Sharing of hand kinematic synergies across subjects in daily living activities

Comparison of synergy patterns between the right and left hand while performing postures and object grasps

The human hand, with many degrees of freedom, serves as an excellent tool for dexterous manipulation. Previous research has demonstrated that there exists a lower-dimensional subspace that synergistically controls the full hand kinematics. The elements of this subspace, also called synergies, have been viewed as the strategy developed by the CNS in the control of finger movements. Considering that the control of fingers is lateralized to the contralateral hemisphere, how the synergies differ for the control of the dominant and the non-dominant hand has not been widely addressed. In this paper, hand kinematics was recorded using electromagnetic tracking system sensors as participants made various postures and object grasps with their dominant hand and non-dominant hand separately. Synergies that explain 90% of variance in data of both hands were analyzed for similarity at the individual level as well as at the population level. The results showed no differences in synergies between the hands at both these levels. PC scores and cross-reconstruction errors were analyzed to further support the prevalence of similarity between the synergies of the hands. Future work is proposed, and implications of the results to the treatment and diagnosis of neuromotor disorders are discussed.

Decomposition and Reconstruction of Human Palm Movements

OBJECTIVE

The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science.

METHODS

By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset. Then, a method for extracting the eigen-movements to characterize the common motion correlation relationships of palm joints was proposed to explore the palm movement constitution.

RESULTS

This study revealed a palm kinematic characteristic that we named the joint motion grouping coupling characteristic. During natural palm movements, there are several joint groups with a high degree of motor independence, while the movements of joints within each joint group are interdependent. Based on these characteristics, the palm movements can be decomposed into seven eigen-movements. The linear combinations of these eigen-movements can reconstruct more than 90% of palm movement ability. Moreover, combined with the palm musculoskeletal structures, we found that the revealed eigen-movements are associated with joint groups that are defined by muscular functions, which provided a meaningful context for palm movement decomposition.

CONCLUSION

This paper suggests that some invariable characteristics underlie the variable palm motor behaviors and can be used to simplify palm movement generation.

SIGNIFICANCE

This paper provides important insights into palm kinematics, and helps facilitate motor function assessment and the development of better artificial hands.

The Measure of Motion Similarity for Robotics Application

A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions.

Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies

A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.

Reducing Anthropomorphic Hand Degrees of Actuation with Grasp-Function-Dependent and Joint-Element-Sparse Hand Synergies

In this paper, a set of grasp-function-dependent and joint-element-sparse hand synergies was proposed. First, hand synergies were extracted from five basic categories of movements by principal component analysis (PCA). Then, varimax rotation was applied on these synergies, so each sparse synergy only represented a limited number of joints. Next, according to the contribution to these sparse synergies, finger joints were clustered into different joint modules. Finally, integrating the joint modules in different categories of hand movements, the minimum number of actuators and joint synergic modules for anthropomorphic hands were determined. The results showed that using 5 groups of joint modules and 7–9 actuators we can achieve the best performance of grasp function and motion flexibility. Furthermore, through the reasonable design of adaptive and hyperextension functional joint modules, anthropomorphic hands can better meet the requirements of different tasks like power grasping and precision pinching. Comparing with traditional finger-based actuation strategy, the joint coupling scheme achieved better anthropomorphic performance and larger workspace. These above findings will benefit the development of mechanical structure design and control method of anthropomorphic hands.

Functional reorganization of locomotor kinematic synergies reflects the neuropathology in a mouse model of spinal cord injury

Spinal cord injury (SCI) disrupts motor commands to modular structures of the spinal cord, limiting the ability to walk. Evidence suggests that these modules are conserved across species from rodent to human and subserve adaptive walking by controlling coordinated joint movements (kinematic synergies). Since SCI causes uncoordinated joint movements of the lower limbs during walking, there may be a disorder of the modular structures that control them. To gain insights into this complex process, we recorded the kinematics of intact and SCI mice when walking on a treadmill and applied principal component analysis to extract kinematic synergies. Most SCI mice walked stably on the treadmill, but their kinematic synergies were generally different from those of intact mice. We classified the kinematic synergies of SCI mice into three groups based on the similarity of the extracted first three synergy components. We found that these three groups had different degrees of spinal cord damage. This suggests that differences in kinematic synergies reflect underlying SCI neuropathology. These results may help guide the development of different rehabilitation approaches and future physiological experiments to understand the mechanisms of motor control and recovery.

Using Sensorized Gloves and Dimensional Reduction for Hand Function Assessment of Patients with Osteoarthritis

Sensorized gloves allow the measurement of all hand kinematics that are essential for daily functionality. However, they are scarcely used by clinicians, mainly because of the difficulty of analyzing all joint angles simultaneously. This study aims to render this analysis easier in order to enable the applicability of the early detection of hand osteoarthritis (HOA) and the identification of indicators of dysfunction. Dimensional reduction was used to compare kinematics (16 angles) of HOA patients and healthy subjects while performing the tasks of the Sollerman hand function test (SHFT). Five synergies were identified by using principal component (PC) analyses, patients using less fingers arch, higher palm arching, and a more independent thumb abduction. The healthy PCs, explaining 70% of patients’ data variance, were used to transform the set of angles of both samples into five reduced variables (RVs): fingers arch, hand closure, thumb-index pinch, forced thumb opposition, and palmar arching. Significant differences between samples were identified in the ranges of movement of most of the RVs and in the median values of hand closure and thumb opposition. A discriminant function for the detection of HOA, based in RVs, is provided, with a success rate of detection higher than that of the SHFT. The temporal profiles of the RVs in two tasks were also compared, showing their potentiality as dysfunction indicators. Finally, reducing the number of sensors to only one sensor per synergy was explored through a linear regression, resulting in a mean error of 7.0°.

Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements

Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.

A soft, synergy-based robotic glove for grasping assistance

This paper presents a soft, tendon-driven, robotic glove designed to augment grasp capability and provide rehabilitation assistance for postspinal cord injury patients. The basis of the design is an underactuation approach utilizing postural synergies of the hand to support a large variety of grasps with a single actuator. The glove is lightweight, easy to don, and generates sufficient hand closing force to assist with activities of daily living. Device efficiency was examined through a characterization of the power transmission elements, and output force production was observed to be linear in both cylindrical and pinch grasp configurations. We further show that, as a result of the synergy-inspired actuation strategy, the glove only slightly alters the distribution of forces across the fingers, compared to a natural, unassisted grasping pattern. Finally, a preliminary case study was conducted using a participant suffering from an incomplete spinal cord injury (C7). It was found that through the use of the glove, the participant was able to achieve a 50% performance improvement (from four to six blocks) in a standard Box and Block test.

Common kinematic synergies of various human locomotor behaviours

Humans show a variety of locomotor behaviours in daily living, varying in locomotor modes and interaction styles with the external environment. However, how this excellent motor ability is formed, whether there are some invariants underlying various locomotor behaviours and simplifying their generation, and what factors contribute to the invariants remain unclear. Here, we find three common kinematic synergies that form the six joint motions of one lower limb during walking, running, hopping and sitting-down-standing-up (movement variance accounted for greater than 90%), through identifying the coordination characteristics of 36 lower limb motor tasks in diverse environments. This finding supports the notion that humans simplify the generation of various motor behaviours through re-using several basic motor modules, rather than developing entirely new modules for each behaviour. Moreover, a potential link is also found between these synergies and the unique biomechanical characteristics of the human musculoskeletal system (muscular-articular connective architecture and bone shape), and the patterns of inter-joint coordination are consistent with the energy-saving mechanism in locomotion by using biarticular muscles as efficient mechanical energy transducers between joints. Altogether, our work helps understand the formation mechanisms of human locomotion from a holistic viewpoint and evokes inspirations for the development of artificial limbs imitating human motor ability.

Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics

Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%.