Abd Moaed A, Engeberg Erik D
Ocean and Mechanical Engineering Department, Florida Atlantic University, Boca Raton, Florida, FL, USA.
Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, Florida, FL, USA.
Res Sq. 2023 Mar 16:rs.3.rs-2684789. doi: 10.21203/rs.3.rs-2684789/v1.
People use their hands to perform sophisticated tasks like playing a musical instrument by integrating manifold and diverse sensations of touch with motor control strategies. In contrast, prosthetic hands lack the capacity for multichannel haptic feedback and multitasking functionality remains rudimentary. There is a dearth of research exploring the potential of upper limb absent (ULA) people to integrate multiple channels of haptic feedback into dexterous prosthetic hand control strategies.
In this paper, we designed a novel experimental paradigm for three ULA people and nine additional subjects to investigate their ability to integrate two simultaneously activated channels of context-specific haptic feedback into their dexterous artificial hand control strategies. Artificial neural networks (ANN) were designed for pattern recognition of the array of efferent electromyogram signals that controlled the dexterous artificial hand. ANNs were also used to classify the directions that objects were sliding across two tactile sensor arrays on the index (I) and little (L) fingertips of the robotic hand. The direction of sliding contact at each robotic fingertip was encoded by different stimulation frequencies of wearable vibrotactile actuators for haptic feedback. The subjects were tasked with implementing different control strategies with each finger simultaneously depending upon the perceived directions of sliding contact. This required the 12 subjects to concurrently control individual fingers of the artificial hand by successfully interpreting two channels of simultaneously activated context-specific haptic feedback.
Subjects were able to accomplish this complex feat of multichannel sensorimotor integration with an overall accuracy of 95.53% ± 0.23%. While there was no statistically significant difference in the classification accuracy between ULA people and the other subjects, the ULA people required more time to correctly respond to the simultaneous haptic feedback slip signals, suggesting a higher cognitive load required by the ULA people.
ULA people can integrate multiple channels of simultaneously activated and nuanced haptic feedback with their control of individual fingers of an artificial hand. These findings provide a step toward empowering amputees to multitask with dexterous prosthetic hands, which remains an ongoing challenge.
人们通过将多种不同的触觉与运动控制策略相结合,用手来完成诸如弹奏乐器等复杂任务。相比之下,假肢手缺乏多通道触觉反馈能力,多任务功能仍很初级。目前缺乏研究探索上肢缺失(ULA)人群将多通道触觉反馈整合到灵巧假肢手控制策略中的潜力。
在本文中,我们为3名ULA人群和另外9名受试者设计了一种新颖的实验范式,以研究他们将两个同时激活的特定情境触觉反馈通道整合到灵巧人工手控制策略中的能力。设计人工神经网络(ANN)用于对控制灵巧人工手的传出肌电信号阵列进行模式识别。ANN还用于对物体在机器人手的食指(I)和小指(L)指尖的两个触觉传感器阵列上滑动的方向进行分类。每个机器人指尖的滑动接触方向通过可穿戴振动触觉致动器的不同刺激频率进行编码,以提供触觉反馈。受试者的任务是根据感知到的滑动接触方向,同时用每根手指实施不同的控制策略。这要求12名受试者通过成功解读两个同时激活的特定情境触觉反馈通道,来同时控制人工手的各个手指。
受试者能够以95.53%±0.23%的总体准确率完成这一多通道感觉运动整合的复杂任务。虽然ULA人群和其他受试者之间的分类准确率没有统计学上的显著差异,但ULA人群需要更多时间来正确响应同时出现的触觉反馈滑动信号,这表明ULA人群需要更高的认知负荷。
ULA人群能够在控制人工手的各个手指时,整合多个同时激活且细微的触觉反馈通道。这些发现朝着使截肢者能够用灵巧的假肢手执行多任务迈出了一步,而这仍然是一个持续存在的挑战。