## 1 Introduction  
Humans use their hands not only to grip objects but also to communicate and express emotions through various hand motions [1]. Upper limb amputees lose such hand functions, and therefore, many of them feel uncomfortable in their daily life. Upper limb amputees are typically fitted with a prosthetic hand to compensate for their lost hand functions. Unfortunately, although a cosmetic prosthetic hand has a similar shape to a human hand, it cannot perform basic grip functions. Further, a body-powered prosthetic hand is difficult to use in daily life because larger body motions are required to perform a simple grip motion. In an effort to resolve such issues, [PERSON] developed a myoelectric hand prosthesis called the MYO hand in 1987 [2]. The MYO hand can grasp objects easily and safely using the electromyogram (EMG) signals from residual muscles on an amputated upper limb, without requiring any additional equipment. However, it cannot execute the same range of motions as that executed by human hands because it has less than two degrees of freedom (DOFs), owing to the use of just one actuator. Recently, multiple-degrees-of-freedom (multi-DOF) myoelectric hand prostheses with four or more embedded actuators in the finger phalange or palm have been developed [3; 4; 5; 6; 7]. These hands can perform various hand gestures and grip motions in a manner that is similar to the human hand owing to their additional DOFs. However, because it is difficult to control multi-DOF myoelectric hand prostheses with the existing control method [8; 9], many researchers utilize a pattern-recognition-based control method that can perform various hand movements using the EMG signals collected from the residual muscle at the amputation site [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. The techniques employed incorporate various types of EMG pattern feature extractions and classifiers to achieve a classification accuracy of more than 95% for pattern recognition (PR) control of the multi-DOF myoelectric hand prosthesis [22]. However, PR control is difficult to use commercially owing to variabilities such as changes in arm posture, level of muscle contraction, and various amputation site shapes and lengths for upper limb amputees [23; 24; 25]. Moreover, most upper limb amputees have a limited number of EMG signals generated at the amputation site [10; 26], making it difficult to perform various hand gestures and grip motions using the multi-DOF myoelectric hand prosthesis with PR control methods. Consequently, the manufacturers of Bebionic hand [2] and i-LIMB hand [27] use mode switching control with just two EMG sensors to control the multi-DOF hand prosthesis. However, it is very difficult for users to master this control method because there is no association between the mode switching control command and the method of performing various hand motions. Furthermore, even when they can perform these hand motions, the time delay and method are unnatural. To solve these problems, the manufacturers have proposed different interfaces for controlling these hands, such as mobile phone applications and small coin-shaped chips attached to certain objects. However, these interfaces require the user to press a button on the phone application during actual conversations, thus disrupting the conversation flow. Therefore, these interfaces are inconvenient for use in everyday life. To solve the problems outlined above, this paper proposes a multifunctional myoelectric hand prosthesis system that can perform eight hand gestures and grip motions using only two EMG sensors, without requiring the training of additional devices or other control methods for users. The proposed system consists of a hand prosthesis with five fingers, a controller equipped with a six-axis motor driver, and an easy and effective control algorithm that enables upper limb amputees to perform various hand gestures and grip motions using a multi-DOF myoelectric hand prosthesis. Most upper limb amputees have less than two EMG signals emerging from the amputation site.