类，并应用10折交叉验证和留一法交叉验证来评估分类性能。线性支持向量机在所有参与者中提供了96%的平均准确率。最终，该分类器在Arduino平台上实现，并实现了对电子游戏的实时成功控制。低功耗以及高精度表明该设备在常用于神经运动康复的运动游戏中具有潜力。传感器数量的减少使得这种人机接口也适用于假手控制。
