# Modification of Hand Muscular Synergies in Stroke Patients After Robot-Aided Rehabilitation
## 1 Introduction  
Motor coordination represents one of the most fascinating aspects of human nature. The central nervous system (CNS) is able to control a very high number of degrees of freedom to perform complex movements of both upper and lower limbs [1]. However, the strategies that the CNS uses to perform complex tasks are not yet completely clear and are still being studied [2]. Many studies have shown the existence of motor primitives, which would allow the CNS to manage the complex architecture of the human body, guaranteeing complex movements starting from simple movements [3]. Muscular synergy, from the Greek language, means \"working together\" and was introduced for the first time in a work of [PERSON] as _\"...solution to the problem of selecting one movement among the infinite possibilities of motor solutions to perform a specific task...\"_[4, 5]. It has been suggested that complex movements are constructed through smaller blocks (i.e., muscular synergies) able to involve different muscle groups, in order to overcome the difficulties related to the coordination of a high number of degrees of freedom [1, 3, 6]. The upper limb and the hand are very complex districts of our body, able to perform very precise movements, such as grasping and manipulating of small objects [2]. A detailed understanding of the mechanisms governing such complex movements would have enormous implications in the rehabilitation and prosthetic fields. Muscle synergies are extracted from electromyographic (EMG) signals acquired from muscles involved in the movement. Several algorithms can be adopted to extract muscle synergies from EMG signals, such as non-negative matrix factorization (NNMF) [7], principal component analysis (PCA), factory analysis (FA), and inverse Gaussian [8; 9; 10]. The goal of such algorithms is to reduce the number of variables describing the EMG dataset, limiting the loss of information as much as possible [10]. The first studies concerning muscle synergies aimed at demonstrating that motor control could be described by a set of muscle synergies. Several studies have been conducted to select the number of synergies to be extracted and to evaluate the stability of synergies. The results already presented in the state-of-the-art have been very promising and have pushed researchers to investigate the role of muscular synergies in different fields of application ranging from sport to clinic and robotics [9]. Unfortunately, there is not a defined protocol on how many and which muscles should be selected to analyze a certain movement or a certain synergy. This choice is left to the experience of the researcher who selects the muscles on the basis of the task to be performed [3]. The monitoring of muscle patterns would allow having a quantitative picture of patients suffering from neurological disorders, such as stroke and musculoskeletal pathologies, in general for both upper and lower limbs [11; 12; 13; 14]. An advantage of this analysis is the unobtrusiveness of the adopted sensors and the ease of execution, since the analysis and extraction of muscle synergies can be performed offline also with superficial EMG sensors. Several studies have been conducted on the alteration of the muscular synergies in subjects affected by stroke. In particular, the anomalies of the synergies of the upper limb in subjects with strong and moderate impairment were analyzed. The results showed a similarity between the synergies of the healthy and the affected limb [15]. From the literature analysis, it is evident that the presence of a particularly strong coupling of elbow flexion and shoulder movements in stroke patients affects reaching movements [15; 16]. The deficit in motor performance of patients affected by stroke is due to alterations in the activation of muscular patterns. Furthermore, patients with significant neurological damage often have alterations in motor performance, evident from the analysis of muscle synergies, as observed in [17; 18; 19].