The loss of an upper limb can have a substantial impact on a person's quality of life since it limits a person's ability to work, interact, and perform daily duties independently. Artificial limbs are used in prosthetics to help people who have lost limbs enhance their function and quality of life. Despite significant breakthroughs in prosthetic technology, rejection rates for complex prosthetic devices remain high[1]-[5]. A quarter to a third of upper-limb amputees abandon their prosthetics due to a lack of comprehension of the technology. The most extensively used method for monitoring muscle activity and regulating the prosthetic arm, surface electromyography (sEMG), has significant drawbacks, including a low signal-to-noise ratio and poor amplitude resolution[6]-[8].Unlike myoelectric control systems, which use electrical muscle activation to calculate end-effector velocity, our strategy employs ultrasound to directly monitor mechanical muscle deformation and then uses the extracted signals to proportionally control end-effector location. This investigation made use of four separate hand motions performed by three physically healthy volunteers. A virtual robotic hand simulation was created using ROS. After witnessing performance comparable to that of a hand with very less training, we concluded that our control method is reliable and natural.

翻译：上肢的缺失会严重限制一个人工作、社交以及独立完成日常事务的能力，从而对生活质量产生重大影响。假肢中采用人工肢体来帮助肢体缺失者改善功能并提升生活质量。尽管假肢技术取得了显著突破，但复杂假肢装置的弃用率仍然很高[1]-[5]。四分之一到三分之一的上肢截肢者因对技术缺乏理解而放弃使用假肢。目前最广泛用于监测肌肉活动并控制假肢手臂的方法——表面肌电图（sEMG），存在显著缺陷，包括信噪比低和幅度分辨率差[6]-[8]。与利用肌肉电活动计算末端执行器速度的肌电控制系统不同，我们的策略采用超声直接监测机械性肌肉形变，并利用提取的信号比例控制末端执行器位置。本研究使用了四名身体健康志愿者执行的四种独立手部动作。借助ROS创建了虚拟机械手的仿真环境。在观察到其性能可媲美经过极少训练的手部表现后，我们得出结论：我们的控制方法既可靠又自然。