Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

In recent years, several studies have investigated upper-limb motion in a variety of scenarios including motor control, physiology, rehabilitation and industry. Such applications assess people’s kinematics and muscular performances, focusing on typical movements that simulate daily-life tasks. However, often only a limited interpretation of the EMG patterns is provided. In fact, rarely the assessments separate phasic (movement-related) and tonic (postural) EMG components, as well as the EMG in the acceleration and deceleration phases. With this paper, we provide a comprehensive and detailed characterization of the activity of upper-limb and trunk muscles in healthy people point-to-point upper limb movements. Our analysis includes in-depth muscle activation magnitude assessment, separation of phasic (movement-related) and tonic (postural) EMG activations, directional tuning, distinction between activations in the acceleration and deceleration phases. Results from our study highlight a predominant postural activity with respect to movement related muscular activity. The analysis based on the acceleration phase sheds light on finer motor control strategies, highlighting the role of each muscle in the acceleration and deceleration phase. The results of this study are applicable to several research fields, including physiology, rehabilitation, design of robots and assistive solutions, exoskeletons.

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

In the field of motion analysis, the gold standard devices are marker-based tracking systems. Despite being very accurate, their cost, stringent working environments, and long preparation time make them unsuitable for small clinics as well as for other scenarios such as industrial application. Since human-centered approaches have been promoted even outside clinical environments, the need for easy-to-use solutions to track human motion is topical. In this context, cost-effective devices, such as RGB-Depth (RBG-D) cameras have been proposed, aiming at a user-centered evaluation in rehabilitation or of workers in industry environment. In this paper, we aimed at comparing marker-based systems and RGB-D cameras for tracking human motion. We used a Vicon system (Vicon Motion Systems, Oxford, UK) as a gold standard for the analysis of accuracy and reliability of the Kinect V2 (Microsoft, Redmond, WA, USA) in a variety of gestures in the upper limb workspace—targeting rehabilitation and working applications. The comparison was performed on a group of 15 adult healthy subjects. Each subject had to perform two types of upper-limb movements (point-to-point and exploration) in three workspace sectors (central, right, and left) that might be explored in rehabilitation and industrial working scenarios. The protocol was conceived to test a wide range of the field of view of the RGB-D device. Our results, detailed in the paper, suggest that RGB-D sensors are adequate to track the upper limb for biomechanical assessments, even though relevant limitations can be found in the assessment and reliability of some specific degrees of freedom and gestures with respect to marker-based systems.

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