## 1 Introduction  
Hand grasp execution comprises two primary stages: reaching for the object and actual grasping. The force required to close the hand around an object is influenced by various factors, including grasp stability (the ability to withstand external forces) and grasp security (resistance to slipping), both of which depend on the grasp configuration [1, 2], among other factors. Hand grasping capability relies on the coordination of 32 muscles in the forearm and hand. However, the specific role of these muscles in activities of daily living (ADL) remains unclear. Therefore, examining forearm muscle activity during common grasps used in ADL could provide valuable insights for assessing hand function and rehabilitation. Moreover, such analysis would provide precise information about an individual's movement intentions, which could be beneficial for applications such as telerobotic systems, teleoperation, and prosthetics. Muscular activity is commonly assessed using electromyography (EMG), which records the electrical activity of muscles. Surface EMG (sEMG) is widely used in controlling prosthetic limbs [3, 4, 5, 6], exoskeletons [7, 8], rehabilitation devices [9], and telerobotic systems [10, 11, 12, 13, 14, 15]. However, sEMG electrodes are placed on the skin surface above the muscle, making the recorded sEMG signal highly dependent on electrode placement and susceptible to interference from adjacent muscles [16]. This issue is particularly significant in the forearm, where 20 muscles overlap [17], making it nearly impossible to isolate the sEMG signal from individual muscles. In a previous study [18], we identified forearm areas with similar muscle activation patterns during a set of standardized ADL, offering a potential method for characterizing muscle activity. Few studies have examined the role of forearm muscles by investigating their muscle activity [19]. A recent study [19] explored forearm muscle activity during handgrip contractions while simultaneously applying various levels of wrist forces. Their findings corroborated existing literature [20], indicating that wrist extensors functioned as joint stabilizers and that wrist flexors exhibited task-dependent behavior. These results underscore the significance of assessing forearm muscle recruitment across different tasks or grasps. However, the precise role of forearm muscles in hand and wrist tasks such as grasping remains not fully understood [19]. Furthermore, disorders affecting the upper extremities can significantly impair the performance of various muscles, thereby limiting an individual's capacity to carry out basic ADL. Fortunately, there are several approaches aimed at restoring upper extremity functionality. Some robotic devices currently used in clinical practice use sEMG as an input signal, providing insights into the individual's intention to execute specific movements [21]. Therefore, improving our understanding of the role of forearm muscles during grasping could potentially improve existing rehabilitation devices. Additionally, some prosthetic hands worn by amputees use sEMG signals from residual muscles post-amputation [22]. More accurate information about an individual's movement intention can enhance the usability of such prostheses [23]. Teleoperation involves the remote control of a robot/machine by a human operator [24]. One approach is to monitor and replicate the motion and/or forces performed by the operator in the local site. sEMG is commonly employed for this purpose in the control interface for telerobots [25; 26]. Therefore, enhancing our understanding of the role of forearm muscles during grasping could contribute to the development of more intuitively controlled hand prostheses and enable more accurate estimations of the operator's intended motions, particularly in the context of rehabilitation, telerobotic systems, and teleoperation.