Physiological Characteristics of Forearm Muscles During Different Movement Patterns of Wrist

Abstract

The abundant muscle tissues of the forearm determine the movements of the wrist, hand and fingers together. However, linking wrist kinematics and forearm muscle activation is still a challenging. There may exist blindness in the rehabilitation therapy of forearm muscles, due to the lack of the physiological characteristics of muscle activation and sequences. An armband with eight channels was used to collect surface electromyographic signals (sEMGs) of a specific section of the forearm under the different wrist movements, palm postures, and external loads, based on the image of magnetic resonance imaging (MRI). The collected cross-sectional muscles covered almost all surface muscles. The muscle activation could be expressed clearly by enveloping the sEMG signals of 8 muscles within a single cycle. The root mean square (RMS) and the average peak value VP were used to evaluate the activation intensities of dominant muscles. The activation sequences and the absolute times of dominant muscles were obtained from the envelopes of their raw sEMGs, and not influenced by the palm postures and external loads. In addition, their RMS and VP under each wrist movement increased approximate linearly with external loads. The corresponding contribution ratios were first calculated to evaluate the role played by each muscle. The well-defined data of forearm muscles could provide standard references for the rehabilitation therapy of forearm muscles.

1. Introduction

Proximal limb segments are responsible for limb transport, while distal limb segments are responsible for object manipulation. Wrist and forearm articulations play an important role in enhancing the usefulness of the hand by allowing it to take a variety of orientations with respect to the elbow, which are critical for daily life. The abundant muscle tissues of the forearm determine the movements of the wrist, hand and fingers together. However, linking wrist kinematics and forearm muscle activation is still a challenging and crucial problem [1, 2] for improving prosthetic control [3, 4], developing realistic biomechanical models [5], or improving wrist rehabilitation by more adapted physiotherapy (functional electrical stimulation (FES) [6, 7], infrared therapy [8], massage therapy [9], etc.). Especially, the full benefits of FES may be limited due to lack of a systematic approach to formulate the muscle patterns of stimulation [10].

There are a large number of forearm muscles, and each muscle is roughly divided into flexor or extensor. However, existing research mainly focuses on the mapping between muscles and hand/finger postures [1, 11, 12]. Artificial intelligence algorithms are often used for the pattern recognition based on the forearm surface electromyographic signals (sEMGs) [13], which are used for motion control of prosthetics with multiple degrees of freedom [14]. Hence, the direct and precise mapping between the muscle activation and wrist movements is even more scarce. The well-defined data of activation sequence of each muscle and its contribution to wrist movements under the different palm postures and external loads are urgently needed to better understand the natural organization of neuromuscular control [15].

Electromyographic signals (EMGs) are commonly used to evaluate or express the muscle activation and physiological effect [16]. Intramuscular electrodes are ideal for deep muscles [2], but have some disadvantages: placement requires thorough knowledge of the musculoskeletal anatomy, and is invasive (needle inserted into muscles) and painful. Although sEMG is non-invasive and easy to apply, the recorded signals are dependent on the accurate placement of electrodes over muscles, notably multichannel data acquisition [17]. As a multichannel electromyographic sensor, the armband has attracted increasing attention from researchers [18–20], as it can fully collect sEMG from any cross-section of the forearm at once [21, 22]. Its collection circuit...