## 1 Introduction  
The hand is one of the most important parts of the human body. It is responsible for almost all of the intellectual activities of our daily living [1]. The dexterity of the hand not only enables us to perform delicate tasks but to also regard that object as a part of our body and manipulate it to execute new tasks that are not possible with the hand alone. Also, the fingertip is equipped with fine sensors that allow the sensing of pressure, temperature, pain etc. For these reasons, amputation of the hand generates both psychological trauma and functional impairment because the person becomes unable to perform most daily tasks and baffled by the change in their appearance [2]. According to the 2006 survey on persons with physical disability conducted by the ministry of health, labour and welfare of Japan, there are approximately 80 thousand upper limb amputees in Japan [3]. Prosthetic arm/handwere developed to help these people by restoring as much as possible the functions of a biological hand and natural appearance. Myoelectric prosthetic hand/arm is a type of externally powered prostheses that is controlled by electrical signals generated during voluntary muscle contraction. The measuring of muscle activity via electric potential is referred to as electromyography (EMG). Since EMG signals can be recorded by placing surface electrodes on top of the skin, it is possible to use this electrical potential to provide a signal for control of prosthetic limbs that is relatively intuitive and non-invasive. Over recent decades, extensive research on myoelectric prosthetic hand/arms have been conducted by many research groups across the globe, improving the overall functionality and reliability of myoelectric hands rapidly each year. However, despite these technological advances, the capability to recognize and produce extensive movements of many existing myoelectric hands is still primitive. The limited capability to execute many movements or to provide the user with the control of multiple degrees of freedom (DOF) consequently limits the number of task the user can perform, and is one of the cause that leads to rejection or abandonment of a prosthesis [4; 5]. Recently, due to huge progress in the mechanical development of prosthetic hands, several complex, articulated (multi degree of freedom) myoelectric hands have become available on the market. The Bebionic hand [6] and the iLimb hand [7] are examples of articulated prosthetic hands. Other multi-DOF prosthetic hands like the Vanderbilt hand [8], the UNB hand [9], the Yale hand [10], Smart hand [11], the DLR/HIT hand [12], the Keio hand [13], and the 6 DOF hand [14] are also being developed as research devices. With the emerging of complex multi-DOF prosthetic hands, development of a control system that can distinguish and produce large number of movements are needed to allow the execution of more complex daily tasks. This study presents a signal processing technique to classify 17 forearm motions (with a rest state included) using 6 EMG sensors. The aim is to explore and determine the extent of hand movement recognition capabilities for myoelectric prosthetic hands with the currently available hardware. The novelty of this work is providing the basic methodology to classify not only wrist movements (grasping, opening, flexing, etc.) but also individual flexion and extension control of the five fingers. It will extend the capabilities of recognizing multiple motions beyond what is currently feasible (3-10 motions) [15; 16; 17; 18; 19]. The experiments will be conducted on fully-limbed participants as we believe that this defines the upper bound. Furthermore, this work will also show and discuss about how tailoring the number of hand motions for a specific task can help increase the performance and usability of the prosthetic hand system. This paper is organized as follows. Section 2 gives a literature review of EMG based pattern recognition and highlights the features of this study.