Our results demonstrated that the synergistic activities of forearm muscle contractions are generally impaired in trans-radial amputees, and the extent to which the dimensionality of the muscle contraction is reduced is related to several clinical parameters. We discuss our results with respect to the muscle synergy framework and prosthesis control applications below.
The present study is a retro-perspective analysis using an open-source dataset. While the dataset contains a wide range of movements and a relatively large number of control and amputee subjects, there are two main limitations associated with the present study and the results. First, the EMG signals were obtained from surface locations that are not precisely linked to individual functional muscles. This prevents physiologically accurate extraction and direct comparison of muscle synergies, due to spatial uncertainties associated with the electrode locations. Therefore, this study only provides an approximation of the muscle synergies and their dimensionality. Ideally, the EMG signals for synergy extraction should be acquired at precise anatomical sites [18], [42], [43], or using high-density electrode arrays that provide more spatial information [38], [45]. Moreover, EMG recordings from the intact limb should be acquired to enable within-subject comparisons. Second, the movements included in the original dataset are missing in some cases that could potentially improve the estimation of the dimensionality of forearm muscles, a notable one being the extension of all fingers. Also, the dataset includes only a few movements that focus on coordinated motion of the fingers and wrist, which are functionally important for manual dexterity [46]. Future studies that quantify muscle control capabilities in amputees should consider these limitations and adjust the experimental protocol accordingly. Despite the inaccuracies in estimating the exact physiological structure of the muscle synergies, our results obtained from the able-bodied control group is consistent with previous studies, suggesting the validity of the present method. We believe that the present study provides good estimations about the dimensionality of the forearm muscles contractions, which has several important indications to the investigation of non-invasive myoelectric control.
4.2 Dimensionality of hand/wrist muscle contraction in able-bodied individuals.
The muscle synergy framework has emerged in recent years as a technique to understand how motor control is coordinated across combinations of muscles, with applications for clinical assessment and rehabilitation after injuries and diseases that impair the neural pathways of sensorimotor control [47], [48]. It has been argued that multiple muscles can generate covarying activities as a ‘functional unit’, i.e., muscle synergy, which enables the central nervous system to operate in a low-dimensional functional (neural) space instead of a high-dimensional muscle/joint (mechanical) space for common tasks [24], [49]. Such dimensionality reduction can often be quantified using matrix factorization methods such as principle component analysis (PCA), independent component analysis, and NMF on EMG signals [50]. Precise estimation of the dimensionality of hand/wrist muscle activation is challenging because experimental setups do not usually record from all relevant muscles given the many DoFs and complex musculoskeletal structure of the forearm. Weiss and Flanders demonstrated approximately 3–4 synergies were needed to account for > 90% of the variance of EMG recorded from five intrinsic and two extrinsic finger muscles during grasping or spelling tasks [43]. Manickaraj et al., identified 2–3 synergies from five forearm muscles to account for > 90% of the variance during wrist movement tasks [51]. Zariffa et al., showed 5 synergies can be extracted from eight electrodes (> 85% variance explained) across in-hand and forearm muscles during functional grasping tasks [42]. The structure of the synergies varies greatly across these studies due to the differences in electrode configuration and measured movements. Therefore, these investigations only captured a subset of the hand/wrist motor synergies, considering that kinematic analysis indicates 9 synergies were needed to explain > 90% variance measured across 19 finger joints during activities of daily living [52]. Constrained by the experimental setup of the dataset used in the current study, we demonstrated approximately five synergies for each able-bodied individual which can be clustered into six distinct types (Fig. 3). One can observe that these extracted synergy types do not explain most of the finger movements. This was expected since the electrode configuration grossly focused on wrist and extrinsic finger muscles, which usually act as ‘joint stabilizers’ through co-contraction when individual fingers are moving. An important finding is the effect of gender: less synergies were extracted from female subjects than from male subjects. Gender was mostly overlooked in previous studies of upper-limb muscle synergies due to small sample sizes. For the present study, we think that this effect could be best explained by body size differences. It was found that female forearm circumference is about 16% less than that of males [53]. A larger circumference of the forearm enables larger inter-electrode distances, which allows the sensors to capture more independent muscle activities. Future studies are needed to better examine this finding.
4.3 Dimensionality of hand/wrist muscle contraction in transradial amputees and the effects of clinical parameters
The structure and activation of muscle synergies of upper limbs can be altered by impaired sensorimotor neural pathways, such as those found in stroke [36], spinal cord injury [42], dystonia [54], and pain [51]. Most of these previous studies focused on injury/diseases occurring at or above spinal motor neuron level. In contrast, amputation could lead to several distinct insults to the motor system at peripheral sites. First, muscular structure can be significantly altered due to surgical management. Specifically, for transradial amputation, myodesis of deeper forearm muscles and myoplasty of superficial muscles are needed for bone coverage and contraction stability post-surgery [55]. These procedures, as well as retractions and fibrosis after surgery, may alter the conduction of the muscle unit action potentials within the forearm tissue due to changes of the source signal locations and tissue conductivity. Consequently, the pattern of surface EMG signals could be altered in an even-spaced electrode setup as in most myoelectric control applications. Such disturbance to the musculature could be less for individuals who have longer residual arm length [56], [57]. For instance, a distal third forearm amputation could leave the origin and insertion of the pronator teres and supinator intact, and tenodesis can be used for more distal amputations in which tendons are preserved. Another factor that could potentially change volume conduction is the circumference of the forearm, as demonstrated in able-bodied subjects between genders. Although the amputees in this study have similar body size as the male able-bodied controls, surgery and muscle atrophy could cause reduction in the forearm circumference. However, we think this effect does not play relatively small role since only small difference (< 0.5 synergy) was found between males and females. This is also supported by the fact that no difference was found between myoelectric device users and non-users, considering the latter are more likely to develop atrophy due to non-use. In addition to these changes at musculature level, changes in muscle synergies could also be attributed to the missing afferent signals. Although questions remain regarding the contribution of sensory feedback in organizing and activating muscle synergies, it has been demonstrated that deafferented frogs exhibit different synergy structure and activation compared to intact frogs. Moreover, it was demonstrated that deafferentation could induce phantom limb pain and reorganization of cortical somatotopic map [58], [59], which may led to alternation of synergy structures as seen in Table 4. Therefore, it is possible that the lack (or alteration) of sensory feedback from the missing part of the limb could impact how synergies are modulated in amputee subjects (Table 3). Considering these two types of damage, our result that the dimensionality and the structure of forearm muscle contractions change as remaining limb length reduces can be expected.
We found that the dimensionality of forearm muscle contraction does not correlate with the number of years after amputation (Fig. 3B). This could suggest that natural usage (contraction) of the muscles may not be important to maintain muscle synergies as those synergies are already well developed in these patients before trauma induced amputation. In contrast, we did find that the number of synergies increase as a function of age (Fig. 3D). However, it is difficult to speculate why this was the case because age did not strongly predict the number of synergies in able-bodied subjects, and the sample size of amputee subjects is considerably smaller than the able-bodied subject dataset. This correlation could be an artifact caused by other unreported clinical parameters mentioned in the previous paragraph.
Lastly, we would like to compare the present study to the work of Atzori and colleagues [29], in which the same dataset was used to examine muscle control in the form of generating discrete muscle activation patterns. These independent patterns were identified by using classification algorithms on the EMG data for each amputee subject, and the number of discrete patterns was determined as the largest subsets of 40 movements (Exercise B and C) that can achieve > 90% classification accuracy. Therefore, this study quantified individual’s muscle contraction space as discrete movement classes. In contrast, our analysis quantified the muscle contraction space as axes on which the activations can co-vary continuously. Atzori et. al. demonstrated that the number of independent movements can vary between 2 and 11 within the amputee subjects (DB3), which is potentially smaller than that in able-bodied subjects [60]. This number is generally larger than the number of synergies we found in the current study, because each synergy can afford more than one independent movement at distinct activation levels. Furthermore, Atzori et. al. found that the number of independent movements can only be weakly predicted by the remaining limb length, but that they can be strongly predicted by the phantom limb sensation and number of years after amputation. A closer examination of the dataset suggested that six myoelectric prosthesis users had larger averaged number of years after amputation is (7.3 years) and higher averaged degree of phantom sensation (3.3) than five non-myoelectric users (5.6 years and 2.4, respectively) in this dataset (same trend can be observed if Sub 7 is excluded). Therefore, we speculate that the ability to produce discrete movements patterns can be improved by the experience of using prosthesis devices. Active use of myoelectric prosthesis requires muscle contraction on a day-to-day basis, which could lead to motor learning at the cortical level, as well as muscle atrophy prevention. Both of these processes could help the patient to generate contraction patterns more consistently for a given movement and more distinct across movement, i.e., higher signal-to-noise ratio in offline classifiers. Indeed, it has been found that movement classification accuracy in pattern recognition based controllers was higher in myoelectric hand users than non-myoelectric users, and non-myoelectric users could get better if training is given [61]. Similar observation was also reported with functional clinical outcome measures for conventional myoelectric hands users (and non-users) [26]. Moreover, prosthesis use has been shown to maintain phantom sensation vividness better than non-use [62], and prosthesis use can preserve mental rotation ability [63] which is shown to be influenced by phantom sensation [64]. Lastly, neuroimaging studies have shown that use of myoelectric prosthesis could reduce cortical reorganization [65]. Given the above evidences, we think that the ability to produce discrete muscle pattern involves both supraspinal structures which can be facilitated by usage of myoelectric prosthesis. In contrast, our current study addresses the muscle control capability mainly defined by the dimensionality of the overall EMG signal variance, which is less affected by the cortical level use-dependent changes.
4.4 Clinical implications in neuroprothetics
NMF is one of the common methods to define simultaneous and proportional myoelectric interfaces in hand-wrist prosthesis, while other methods (e.g., regression, PCA) are also based on the muscle synergy framework to extract intuitive motor commands [66]. Although these methods are usually not completely unsupervised as implemented in the present study, the number of DoFs that can be controlled by such interfaces is still directly related to the dimensionality of the forearm EMG. Most existing studies that involve transradial amputees use 2-DoF interfaces, in which the first DoF is usually wrist flexion/extension [20], [67], [68]. This accords well with our result that a muscle synergy associated with this DoF is mostly intact in amputees with varying clinical parameters. The mapping of the second DoF in these studies includes: wrist pronation/supination [67], finger flexion/extension [68], and wrist radial/ulnar deviation [20]. These DoFs were all consistently found in the able-bodied subjects, but highly variable in amputees. In fact, some of the amputee synergies does not resemble any of the able-bodied synergies. Therefore, our results indicate that the optimal choice of 2-DoF interface is highly user dependent, and we cannot simply use one-size-fit-all approach in clinical testing. A suboptimal selection of DoFs could force the user to use synergies that have relatively small variances, which may limit the performance or increase the energy expenditure. 3-DoF interfaces have also been tested in able-bodied subjects [18], [38]. However, it may be challenging to define 3-DoF interfaces using surface EMG for amputees with remaining forearm length < 70% due to the reduced muscle contraction space. Furthermore, motor synergies that involve partial hand motions may not be a good option for amputees, although it has been successfully implemented to drive a multi-DoF hand for able-bodied subjects [69]. In summary, clinical parameters play an important role in determining the DoFs for clinical implementations of simultaneous and proportional myoelectric interfaces, and studies with able-bodied subjects may not always be translational for clinical use. It is important to test patient’s muscle control capacity before fitting the terminal device for better customization (i.e., precision medicine). We propose that mechanical designs (e.g., how to map the DoFs) should also be customized to match the available dimensionality of the patient’s muscle control. Moreover, mechanically complimentary prosthetic hands could enhance the capability of simultaneous and proportional interfaces (limited by number of muscle synergies) by providing additional flexibility in day-to-day operations such as grasping [70], [71].