Next Article in Journal
Biomechanical Comparisons between One- and Two-Compartment Devices for Reconstructing Vertebrae by Kyphoplasty
Previous Article in Journal
An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning
Previous Article in Special Issue
The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Bioengineering
Volume 11
Issue 8
10.3390/bioengineering11080793
bioengineering-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective

by
Daniele Borzelli
1,2,*,†,
Cristiano De Marchis
3,†,
Angelica Quercia
1,
Paolo De Pasquale
4,
Antonino Casile
1,
Angelo Quartarone
4,
Rocco Salvatore Calabrò
4 and
Andrea d’Avella
2,5
1
Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98125 Messina, Italy
2
Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy
3
Engineering Department, University of Messina, Messina 98166, Italy
4
IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy
5
Department of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Bioengineering 2024, 11(8), 793; https://doi.org/10.3390/bioengineering11080793
Submission received: 25 June 2024 / Revised: 19 July 2024 / Accepted: 29 July 2024 / Published: 5 August 2024
(This article belongs to the Special Issue Bioengineering of the Motor System)
Browse Figures
Versions Notes

Abstract

:
According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients’ locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

1. Introduction

Gait is a complex task that requires the coordination of multiple muscles, driven by different cortical and spinal structures [1,2,3,4]. Neurological diseases can lead to impairments in the neuromuscular control of gait, with an increased risk of accidental falls [5] and the consequent reduced independence in performing activities of daily living, due to alterations in coordination strategies and in spatiotemporal gait parameters [6]. In this scenario, gait rehabilitation plays a crucial role, and recovering a functional gait is a fundamental step for independent living. Gait rehabilitation may require the action of physiotherapists alone, or it could additionally involve the use of rehabilitation devices. Gait assistive equipment comes in a variety of forms that can be roughly classified into two major categories that can be identified as follows [7,8]: alternative devices, which are used by patients with no movements and do not involve exercises for injured extremities, and augmentative devices, employed by people with limited mobility to generate movements or workouts with a rehabilitative purpose. Augmentative gait devices can be used on a treadmill, on foot-plates, overground through the use of mobile robotic bases [9], or they can be stationary [10]. The quantitative assessment of gait is of great importance to characterize the stage of a neurological pathology [11,12,13,14], to validate the beneficial effects of a therapy [15,16,17,18], or for the early detection of conditions such as risk of fall [19], dementia [20], and Parkinson’s disease [11]. The quantification of the motion of body segments in two or three dimensions is frequently exploited to estimate the spatiotemporal and kinematic parameters of gait [21]. This is usually carried out through marker-based [22] or marker-less [23,24] motion capture systems, even though the use of Inertial Measurement Units (IMUs) have started to gain popularity [25,26,27,28]. This kind of analysis is often complemented by the measurement of contact forces during the stance phase of gait to quantify impact forces, loading rates, propulsive and breaking forces, and to track variations in the center of pressure [29,30].
However, the main window on the physiological mechanisms underlying the neural control of gait is provided by electromyography (EMG), which allows to gain quantitative information regarding muscle coordination. Earlier EMG systems used cables to transmit recorded signals. This additional wiring represented a major limitation as it potentially reduced the subject’s range of motion. However, over the last two decades, EMG systems have been improved by the incorporation of technology that enables the data to be delivered wirelessly or kept in a data logger worn by the subject to detect relevant pathological features [31,32,33,34] or to drive robotic devices [35,36,37,38,39,40,41,42,43].
Concurrently, data analysis techniques have been proposed that allow us to investigate multi-muscle activity in order to quantitatively characterize the coordination among muscles acting across different joints. Muscle synergy analysis, i.e., the analysis of low-dimensional structures through the factorization of multi-muscle sEMG recordings, has gained popularity as it has been shown to provide relevant information regarding the neuro-mechanics of movement for a variety of motor tasks and motor impairment in neurological conditions [44,45,46,47,48,49,50,51,52,53,54,55,56,57]. Indeed, muscle synergies are suggested to derive from neural inputs that drive multiple muscles [58,59,60]. This idea is supported by the presence of synchronized oscillations within the firings of motor unit pools across different muscles, potentially stemming from cortical oscillations [61,62,63]. The sEMG signal represents the sum of action potentials induced in multiple muscle fibers [64,65]. Motor unit action potentials have been modeled as electromagnetic resonant modes [66], and their spectral characteristics [67] are known to change during fatiguing tasks [36,68,69], with aging [70], and at different muscle temperatures [71]. The synchronous modulation of motor unit pools is thought to reduce kinematic noise [72] and the existence of a common drive has been suggested to respect both the size principle [73,74,75,76] and the onion skin principle [77].
Many previous studies have shown that muscle coordination during human gait can be well described by the combination of a small number of muscle synergies and that each synergy is associated with specific biomechanical functions of gait in various conditions [53,78,79,80,81,82]. There is relatively vast literature analyzing muscle synergies in gait in physiological and pathological conditions, indicating how changes in the muscle synergy dimensionality and spatiotemporal structures can account for several behavioral correlates and clinical scales as well. This body of literature collectively suggests that muscle synergy analysis might be a good quantitative tool to investigate the neural correlates of gait performance and functional gait recovery.
Following the acknowledgment of motor synergies as a potential tool to compactly index motor functions [83,84,85], reviews have been published that examine the alteration of synergies after neurological diseases such as Parkinson’s disease [86], stroke [87,88], or neurodevelopmental disorders [89]. However, to the best of our knowledge, no review to date has comprehensively examined the alteration of muscle synergies identified during locomotion tasks after rehabilitation therapy. If muscle synergies capture the neurophysiological mechanisms underlying the control of gait and provide a reliable indicator of gait performance, they should also highlight specific changes in motor coordination during motor recovery. Therefore, this work aims to systematically review the literature to assess the state of the art in research on the use of muscle synergies during a locomotion task, as a quantitative tool to analyze the efficacy of rehabilitation therapy.

2. Materials and Methods

2.1. Search Strategy

A systematic search strategy was conducted using the electronic databases of PubMed, Scopus, and Web of Science and performed on the 1 April 2024. The time of publication was restricted to the interval between January 2011 and March 2024. The lower bound of January 2011 was that of the publication date of the first study that suggested muscle synergies as a tool for clinicians to assess healthy and pathological muscle activity [83]. As shown in Table 1, the searches involved a combination of the following words only in the title or abstract: “Gait”, or any word beginning with “walk” or “locomot”; “Therapy”, or “training”, or any word beginning with “rehabilit” or “neurorehabilit”; “synergy”, or “synergies”, or “muscle coordination”, or “motor module”, or “motor modules”, or “primitive”, or “primitives”.

2.2. Study Eligibility: Inclusion and Exclusion Criteria

Studies were retained if the following eligibility criteria were met: (1) a pathological condition was investigated; (2) a therapy, which was finalized to recover gait motor function, was administered; (3) the muscle synergies of the patients were assessed before and after the therapy; (4) studies followed clear and reproducible methodological stages [90], and studies were excluded when the therapy was not adequately specified, the task was not clear and reproducible, or the assessment of muscle synergies did not include the lower limb; (5) the study was published in English in peer-reviewed journals. Reviews, meta-analysis articles, perspective/position papers, editorials, commentaries, and conference papers were excluded, but case reports were retained.

2.3. Study Selection and Data Extraction

In compliance with the PRISMA statement [91,92], the eligibility of potentially relevant studies was based on title and abstract adherence to inclusion/exclusion criteria, and the screening was conducted by three authors independently (D.B., C.D., and Angelica Quercia). Full texts were then retrieved and evaluated thoroughly to confirm eligibility based on the described inclusion and exclusion criteria. Conflicts were resolved by discussions among the authors. Reference lists of the included studies were manually screened to identify additional relevant studies. From the retained studies, we extracted the following information and imported them into an Excel spreadsheet: First author, year of publication, the number of patients, the assessed pathology, the collected muscle set (for details, see Table 2), the type of task during which muscle synergies were extracted, the type of training and its duration, the algorithm used to extract the muscle synergies and define their number, the metrics adopted to compare synergies extracted before and after the training were tabulated for each study. The number of patients and the number of control participants enrolled in each study were both recorded in the spreadsheet. Additionally, a check on the inclusion of healthy control participants, the acquired clinical scales, and the presence of other kinematic measures was conducted. Figure 1 shows the PRISMA flowchart for study inclusion/exclusion.

3. Results

3.1. Selected Studies

Our search query returned a total of 563 studies, of which 26 full-text articles were retained and further screened based on their title and abstract and the inclusion/exclusion criteria. Manual reference list screening did not result in any additional studies. A total of 15 articles passed this further screening and were thus included in this review.

3.2. Study Characteristics

3.2.1. Patients

Most of the retained studies (9 out of 15) investigated the effect of physical gait rehabilitation on the muscle synergies of stroke survivors [93,94,95,96,97,98,99,100,101]. The other studies investigated Parkinson’s disease [102,103], cerebral palsy [104], multiple sclerosis [105], myelopathy [106], and brain tumor [107].
Four studies enrolled a large population of patients (≥20 patients) [96,99,100,103], while two studies were case reports [106,107], and two other studies only enrolled two patients each [94,95].
Three studies included as control groups patients who underwent conventional therapy [99], cognitive training [100], or treadmill training with body weight support [97].
Seven studies also enrolled healthy control participants to define a set of representative healthy muscle synergies sets and activation profiles for reference data [94,95,96,97,101,103,105], five of which selected age-matched healthy controls [95,96,97,103,105].

3.2.2. Task

During walking, the changes in the muscle synergies were investigated overground in 10 studies [94,95,97,98,99,100,102,103,105,106] but also on a treadmill [96,104,107]. Both overground and treadmill conditions were investigated in one study [101]. In addition to gait, in two studies, muscle synergies were also explored during a reactive balance task [102] and a recumbent cycling task [93].

3.2.3. Training and Clinical Evaluation

In the 15 retained studies, patients underwent rehabilitative training for an average of 4 weeks (range: 3–12 weeks). Rehabilitative sessions (14 on average; range: 9–36) were administered 2 to 5 times a week, and each session lasted 33 min on average (range: 5–90 min).
In 6 out of our 15 selected studies [97,98,99,101,105,107], gait rehabilitative training of the lower limbs of patients was based on robotic exoskeletons [108,109]. Lower-limb exoskeleton-assisted training was mainly used for post-stroke patients [97,98,99,101], in children with cerebral palsy [104] and in cases of thoracic myelopathy [106]. In 4 of the selected studies, multichannel Functional Electrical Stimulation (FES) was applied as a rehabilitation method combined with walking [94,95,107] and cycling [93]. FES is considered an effective intervention for lower-limb rehabilitation, particularly suitable in stroke patients, as the combination of FES with walking or cycling can enhance motor learning and plasticity, improve locomotion ability, and strengthen lower limb muscles and motor coordination [110,111]. In the absence of severe musculoskeletal and neurologic pathologies that could influence gait, treadmill walking sessions can also be used for locomotor rehabilitation [112,113]. In the retained studies, treadmill training sessions were administered both in stroke patients [96] and patients with multiple sclerosis [105]. Finally, in three out of the 15 studies [101,103,104], the rehabilitation protocol did not involve an assistive device. One study [100] investigated muscle activation patterns after trunk training in stroke patients to provide new insights in gait recovery. Another study [102], instead, investigated changes in the neuromuscular control of gait and balance after dance-based rehabilitation in Parkinson’s patients, specifically, an Adapted Tango (AT) dance. Finally, a third study [103] investigated the alteration in muscle synergies after a bilateral deep brain stimulation of the subthalamic nucleus.
Across the retained studies, participants were evaluated at baseline (T0) and after an intervention period (T1) with the following validated clinical scales used to measure patient motor functions: Functional Independence Measure-Motor General (FIM-M) [114]; Functional Independence Measure-Locomotion (FIM-Locomotion) [114]; Functional Ambulation Categories (FACs) [115]; The Motricity Index (MI) [116]; Fugl-Meyer Assessment, Lower Extremity (FMA-LE) [117]; Barthel Index (BI) [118]; Functional gait assessment (FGA) [119]; the Berg Balance scale (BBS) for standing balance [120]; Mini Best test (MBT), which evaluated the dynamic balance [121]; Fullerton Advanced Balance scale (FAB) [122]; Dynamic Gait Index (DGI) [123]; the 2 Minute Walking Test (2MWT) for gait endurance [124]; the 10 m timed walk (10MTW) for gait speed [125]; time up and go (TUG) [126]; the 6 min walk test (6MWT) [124]; Tinetti Performance Oriented Mobility Assessment (POMA) [127]; the Brunnstrom recovery stage (BRS), which evaluated impairment of the lower limb [128]; Trunk Control Test (TCT) [129]; and Trunk Impairment Scale (TIS) [130]. The motor function of children with cerebral palsy in [104] was evaluated following the Gross Motor Function Classification System (GMFCS) [131] to establish the ability to walk and which lower limb was most affected.

3.2.4. Muscles

Across the selected studies, the EMG activity was collected either unilaterally or bilaterally. Given a set of available EMG sensors, a unilateral arrangement allows to collect the activity of more muscles in a single leg, and therefore, it provides a finer characterization of the locomotor patterns which may allow for a better assessment of intra-subject variability [82]. On the contrary, a bilateral sensor arrangement allows to investigate gait asymmetries occurring as a consequence of several diseases [132]. Six out of the 15 identified studies collected muscle activity unilaterally [94,95,97,102,104,105], and seven studies collected muscle activity bilaterally [93,96,98,99,101,106,107]. One study [100] collected unilaterally the activity of six leg muscles and bilaterally the activity of one trunk muscle (i.e., the erector spinae), while another study [103] collected unilaterally the activity of eleven leg muscles and bilaterally the activity of one trunk muscle (i.e., the longissimus dorsi).
Muscle selection varied across studies. In Table 2, the number and the identity of the muscles included in each study are reported, while in Figure 2, the percentage of studies which included each muscle is reported. Due to the redundancy of the musculoskeletal system [133] or its abundance [134], different studies selected different muscles with similar function, e.g., while nine studies collected the activity only from Vastus Medialis muscle (VM), three studies collected the activity from the Vastus Lateralis muscle (VL) whose action is the same as the VM. Moreover, one study [103] collected the activity from the lateral and medial hamstring without specifying whether the activity was from the semitendinosus or the semimembranosus muscles (medial hamstring) or the short or long head of the biceps femoris (lateral hamstring).
Table 2. Muscles that were collected from the selected studies. An ‘X’ in correspondence to the column related to a muscle indicates that the muscle was acquired. The muscles were Tibialis Anterior (TA), Gastrocnemius Medialis (GM), Gastrocnemius Lateralis (GL), Vastus Medialis (VM), Vastus Lateralis (VL), Soleus (SOL), Rectus Femoris (RF), Biceps Femoris long head (BFl), Biceps Femoris short head (BFs), Semitendinosus (ST), Adductor (AD), Tensor Fascia Lata (TFL), Gluteus Maximus (Gx), Gluteus Medius (Gd), Erector Spinae (ES), External Oblique (XO), Rectus Abdominis (RA), Peroneus Longus (PL), Longissimus Dorsii (LD), Lateral Hamstrings (Hl), and Medial Hamstrings (Hm). The ‘number of muscles’ column reports how many muscles were collected on a single side (unilaterally, U) and on both sides (bilaterally, B).
Table 2. Muscles that were collected from the selected studies. An ‘X’ in correspondence to the column related to a muscle indicates that the muscle was acquired. The muscles were Tibialis Anterior (TA), Gastrocnemius Medialis (GM), Gastrocnemius Lateralis (GL), Vastus Medialis (VM), Vastus Lateralis (VL), Soleus (SOL), Rectus Femoris (RF), Biceps Femoris long head (BFl), Biceps Femoris short head (BFs), Semitendinosus (ST), Adductor (AD), Tensor Fascia Lata (TFL), Gluteus Maximus (Gx), Gluteus Medius (Gd), Erector Spinae (ES), External Oblique (XO), Rectus Abdominis (RA), Peroneus Longus (PL), Longissimus Dorsii (LD), Lateral Hamstrings (Hl), and Medial Hamstrings (Hm). The ‘number of muscles’ column reports how many muscles were collected on a single side (unilaterally, U) and on both sides (bilaterally, B).
Muscles
ReferenceTAGMGLVMVLSOLRFBFlBFsSTADTFLGxGdESXORAPLLDHlHmNumber
Allen et al., 2017 [102]XXXX XXX X XXXXX 13 U
Ambrosini et al., 2020 [93]XX XXXXX XX 9 B
Conner et al., 2021 [104]X XX X 4 U
Ferrante et al., 2016 [94]XX X XX X X 8 U
Ghislieri et al., 2023 [103]X XX XX X X X XXXX12U + 1B
Jonsdottir et al., 2020 [105]XXXX XX X X 8 U
Kadone et al., 2020 [106]XX X X X 5 B
Kinugawa et al., 2022 [107]XX XX 4 B
Lim et al., 2021 [95]XX X XX XX X 8 U
Routson et al., 2013 [96]XX X XXX X X 8 B
Srivastava et al., 2016 [97]XXXXXXXX X X 10 U
Tan et al., 2020 [99]XX X X X X 6 B
Tan et al., 2018 [98]XX X X X X 6 B
Van Criekinge et al., 2021 [100]XX X XX X X 6 U + 1 B
Zhu et al., 2021 [101]XX X XXX X X 8 B

3.2.5. Muscle Synergies Extraction

In all the retained studies, muscle synergies were analyzed within the framework of the spatial (or synchronous) model, in which the muscle activity m of M muscles can be represented as the linear combination of a set of N < M time-invariant modules W modulated by time-varying activation profiles C, as follows:
m t = ∑ i = 1 N w i   c i t + ε ( t )
In which m(t) is a vector of EMG data samples of all the recorded muscles at time t, wi is the i-th synergy vector, ci(t) is the synergy activation coefficient of the i-th synergy at time t, and ε(t) is additive noise vector at time t. In all studies but one, muscle synergies were extracted through a Non-negative Matrix Factorization (NMF) algorithm [135], which decomposes the muscle activation data matrix M = m t 1 … m t T , whose dimensions are [M × T] where M is the number of muscles and T the number of time samples, into two matrices such that M = WC+ ε, where W is an [M × N] matrix with N muscle synergies, and C is an [N × T] matrix of synergy activation coefficients. One study [93] implemented an algorithm, the Weighted Non-negative Matrix Factorization (WNMF) [136], that differs from the traditional NMF as it assigns each data sample a weight (1 =  EMG present, 0  =  EMG absent), to accommodate clinical data that contain poor or missing EMG channels.
The number of muscle synergies, which is a free parameter of the factorization algorithm, was defined in all studies but one, according to the uncentered Variance Accounted For (VAF), which measures the quality of the experimental EMG data with the extracted synergies:
V A F = 100 · 1 − ∑ t m t − W c t 2 ∑ t m t 2
One study [103] used a centered VAF, otherwise called the coefficient of determination ( R 2 ), defined as follows:
R 2 = 100 · 1 − ∑ t m t − W c t 2 ∑ t m t − m ¯ 2
where m ¯ is the mean of the observed EMG data and retained the number of synergies at which the R 2 vs. number of synergies curve achieves the highest curvature [137].
Seven studies identified the number of synergies as the set whose VAF was higher than 90% [93,96,97,100,102,105,106]. Three studies [94,98,99] added to the condition of VAF > 90% a second condition, by which a new synergy, added to the identified set, did not increase the VAF more than 5%. One study [101] exploited a ‘three-way VAF > 90%’, which imposed that the overall VAF, calculated with all muscles through the entire gait cycle, each of the VAF values separately calculated for each muscle throughout the entire gait cycle, and each of the VAF values calculated with all muscles within six separate gait phases were higher than 90%. In contrast, two studies [104,107] calculated the total variance accounted for by one synergy from the EMG data (VAF1), and one study [95] reconstructed the EMG signals collected from patients with four synergies extracted from healthy participants.

3.2.6. Muscle Synergy Analysis and Improvement-Related Metrics

To assess the effectiveness of the adopted rehabilitation therapy, the retained studies relied on a set of metrics related to the spatiotemporal structure of the extracted muscle synergies and compared their evolution along the rehabilitation process, mostly comparing the T0–T1 modifications in these metrics before (T0) and after (T1) the therapy and sometimes comparing them with the metrics of healthy participants, when available.
  • Clinical evaluation
As shown in Table 3, overall, patients improved in all clinical scales after the rehabilitation protocols. The most sensitive scales to clinical improvement in motor function resulted in the Functional Independence Measure-Locomotion (FIM-Locomotion) [114], the 10 m timed walk (10MTW) for gait speed [125], and 6 min walk test (6MWT) [124]. However, only [93] investigated correlations between muscle synergies and clinical scales. The authors found from moderate to high correlations between BBS, TCT, and motor subscale of FIM and VAF1 of the affected and unaffected leg.
  • Number of synergies
Among the analyzed parameters, the number of extracted synergies was the most commonly adopted metric (see Table 4), as 10 out of the 15 retained studies used the number of extracted synergies as a marker of the complexity of the modular organization [79] to assess rehabilitation effectiveness [93,94,96,98,99,100,101,102,103,105].
No accordance was identified among studies in terms of changes in the number of muscle synergies after the intervention. Some of the studies on stroke patients reported that an increase in the number of muscle synergies reflected an increase in performance [94,96,100] but other studies on stroke patients [93] and on patients with multiple sclerosis [105] or with Parkinson’s disease [103] did not show such change after the therapy. One study even reported a reduction in the number of muscle synergies for a subset of participants [102].
  • Spatial and temporal organization
A total of 10 out of the 15 studies made quantitative assessments of the spatial composition of the extracted muscle synergies, either by comparing their spatial structure [93,94,95,96,97,98,99,100,101,103,105] or their degree of bilateral symmetry [98,99] to that of healthy individuals. The same studies also used the temporal activation coefficients to define quantitative metrics, either by calculating the temporal symmetry between sides or by measuring the similarity with the synergy activation pattern of healthy controls. A subset of studies (4 out of 15) defined VAF-based metrics to compactly indicate the complexity of muscle coordination with the VAF explained by one synergy [93,104,106,107] or two and three synergies [106]. One study constructed specific metrics on the synergy vectors, assessing generalizability, sparsity, and variability [102].
An increase in the complexity after rehabilitation was identified in patients with brain tumor [107] or cerebral palsy [104] but not in stroke patients [93] or patients with myelopathy [106]. Finally, an increase in the synergy coefficients within gait subphases was correlated with performance increase in patients with multiple sclerosis [105].
Some neurological diseases, such as unilateral stroke, led to a kinematical asymmetry in the lower limbs during locomotion [138], which is reflected in an asymmetry in muscle synergies [139]. Therefore, an improvement in the synergy symmetry, which was accompanied by an improvement in performance, was demonstrated by [98], or a synergy timing symmetry, not accompanied by improvements in clinical scores, was identified by [99].

4. Discussion

The goal of the present review was to assess the state of the art of the investigation of the effect of gait rehabilitation in patients with neurological diseases in terms of changes in the organization and recruitment of muscle synergies. Even though muscle synergies as a tool to investigate motor coordination was introduced over two decades ago [140,141,142] and despite several studies demonstrating that neurological patients showed altered muscle synergies with respect to healthy participants [51,86,143,144,145,146] and that synergies were proposed as a potential candidate marker for the quantitative assessment of neurological pathologies [143,147], only a few studies have specifically investigated the alteration of muscle synergies after rehabilitation. Specifically, we found only 15 studies in the last 13 years that investigated the effect of gait rehabilitation therapies on muscle synergies in neurological patients and to what extent the changes in muscle synergies are related to clinical improvements.
Fourteen out of the 15 selected studies reported a modification of the muscle synergies after gait rehabilitation, and only one study [106] identified no clear effect on muscle synergies. Most of the studies that involved a control group demonstrated that rehabilitation makes the muscle synergies more similar to those of healthy participants in terms of structure. In contrast, the temporal patterns of activation of the muscle synergies identified in patients with multiple sclerosis after rehabilitation differed from those of healthy controls when walking at comparable speeds [105]. An improvement in the synergy symmetry [98], or timing synergy symmetry [99], were also identified.
No accordance could be found across studies, in terms of changes in the number of muscle synergies. While some studies on stroke patients demonstrated an increase in the number of muscle synergies, also related to a performance increase [94,96,100], other studies that enrolled patients with multiple sclerosis [105], Parkinson’s disease [102], or stroke patients [93] either found no change or even a reduction in the number of muscle synergies [102]. Moreover, an increase in the number of synergies after rehabilitation was identified in patients with brain tumor [107] or cerebral palsy [104] but not in stroke patients [93] or patients with myelopathy [106]. Finally, an increase in the synergy coefficients within gait subphases was correlated with performance increase in patients with multiple sclerosis [105].
Overall, this review supports the hypothesis that modifications in the muscle synergies can index the progression of the rehabilitation process in an interpretable and quantitatively measurable manner.
Discrepancies were found among the studies. These discrepancies may be a consequence of the investigated neurological pathologies, which differ among the studies, and therefore may differently influence motor control and muscle synergies. Moreover, these discrepancies may reflect the lack of standardized protocols for processing and investigating muscle synergies and to compare them with those extracted from healthy participants.
When characterizing the modular control of locomotion in healthy subjects, four-to--five muscle synergies are typically extracted, and they are usually related to four specific biomechanical sub-functions along the gait cycle [79]:
-
M1—a knee-hip extensor module, activated during early stance, serving as body support and weight acceptance
-
M2—a calf plantar-flexor muscle module, activated during late stance, with forward propulsion, body support, and swing preparation function
-
M3—an ankle dorsiflexion module, activated during early swing, contributing to the ground clearance of the foot
-
M4—a knee flexor module, activated during late swing, to decelerate the leg an prepare heel strike
Despite the previously discussed large variability both in adopted methodologies and pathological conditions across studies, few invariances could be identified regarding the effect of rehabilitation therapy on muscle synergies. Indeed, regardless of the adopted metric to characterize the change, all those studies showing modifications in the spatial organization or temporal recruitment of muscle synergies after the therapy [70,71,72,73,77,78,82] reported a modification related to the plantarflexion module M2 and/or the knee flexor module M4, likely indicating an effect on forward propulsion and balance.
A recommendation for future research is to define standardized protocols and algorithms for the characterization of synergy structures and patterns in neurological patients, and for their comparison with healthy participants [148]. This would allow for a systematic characterization and comparison of the muscle synergies of patients with different pathologies and different levels of motor impairment.
Moreover, this review underlines the need for further research on muscle synergy analysis in the assessment of neurological patients’ rehabilitation, before it can be fully transferred to the clinical practice as a marker of the progression of the rehabilitation and as a support to clinical decision making [85], which would provide clinicians and therapists with a novel instrument to assess the efficacy of a therapy and whether and how it should be changed, not only by analyzing movement kinematics and kinetics but also through the lens of the underlying neural control strategies.

5. Conclusions

The present review strongly suggests that muscle synergy analysis has a very high potential as a tool to quantitatively assess the efficacy of rehabilitation therapies in neurological patients. However, it also highlights the significant lack of studies specifically investigating the effect of physical rehabilitation on muscle synergies. In the last 13 years, only 15 studies have examined the alteration of muscle synergies after physical rehabilitation during locomotion tasks. This indicates that the potential of muscle synergy analysis remains largely untapped as it has been scarcely used in clinical studies so far. For this promising approach to move to the clinical practice, the scientific community working on these topics should spend a greater effort in defining a methodological standardization of the assessment protocols and algorithms for the extraction and description of muscle synergies, together with the creation of an extensive publicly available database of the synergies identified in patients with different neurological pathologies and different levels of impairment. This would facilitate the standardization of the adopted procedures, together with the related metrics to quantify the effect of motor rehabilitation on the muscle synergies of a patient. This would provide clinicians and physiotherapists with a novel tool to be used as a marker of the effectiveness of therapy, as well as a source of information to develop innovative therapies.

Author Contributions

Conceptualization, D.B., C.D.M. and A.d.; methodology, D.B., C.D.M. and A.Q. (Angelica Quercia); formal analysis, D.B., C.D.M. and A.Q. (Angelica Quercia); data curation, D.B., C.D.M. and A.Q. (Angelica Quercia); writing—original draft preparation, D.B., C.D.M. and A.Q. (Angelica Quercia); writing—review and editing, D.B., C.D.M., A.Q. (Angelica Quercia), P.D.P., A.C., A.Q. (Angelo Quartarone), R.S.C. and A.d. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the American Ministry of Defense (DoD Grant), grant number W81XWH-19-1-0810.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be made available on request by the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Knutsson, E.; Richards, C. Different Types of Disturbed Motor Control in Gait of Hemiparetic Patients. Brain 1979, 102, 405–430. [Google Scholar] [CrossRef] [PubMed]
  2. Petersen, T.H.; Willerslev-Olsen, M.; Conway, B.A.; Nielsen, J.B. The Motor Cortex Drives the Muscles during Walking in Human Subjects. J. Physiol. 2012, 590, 2443–2452. [Google Scholar] [CrossRef] [PubMed]
  3. Schubert, M.; Curt, A.; Jensen, L.; Dietz, V. Corticospinal Input in Human Gait: Modulation of Magnetically Evoked Motor Responses. Exp. Brain Res. 1997, 115, 234–246. [Google Scholar] [CrossRef]
  4. Winter, D.A.; MacKinnon, C.D.; Ruder, G.K.; Wieman, C. Chapter 32 An Integrated EMG/Biomechanical Model of Upper Body Balance and Posture during Human Gait. In Progress in Brain Research; Natural and Artificial Control of Hearing and Balance; Allum, J.H.J., Allum-Mecklenburg, D.J., Harris, F.P., Probst, R., Eds.; Elsevier: Amsterdam, The Netherlands, 1993; Volume 97, pp. 359–367. [Google Scholar]
  5. Stolze, H.; Klebe, S.; Zechlin, C.; Baecker, C.; Friege, L.; Deuschl, G. Falls in Frequent Neurologicaldiseases. J. Neurol. 2004, 251, 79–84. [Google Scholar] [CrossRef] [PubMed]
  6. Ebersbach, G.; Sojer, M.; Valldeoriola, F.; Wissel, J.; Müller, J.; Tolosa, E.; Poewe, W. Comparative Analysis of Gait in Parkinson’s Disease, Cerebellar Ataxia and Subcortical Arteriosclerotic Encephalopathy. Brain 1999, 122, 1349–1355. [Google Scholar] [CrossRef] [PubMed]
  7. Chaparro-Cárdenas, S.L.; Lozano-Guzmán, A.A.; Ramirez-Bautista, J.A.; Hernández-Zavala, A. A Review in Gait Rehabilitation Devices and Applied Control Techniques. Disabil. Rehabil. Assist. Technol. 2018, 13, 819–834. [Google Scholar] [CrossRef]
  8. Vitiello, N.; Oddo, C.M.; Lenzi, T.; Roccella, S.; Beccai, L.; Vecchi, F.; Carrozza, M.C.; Dario, P. Neuro-Robotics Paradigm for Intelligent Assistive Technologies. In Intelligent Assistive Robots: Recent Advances in Assistive Robotics for Everyday Activities; Springer Tracts in Advanced Robotics; Mohammed, S., Moreno, J.C., Kong, K., Amirat, Y., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 1–40. ISBN 978-3-319-12922-8. [Google Scholar]
  9. Senanayake, C.; Senanayake, S.M.N.A. Emerging Robotics Devices for Therapeutic Rehabilitation of the Lower Extremity. In Proceedings of the 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore, 14–17 July 2009; pp. 1142–1147. [Google Scholar]
  10. Díaz, I.; Gil, J.J.; Sánchez, E. Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. J. Robot. 2011, 2011, e759764. [Google Scholar] [CrossRef]
  11. Balaji, E.; Brindha, D.; Balakrishnan, R. Supervised Machine Learning Based Gait Classification System for Early Detection and Stage Classification of Parkinson’s Disease. Appl. Soft Comput. 2020, 94, 106494. [Google Scholar] [CrossRef]
  12. Kalron, A.; Achiron, A.; Dvir, Z. Muscular and Gait Abnormalities in Persons With Early Onset Multiple Sclerosis. J. Neurol. Phys. Ther. 2011, 35, 164–169. [Google Scholar] [CrossRef]
  13. Pistacchi, M.; Gioulis, M.; Sanson, F.; De Giovannini, E.; Filippi, G.; Rossetto, F.; Marsala, S.Z. Gait Analysis and Clinical Correlations in Early Parkinson’s Disease. Funct. Neurol. 2017, 32, 28–34. [Google Scholar] [CrossRef]
  14. Alito, A.; Fenga, D.; Portaro, S.; Leonardi, G.; Borzelli, D.; Sanzarello, I.; Calabrò, R.; Milone, D.; Tisano, A.; Leonetti, D. Early Hip Fracture Surgery and Rehabilitation. How to Improve Functional Quality Outcomes. A Retrospective Study. Folia Med. 2023, 65, 879–884. [Google Scholar] [CrossRef]
  15. Borggraefe, I.; Meyer-Heim, A.; Kumar, A.; Schaefer, J.S.; Berweck, S.; Heinen, F. Improved Gait Parameters after Robotic-Assisted Locomotor Treadmill Therapy in a 6-Year-Old Child with Cerebral Palsy. Mov. Disord. 2008, 23, 280–283. [Google Scholar] [CrossRef]
  16. Gunning, E.; Uszynski, M.K. Effectiveness of the Proprioceptive Neuromuscular Facilitation Method on Gait Parameters in Patients With Stroke: A Systematic Review. Arch. Phys. Med. Rehabil. 2019, 100, 980–986. [Google Scholar] [CrossRef]
  17. Rocha, P.A.; Porfírio, G.M.; Ferraz, H.B.; Trevisani, V.F.M. Effects of External Cues on Gait Parameters of Parkinson’s Disease Patients: A Systematic Review. Clin. Neurol. Neurosurg. 2014, 124, 127–134. [Google Scholar] [CrossRef]
  18. Maggio, M.G.; Cezar, R.P.; Milardi, D.; Borzelli, D.; De Marchis, C.; D’avella, A.; Quartarone, A.; Calabrò, R.S. Do Patients with Neurological Disorders Benefit from Immersive Virtual Reality? A Scoping Review on the Emerging Use of the Computer-Assisted Rehabilitation Environment. Eur. J. Phys. Rehabil. Med. 2024, 60, 37–43. [Google Scholar] [CrossRef]
  19. Ruiz-Ruiz, L.; Jimenez, A.R.; Garcia-Villamil, G.; Seco, F. Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters. Sensors 2021, 21, 6918. [Google Scholar] [CrossRef]
  20. Kondragunta, J.; Hirtz, G. Gait Parameter Estimation of Elderly People Using 3D Human Pose Estimation in Early Detection of Dementia. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; pp. 5798–5801. [Google Scholar]
  21. Pfister, A.; West, A.M.; Bronner, S.; Noah, J.A. Comparative Abilities of Microsoft Kinect and Vicon 3D Motion Capture for Gait Analysis. J. Med. Eng. Technol. 2014, 38, 274–280. [Google Scholar] [CrossRef]
  22. Sigal, L.; Balan, A.O.; Black, M.J. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion. Int. J. Comput. Vis. 2010, 87, 4–27. [Google Scholar] [CrossRef]
  23. Kanko, R.M.; Laende, E.K.; Strutzenberger, G.; Brown, M.; Selbie, W.S.; DePaul, V.; Scott, S.H.; Deluzio, K.J. Assessment of Spatiotemporal Gait Parameters Using a Deep Learning Algorithm-Based Markerless Motion Capture System. J. Biomech. 2021, 122, 110414. [Google Scholar] [CrossRef]
  24. Mathis, M.W.; Mathis, A. Deep Learning Tools for the Measurement of Animal Behavior in Neuroscience. Curr. Opin. Neurobiol. 2020, 60, 1–11. [Google Scholar] [CrossRef]
  25. Gu, C.; Lin, W.; He, X.; Zhang, L.; Zhang, M. IMU-Based Motion Capture System for Rehabilitation Applications: A Systematic Review. Biomim. Intell. Robot. 2023, 3, 100097. [Google Scholar] [CrossRef]
  26. Gujarathi, T.; Bhole, K. Gait Analysis Using Imu Sensor. In Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, 6–8 July 2019; pp. 1–5. [Google Scholar]
  27. Panero, E.; Digo, E.; Agostini, V.; Gastaldi, L. Comparison of Different Motion Capture Setups for Gait Analysis: Validation of Spatio-Temporal Parameters Estimation. In Proceedings of the 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 11–13 June 2018; pp. 1–6. [Google Scholar]
  28. Washabaugh, E.P.; Kalyanaraman, T.; Adamczyk, P.G.; Claflin, E.S.; Krishnan, C. Validity and Repeatability of Inertial Measurement Units for Measuring Gait Parameters. Gait Posture 2017, 55, 87–93. [Google Scholar] [CrossRef]
  29. Belli, A.; Bui, P.; Berger, A.; Geyssant, A.; Lacour, J.-R. A Treadmill Ergometer for Three-Dimensional Ground Reaction Forces Measurement during Walking. J. Biomech. 2001, 34, 105–112. [Google Scholar] [CrossRef]
  30. Dierick, F.; Penta, M.; Renaut, D.; Detrembleur, C. A Force Measuring Treadmill in Clinical Gait Analysis. Gait Posture 2004, 20, 299–303. [Google Scholar] [CrossRef]
  31. Campanini, I.; Disselhorst-Klug, C.; Rymer, W.Z.; Merletti, R. Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use. Front. Neurol. 2020, 11, 934. [Google Scholar] [CrossRef]
  32. Stlberg, E.; Falck, B. The Role of Electromyography in Neurology. Electroencephalogr. Clin. Neurophysiol. 1997, 103, 579–598. [Google Scholar] [CrossRef]
  33. Zwarts, M.J.; Drost, G.; Stegeman, D.F. Recent Progress in the Diagnostic Use of Surface EMG for Neurological Diseases. J. Electromyogr. Kinesiol. 2000, 10, 287–291. [Google Scholar] [CrossRef]
  34. Borzelli, D.; Cesqui, B.; Berger, D.J.; Burdet, E.; d’Avella, A. Muscle Patterns Underlying Voluntary Modulation of Co-Contraction. PLoS ONE 2018, 13, e0205911. [Google Scholar] [CrossRef]
  35. Borzelli, D.; Pastorelli, S.; Gastaldi, L. Determination of the Human Arm Stiffness Efficiency with a Two Antagonist Muscles Model. In Advances in Italian Mechanism Science: Proceedings of the First International Conference of IFToMM Italy, Vicenza, Italy, 1–2 December 2016; Boschetti, G., Gasparetto, A., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 71–78. [Google Scholar]
  36. Borzelli, D.; Burdet, E.; Pastorelli, S.; d’Avella, A.; Gastaldi, L. Identification of the Best Strategy to Command Variable Stiffness Using Electromyographic Signals. J. Neural Eng. 2020, 17, 016058. [Google Scholar] [CrossRef]
  37. Cimolato, A.; Driessen, J.J.M.; Mattos, L.S.; De Momi, E.; Laffranchi, M.; De Michieli, L. EMG-Driven Control in Lower Limb Prostheses: A Topic-Based Systematic Review. J. Neuroeng. Rehabil. 2022, 19, 43. [Google Scholar] [CrossRef]
  38. Manal, K.; Gonzalez, R.V.; Lloyd, D.G.; Buchanan, T.S. A Real-Time EMG-Driven Virtual Arm. Comput. Biol. Med. 2002, 32, 25–36. [Google Scholar] [CrossRef]
  39. Yang, H.; Wan, J.; Jin, Y.; Yu, X.; Fang, Y. EEG- and EMG-Driven Poststroke Rehabilitation: A Review. IEEE Sens. J. 2022, 22, 23649–23660. [Google Scholar] [CrossRef]
  40. Borzelli, D.; Pastorelli, S.; Gastaldi, L. Model of the Human Arm Stiffness Exerted by Two Antagonist Muscles. In Advances in Robot Design and Intelligent Control: Proceedings of the 25th Conference on Robotics in Alpe-Adria-Danube Region (RAAD16), Belgrade, Serbia, 30 June–2 July 2016; Rodić, A., Borangiu, T., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 285–292. [Google Scholar]
  41. Borzelli, D.; Pastorelli, S.; d’Avella, A.; Gastaldi, L. Virtual Stiffness: A Novel Biomechanical Approach to Estimate Limb Stiffness of a Multi-Muscle and Multi-Joint System. Sensors 2023, 23, 673. [Google Scholar] [CrossRef]
  42. Borzelli, D.; Gurgone, S.; De Pasquale, P.; Lotti, N.; d’Avella, A.; Gastaldi, L. Use of Surface Electromyography to Estimate End-Point Force in Redundant Systems: Comparison between Linear Approaches. Bioengineering 2023, 10, 234. [Google Scholar] [CrossRef]
  43. Borzelli, D.; Pastorelli, S.; Gastaldi, L. Elbow Musculoskeletal Model for Industrial Exoskeleton with Modulated Impedance Based on Operator’s Arm Stiffness. Int. J. Autom. Technol. 2017, 11, 442–449. [Google Scholar] [CrossRef]
  44. Berger, D.J.; Gentner, R.; Edmunds, T.; Pai, D.K.; d’Avella, A. Differences in Adaptation Rates after Virtual Surgeries Provide Direct Evidence for Modularity. J. Neurosci. 2013, 33, 12384–12394. [Google Scholar] [CrossRef]
  45. Berger, D.J.; Masciullo, M.; Molinari, M.; Lacquaniti, F.; d’Avella, A. Does the Cerebellum Shape the Spatiotemporal Organization of Muscle Patterns? Insights from Subjects with Cerebellar Ataxias. J. Neurophysiol. 2020, 123, 1691–1710. [Google Scholar] [CrossRef]
  46. Berger, D.J.; Borzelli, D.; d’Avella, A. Task Space Exploration Improves Adaptation after Incompatible Virtual Surgeries. J. Neurophysiol. 2022, 127, 1127–1146. [Google Scholar] [CrossRef]
  47. Borzelli, D.; Berger, D.; Pai, D.; D’avella, A. Effort Minimization and Synergistic Muscle Recruitment for Three-Dimensional Force Generation. Front. Comput. Neurosci. 2013, 7, 186. [Google Scholar] [CrossRef]
  48. Cheung, V.C.K.; Piron, L.; Agostini, M.; Silvoni, S.; Turolla, A.; Bizzi, E. Stability of Muscle Synergies for Voluntary Actions after Cortical Stroke in Humans. Proc. Natl. Acad. Sci. USA 2009, 106, 19563–19568. [Google Scholar] [CrossRef]
  49. d’Avella, A.; Portone, A.; Fernandez, L.; Lacquaniti, F. Control of Fast-Reaching Movements by Muscle Synergy Combinations. J. Neurosci. 2006, 26, 7791–7810. [Google Scholar] [CrossRef]
  50. De Marchis, C.; Schmid, M.; Bibbo, D.; Bernabucci, I.; Conforto, S. Inter-Individual Variability of Forces and Modular Muscle Coordination in Cycling: A Study on Untrained Subjects. Hum. Mov. Sci. 2013, 32, 1480–1494. [Google Scholar] [CrossRef]
  51. Dipietro, L.; Krebs, H.I.; Fasoli, S.E.; Volpe, B.T.; Stein, J.; Bever, C.; Hogan, N. Changing Motor Synergies in Chronic Stroke. J. Neurophysiol. 2007, 98, 757–768. [Google Scholar] [CrossRef] [PubMed]
  52. Gentner, R.; Edmunds, T.; Pai, D.; D’avella, A. Robustness of Muscle Synergies during Visuomotor Adaptation. Front. Comput. Neurosci. 2013, 7, 120. [Google Scholar] [CrossRef]
  53. Ivanenko, Y.P.; Poppele, R.E.; Lacquaniti, F. Five Basic Muscle Activation Patterns Account for Muscle Activity during Human Locomotion. J. Physiol. 2004, 556, 267–282. [Google Scholar] [CrossRef] [PubMed]
  54. Roh, J.; Rymer, W.Z.; Perreault, E.J.; Yoo, S.B.; Beer, R.F. Alterations in Upper Limb Muscle Synergy Structure in Chronic Stroke Survivors. J. Neurophysiol. 2013, 109, 768–781. [Google Scholar] [CrossRef]
  55. Torres-Oviedo, G.; Ting, L.H. Subject-Specific Muscle Synergies in Human Balance Control Are Consistent Across Different Biomechanical Contexts. J. Neurophysiol. 2010, 103, 3084–3098. [Google Scholar] [CrossRef]
  56. Borzelli, D.; Gurgone, S.; Mezzetti, M.; De Pasquale, P.; Berger, D.J.; Milardi, D.; Acri, G.; D’Avella, A. Adaptation to Virtual Surgeries Across Multiple Practice Sessions. In Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), Online, 13–16 October 2020; Torricelli, D., Akay, M., Pons, J.L., Eds.; Springer International Publishing: Cham, Switzerland, 2022; pp. 563–568. [Google Scholar]
  57. Borzelli, D.; Gurgone, S.; De Pasquale, P.; Berger, D.J.; d’Avella, A. Consistency of Myoelectric Control Across Multiple Sessions. In Converging Clinical and Engineering Research on Neurorehabilitation III: Proceedings of the 4th International Conference on NeuroRehabilitation (ICNR2018), Pisa, Italy, 16–20 October 2018; Masia, L., Micera, S., Akay, M., Pons, J.L., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 1166–1170. [Google Scholar]
  58. Borzelli, D.; Vieira, T.M.M.; Botter, A.; Gazzoni, M.; Lacquaniti, F.; d’Avella, A. Synaptic Inputs to Motor Neurons Underlying Muscle Co-Activation for Functionally Different Tasks Have Different Spectral Characteristics. J. Neurophysiol. 2024, 131, 1126–1142. [Google Scholar] [CrossRef]
  59. Laine, C.M.; Martinez-Valdes, E.; Falla, D.; Mayer, F.; Farina, D. Motor Neuron Pools of Synergistic Thigh Muscles Share Most of Their Synaptic Input. J. Neurosci. 2015, 35, 12207–12216. [Google Scholar] [CrossRef]
  60. Levine, J.; Avrillon, S.; Farina, D.; Hug, F.; Pons, J.L. Two Motor Neuron Synergies, Invariant across Ankle Joint Angles, Activate the Triceps Surae during Plantarflexion. J. Physiol. 2023, 601, 4337–4354. [Google Scholar] [CrossRef]
  61. David, F.J.; Munoz, M.J.; Corcos, D.M. The Effect of STN DBS on Modulating Brain Oscillations: Consequences for Motor and Cognitive Behavior. Exp. Brain Res. 2020, 238, 1659–1676. [Google Scholar] [CrossRef]
  62. Leonardi, G.; Ciurleo, R.; Cucinotta, F.; Fonti, B.; Borzelli, D.; Costa, L.; Tisano, A.; Portaro, S.; Alito, A. The Role of Brain Oscillations in Post-Stroke Motor Recovery: An Overview. Front. Syst. Neurosci. 2022, 16, 947421. [Google Scholar] [CrossRef]
  63. MacKay, W.A. Synchronized Neuronal Oscillations and Their Role in Motor Processes. Trends Cogn. Sci. 1997, 1, 176–183. [Google Scholar] [CrossRef] [PubMed]
  64. Basmajian, J.V. Muscles Alive. Their Functions Revealed by Electromyography. Acad. Med. 1962, 37, 802. [Google Scholar]
  65. De Luca, C.J. Physiology and Mathematics of Myoelectric Signals. IEEE Trans. Biomed. Eng. 1979, BME-26, 313–325. [Google Scholar] [CrossRef]
  66. Lugo, J.E.; Doti, R.; Faubert, J. Planckian Power Spectral Densities from Human Calves during Posture Maintenance and Controlled Isometric Contractions. PLoS ONE 2015, 10, e0131798. [Google Scholar] [CrossRef]
  67. Lindstrom, L.H.; Magnusson, R.I. Interpretation of Myoelectric Power Spectra: A Model and Its Applications. Proc. IEEE 1977, 65, 653–662. [Google Scholar] [CrossRef]
  68. Merletti, R.; Parker, P.J. Electromyography: Physiology, Engineering, and Non-Invasive Applications; John Wiley & Sons: Hoboken, NJ, USA, 2004; ISBN 978-0-471-67580-8. [Google Scholar]
  69. Castronovo, A.M.; De Marchis, C.; Schmid, M.; Conforto, S.; Severini, G. Effect of Task Failure on Intermuscular Coherence Measures in Synergistic Muscles. Appl. Bionics Biomech. 2018, 2018, e4759232. [Google Scholar] [CrossRef]
  70. Yamada, H.; Okada, M.; Oda, T.; Nemoto, S.; Shiozaki, T.; Kizuka, T.; Kuno, S.; Masuda, T. Effects of Aging on Emg Variables During Fatiguing Isometric Contractions. J. Hum. Ergol. 2000, 29, 7–14. [Google Scholar] [CrossRef]
  71. Petrofsky, J.; Laymon, M. Muscle Temperature and EMG Amplitude and Frequency during Isometric Exercise. Aviat. Space Environ. Med. 2005, 76, 1024–1030. [Google Scholar]
  72. Farina, D.; Negro, F.; Dideriksen, J.L. The Effective Neural Drive to Muscles Is the Common Synaptic Input to Motor Neurons: Effective Neural Drive to Muscles. J. Physiol. 2014, 592, 3427–3441. [Google Scholar] [CrossRef]
  73. Henneman, E.; Somjen, G.; Carpenter, D.O. Functional Significance of Cell Size in Spinal Motoneurons. J. Neurophysiol. 1965, 28, 560–580. [Google Scholar] [CrossRef]
  74. Henneman, E. Relation between Size of Neurons and Their Susceptibility to Discharge. Science 1957, 126, 1345–1347. [Google Scholar] [CrossRef] [PubMed]
  75. Monster, A.W.; Chan, H. Isometric Force Production by Motor Units of Extensor Digitorum Communis Muscle in Man. J. Neurophysiol. 1977, 40, 1432–1443. [Google Scholar] [CrossRef]
  76. Hug, F.; Avrillon, S.; Ibáñez, J.; Farina, D. Common Synaptic Input, Synergies and Size Principle: Control of Spinal Motor Neurons for Movement Generation. J. Physiol. 2023, 601, 11–20. [Google Scholar] [CrossRef] [PubMed]
  77. de Luca, C.J.; LeFever, R.S.; McCue, M.P.; Xenakis, A.P. Behaviour of Human Motor Units in Different Muscles during Linearly Varying Contractions. J. Physiol. 1982, 329, 113–128. [Google Scholar] [CrossRef]
  78. Chvatal, S.A.; Ting, L.H. Voluntary and Reactive Recruitment of Locomotor Muscle Synergies during Perturbed Walking. J. Neurosci. 2012, 32, 12237–12250. [Google Scholar] [CrossRef]
  79. Clark, D.J.; Ting, L.H.; Zajac, F.E.; Neptune, R.R.; Kautz, S.A. Merging of Healthy Motor Modules Predicts Reduced Locomotor Performance and Muscle Coordination Complexity Post-Stroke. J. Neurophysiol. 2010, 103, 844–857. [Google Scholar] [CrossRef]
  80. De Marchis, C.; Ranaldi, S.; Serrao, M.; Ranavolo, A.; Draicchio, F.; Lacquaniti, F.; Conforto, S. Modular Motor Control of the Sound Limb in Gait of People with Trans-Femoral Amputation. J. NeuroEng. Rehabil. 2019, 16, 132. [Google Scholar] [CrossRef] [PubMed]
  81. Neptune, R.R.; Clark, D.J.; Kautz, S.A. Modular Control of Human Walking: A Simulation Study. J. Biomech. 2009, 42, 1282–1287. [Google Scholar] [CrossRef] [PubMed]
  82. Rimini, D.; Agostini, V.; Knaflitz, M. Intra-Subject Consistency during Locomotion: Similarity in Shared and Subject-Specific Muscle Synergies. Front. Hum. Neurosci. 2017, 11, 292485. [Google Scholar] [CrossRef]
  83. Safavynia, S.; Torres-Oviedo, G.; Ting, L. Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement. Top. Spinal Cord Inj. Rehabil. 2011, 17, 16–24. [Google Scholar] [CrossRef]
  84. Zhao, K.; Zhang, Z.; Wen, H.; Liu, B.; Li, J.; d’Avella, A.; Scano, A. Muscle Synergies for Evaluating Upper Limb in Clinical Applications: A Systematic Review. Heliyon 2023, 9, e16202. [Google Scholar] [CrossRef]
  85. Scano, A.; Lanzani, V.; Brambilla, C.; d’Avella, A. Transferring Sensor-Based Assessments to Clinical Practice: The Case of Muscle Synergies. Sensors 2024, 24, 3934. [Google Scholar] [CrossRef]
  86. Mileti, I.; Zampogna, A.; Santuz, A.; Asci, F.; Del Prete, Z.; Arampatzis, A.; Palermo, E.; Suppa, A. Muscle Synergies in Parkinson’s Disease. Sensors 2020, 20, 3209. [Google Scholar] [CrossRef] [PubMed]
  87. Hong, Y.N.G.; Ballekere, A.N.; Fregly, B.J.; Roh, J. Are Muscle Synergies Useful for Stroke Rehabilitation? Curr. Opin. Biomed. Eng. 2021, 19, 100315. [Google Scholar] [CrossRef]
  88. Van Criekinge, T.; Vermeulen, J.; Wagemans, K.; Schröder, J.; Embrechts, E.; Truijen, S.; Hallemans, A.; Saeys, W. Lower Limb Muscle Synergies during Walking after Stroke: A Systematic Review. Disabil. Rehabil. 2020, 42, 2836–2845. [Google Scholar] [CrossRef]
  89. Beltrame, G.; Scano, A.; Marino, G.; Peccati, A.; Molinari Tosatti, L.; Portinaro, N. Recent Developments in Muscle Synergy Analysis in Young People with Neurodevelopmental Diseases: A Systematic Review. Front. Bioeng. Biotechnol. 2023, 11, 1145937. [Google Scholar] [CrossRef] [PubMed]
  90. Khan, M.H.; Farid, M.S.; Grzegorzek, M. Vision-Based Approaches towards Person Identification Using Gait. Comput. Sci. Rev. 2021, 42, 100432. [Google Scholar] [CrossRef]
  91. Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. Ann. Intern. Med. 2009, 151, W-65–W-94. [Google Scholar] [CrossRef]
  92. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Int. J. Surg. 2021, 88, 105906. [Google Scholar] [CrossRef] [PubMed]
  93. Ambrosini, E.; Parati, M.; Peri, E.; De Marchis, C.; Nava, C.; Pedrocchi, A.; Ferriero, G.; Ferrante, S. Changes in Leg Cycling Muscle Synergies after Training Augmented by Functional Electrical Stimulation in Subacute Stroke Survivors: A Pilot Study. J. NeuroEng. Rehabil. 2020, 17, 35. [Google Scholar] [CrossRef]
  94. Ferrante, S.; Chia Bejarano, N.; Ambrosini, E.; Nardone, A.; Turcato, A.M.; Monticone, M.; Ferrigno, G.; Pedrocchi, A. A Personalized Multi-Channel FES Controller Based on Muscle Synergies to Support Gait Rehabilitation after Stroke. Front. Neurosci. 2016, 10, 425. [Google Scholar] [CrossRef]
  95. Lim, J.; Lim, T.; Lee, J.; Sim, J.; Chang, H.; Yoon, B.; Jung, H. Patient-Specific Functional Electrical Stimulation Strategy Based on Muscle Synergy and Walking Posture Analysis for Gait Rehabilitation of Stroke Patients. J. Int. Med. Res. 2021, 49, 03000605211016782. [Google Scholar] [CrossRef]
  96. Routson, R.L.; Clark, D.J.; Bowden, M.G.; Kautz, S.A.; Neptune, R.R. The Influence of Locomotor Rehabilitation on Module Quality and Post-Stroke Hemiparetic Walking Performance. Gait Posture 2013, 38, 511–517. [Google Scholar] [CrossRef] [PubMed]
  97. Srivastava, S.; Kao, P.C.; Reisman, D.S.; Scholz, J.P.; Agrawal, S.K.; Higginson, J.S. Robotic Assist-As-Needed as an Alternative to Therapist-Assisted Gait Rehabilitation. Int. J. Phys. Med. Rehabil. 2016, 4, 370. [Google Scholar] [CrossRef] [PubMed]
  98. Tan, C.K.; Kadone, H.; Watanabe, H.; Marushima, A.; Yamazaki, M.; Sankai, Y.; Suzuki, K. Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-Stroke Patients After Robotic Intervention. Front. Neurosci. 2018, 12, 276. [Google Scholar] [CrossRef]
  99. Tan, C.K.; Kadone, H.; Watanabe, H.; Marushima, A.; Hada, Y.; Yamazaki, M.; Sankai, Y.; Matsumura, A.; Suzuki, K. Differences in Muscle Synergy Symmetry between Subacute Post-Stroke Patients with Bioelectrically-Controlled Exoskeleton Gait Training and Conventional Gait Training. Front. Bioeng. Biotechnol. 2020, 8, 770. [Google Scholar] [CrossRef]
  100. Van Criekinge, T.; Hallemans, A.; Herssens, N.; Lafosse, C.; Claes, D.; De Hertogh, W.; Truijen, S.; Saeys, W. SWEAT2 Study: Effectiveness of Trunk Training on Gait and Trunk Kinematics After Stroke: A Randomized Controlled Trial. Phys. Ther. 2020, 100, 1568–1581. [Google Scholar] [CrossRef]
  101. Zhu, F.; Kern, M.; Fowkes, E.; Afzal, T.; Contreras-Vidal, J.-L.; Francisco, G.E.; Chang, S.-H. Effects of an Exoskeleton-Assisted Gait Training on Post-Stroke Lower-Limb Muscle Coordination. J. Neural Eng. 2021, 18, 046039. [Google Scholar] [CrossRef]
  102. Allen, J.L.; McKay, J.L.; Sawers, A.; Hackney, M.E.; Ting, L.H. Increased Neuromuscular Consistency in Gait and Balance after Partnered, Dance-Based Rehabilitation in Parkinson’s Disease. APSselect 2017, 4, 363–373. [Google Scholar] [CrossRef] [PubMed]
  103. Ghislieri, M.; Lanotte, M.; Knaflitz, M.; Rizzi, L.; Agostini, V. Muscle Synergies in Parkinson’s Disease before and after the Deep Brain Stimulation of the Bilateral Subthalamic Nucleus. Sci. Rep. 2023, 13, 6997. [Google Scholar] [CrossRef] [PubMed]
  104. Conner, B.C.; Schwartz, M.H.; Lerner, Z.F. Pilot Evaluation of Changes in Motor Control after Wearable Robotic Resistance Training in Children with Cerebral Palsy. J. Biomech. 2021, 126, 110601. [Google Scholar] [CrossRef]
  105. Jonsdottir, J.; Lencioni, T.; Gervasoni, E.; Crippa, A.; Anastasi, D.; Carpinella, I.; Rovaris, M.; Cattaneo, D.; Ferrarin, M. Improved Gait of Persons with Multiple Sclerosis after Rehabilitation: Effects on Lower Limb Muscle Synergies, Push-Off, and Toe-Clearance. Front. Neurol. 2020, 11, 668. [Google Scholar] [CrossRef] [PubMed]
  106. Kadone, H.; Kubota, S.; Abe, T.; Noguchi, H.; Miura, K.; Koda, M.; Shimizu, Y.; Hada, Y.; Sankai, Y.; Suzuki, K.; et al. Muscular Activity Modulation During Post-Operative Walking With Hybrid Assistive Limb (HAL) in a Patient With Thoracic Myelopathy Due to Ossification of Posterior Longitudinal Ligament: A Case Report. Front. Neurol. 2020, 11, 102. [Google Scholar] [CrossRef] [PubMed]
  107. Kinugawa, K.; Mano, T.; Wada, H.; Ozaki, M.; Shirai, D.; Imura, T.; Kido, A. Improvement in Lower Extremity Hemiplegia in a Post-Operative Brain Tumor Patient by Applying an Integrated Volitional Control Electrical Stimulator. J. Phys. Ther. Sci. 2022, 34, 473–477. [Google Scholar] [CrossRef]
  108. Calafiore, D.; Negrini, F.; Tottoli, N.; Ferraro, F.; Ozyemisci-Taskiran, O.; de Sire, A. Efficacy of Robotic Exoskeleton for Gait Rehabilitation in Patients with Subacute Stroke: A Systematic Review. Eur. J. Phys. Rehabil. Med. 2022, 58, 1–8. [Google Scholar] [CrossRef] [PubMed]
  109. Chen, G.; Chan, C.K.; Guo, Z.; Yu, H. A Review of Lower Extremity Assistive Robotic Exoskeletons in Rehabilitation Therapy. Crit. Rev. Biomed. Eng. 2013, 41, 343–363. [Google Scholar] [CrossRef]
  110. Kostov, A.; Stein, R.B.; Popović, D.; Armstrong, W.W. Improved Methods for Control of FES for Locomotion. IFAC Proc. Vol. 1994, 27, 445–450. [Google Scholar] [CrossRef]
  111. Peri, E.; Ambrosini, E.; Pedrocchi, A.; Ferrigno, G.; Nava, C.; Longoni, V.; Monticone, M.; Ferrante, S. Can FES-Augmented Active Cycling Training Improve Locomotion in Post-Acute Elderly Stroke Patients? Eur. J. Transl. Myol. 2016, 26, 6063. [Google Scholar] [CrossRef] [PubMed]
  112. Duncan, P.W.; Sullivan, K.J.; Behrman, A.L.; Azen, S.P.; Wu, S.S.; Nadeau, S.E.; Dobkin, B.H.; Rose, D.K.; Tilson, J.K.; Cen, S.; et al. Body-Weight–Supported Treadmill Rehabilitation after Stroke. N. Engl. J. Med. 2011, 364, 2026–2036. [Google Scholar] [CrossRef]
  113. Ivey, F.M.; Hafer-Macko, C.E.; Macko, R.F. Exercise Rehabilitation after Stroke. NeuroRX 2006, 3, 439–450. [Google Scholar] [CrossRef]
  114. Iaccarino, M.A.; Bhatnagar, S.; Zafonte, R. Chapter 26—Rehabilitation after Traumatic Brain Injury. In Handbook of Clinical Neurology; Grafman, J., Salazar, A.M., Eds.; Traumatic Brain Injury, Part I; Elsevier: Amsterdam, The Netherlands, 2015; Volume 127, pp. 411–422. [Google Scholar]
  115. Mehrholz, J.; Wagner, K.; Rutte, K.; Meiβner, D.; Pohl, M. Predictive Validity and Responsiveness of the Functional Ambulation Category in Hemiparetic Patients After Stroke. Arch. Phys. Med. Rehabil. 2007, 88, 1314–1319. [Google Scholar] [CrossRef]
  116. Demeurisse, G.; Demol, O.; Robaye, E. Motor Evaluation in Vascular Hemiplegia. Eur. Neurol. 2008, 19, 382–389. [Google Scholar] [CrossRef] [PubMed]
  117. Fugl-Meyer, A.R.; Jääskö, L.; Norlin, V. The Post-Stroke Hemiplegic Patient. Scand. J. Rehabil. Med. 1975, 7, 73–83. [Google Scholar]
  118. Mahoney, F.I.; Barthel, D.W. Functional Evaluation: The Barthel Index: A Simple Index of Independence Useful in Scoring Improvement in the Rehabilitation of the Chronically Ill. Md. State Med. J. 1965, 14, 61–65. [Google Scholar]
  119. Wrisley, D.M.; Marchetti, G.F.; Kuharsky, D.K.; Whitney, S.L. Reliability, Internal Consistency, and Validity of Data Obtained with the Functional Gait Assessment. Phys. Ther. 2004, 84, 906–918. [Google Scholar] [CrossRef]
  120. Berg, K.; Wood-Dauphinee, S.; Williams, J.I. The Balance Scale: Reliability Assessment with Elderly Residents and Patients with an Acute Stroke. Scand. J. Rehabil. Med. 1995, 27, 27–36. [Google Scholar]
  121. Franchignoni, F.; Horak, F.; Godi, M.; Nardone, A.; Giordano, A. Using Psychometric Techniques to Improve the Balance Evaluation System’s Test: The Mini-BESTest. J. Rehabil. Med. Off. J. UEMS Eur. Board Phys. Rehabil. Med. 2010, 42, 323–331. [Google Scholar] [CrossRef]
  122. Klein, P.J.; Fiedler, R.C.; Rose, D.J. Rasch Analysis of the Fullerton Advanced Balance (FAB) Scale. Physiother. Can. 2011, 63, 115–125. [Google Scholar] [CrossRef]
  123. Whitney, S.L.; Hudak, M.T.; Marchetti, G.F. The Dynamic Gait Index Relates to Self-Reported Fall History in Individuals with Vestibular Dysfunction. J. Vestib. Res. 2000, 10, 99–105. [Google Scholar] [CrossRef] [PubMed]
  124. Butland, R.J.; Pang, J.; Gross, E.R.; Woodcock, A.A.; Geddes, D.M. Two-, Six-, and 12-Minute Walking Tests in Respiratory Disease. Br. Med. J. Clin. Res. Ed 1982, 284, 1607–1608. [Google Scholar] [CrossRef]
  125. Peters, D.M.; Fritz, S.L.; Krotish, D.E. Assessing the Reliability and Validity of a Shorter Walk Test Compared With the 10-Meter Walk Test for Measurements of Gait Speed in Healthy, Older Adults. J. Geriatr. Phys. Ther. 2013, 36, 24–30. [Google Scholar] [CrossRef]
  126. Podsiadlo, D.; Richardson, S. The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons. J. Am. Geriatr. Soc. 1991, 39, 142–148. [Google Scholar] [CrossRef] [PubMed]
  127. Tinetti, M.E. Performance-Oriented Assessment of Mobility Problems in Elderly Patients. J. Am. Geriatr. Soc. 1986, 34, 119–126. [Google Scholar] [CrossRef]
  128. Brunnstrom, S. Motor Testing Procedures in Hemiplegia: Based on Sequential Recovery Stages. Phys. Ther. 1966, 46, 357–375. [Google Scholar] [CrossRef] [PubMed]
  129. Collin, C.; Wade, D. Assessing Motor Impairment after Stroke: A Pilot Reliability Study. J. Neurol. Neurosurg. Psychiatry 1990, 53, 576–579. [Google Scholar] [CrossRef]
  130. Verheyden, G.; Nieuwboer, A.; Mertin, J.; Preger, R.; Kiekens, C.; De Weerdt, W. The Trunk Impairment Scale: A New Tool to Measure Motor Impairment of the Trunk after Stroke. Clin. Rehabil. 2004, 18, 326–334. [Google Scholar] [CrossRef]
  131. Palisano, R.; Rosenbaum, P.; Walter, S.; Russell, D.; Wood, E.; Galuppi, B. Development and Reliability of a System to Classify Gross Motor Function in Children with Cerebral Palsy. Dev. Med. Child Neurol. 1997, 39, 214–223. [Google Scholar] [CrossRef]
  132. Meder, K.G.; LoJacono, C.T.; Rhea, C.K. A Systematic Review of Non-Pharmacological Interventions to Improve Gait Asymmetries in Neurological Populations. Symmetry 2022, 14, 281. [Google Scholar] [CrossRef]
  133. Bernstein, N.A. The Co-Ordination and Regulation of Movements, 1st ed.; Pergamon Press: Oxford, NY, USA, 1967. [Google Scholar]
  134. Latash, M.L. There Is No Motor Redundancy in Human Movements. There Is Motor Abundance. Motor Control 2000, 4, 259–261. [Google Scholar] [CrossRef] [PubMed]
  135. Lee, D.D.; Seung, H.S. Learning the Parts of Objects by Non-Negative Matrix Factorization. Nature 1999, 401, 788–791. [Google Scholar] [CrossRef]
  136. Li, Y.; Ngom, A. The Non-Negative Matrix Factorization Toolbox for Biological Data Mining. Source Code Biol. Med. 2013, 8, 10. [Google Scholar] [CrossRef]
  137. Tresch, M.C.; Cheung, V.C.K.; d’Avella, A. Matrix Factorization Algorithms for the Identification of Muscle Synergies: Evaluation on Simulated and Experimental Data Sets. J. Neurophysiol. 2006, 95, 2199–2212. [Google Scholar] [CrossRef] [PubMed]
  138. Olney, S.J.; Richards, C. Hemiparetic Gait Following Stroke. Part I: Characteristics. Gait Posture 1996, 4, 136–148. [Google Scholar] [CrossRef]
  139. Coscia, M.; Monaco, V.; Martelloni, C.; Rossi, B.; Chisari, C.; Micera, S. Muscle Synergies and Spinal Maps Are Sensitive to the Asymmetry Induced by a Unilateral Stroke. J. NeuroEng. Rehabil. 2015, 12, 39. [Google Scholar] [CrossRef] [PubMed]
  140. Bizzi, E.; Cheung, V.C.K.; d’Avella, A.; Saltiel, P.; Tresch, M. Combining Modules for Movement. Brain Res. Rev. 2008, 57, 125–133. [Google Scholar] [CrossRef] [PubMed]
  141. d’Avella, A.; Saltiel, P.; Bizzi, E. Combinations of Muscle Synergies in the Construction of a Natural Motor Behavior. Nat. Neurosci. 2003, 6, 300–308. [Google Scholar] [CrossRef] [PubMed]
  142. Tresch, M.C.; Saltiel, P.; Bizzi, E. The Construction of Movement by the Spinal Cord. Nat. Neurosci. 1999, 2, 162–167. [Google Scholar] [CrossRef]
  143. Cheung, V.C.K.; Turolla, A.; Agostini, M.; Silvoni, S.; Bennis, C.; Kasi, P.; Paganoni, S.; Bonato, P.; Bizzi, E. Muscle Synergy Patterns as Physiological Markers of Motor Cortical Damage. Proc. Natl. Acad. Sci. USA 2012, 109, 14652–14656. [Google Scholar] [CrossRef]
  144. Roh, J.; Rymer, W.Z.; Beer, R.F. Evidence for Altered Upper Extremity Muscle Synergies in Chronic Stroke Survivors with Mild and Moderate Impairment. Front. Hum. Neurosci. 2015, 9, 6. [Google Scholar] [CrossRef] [PubMed]
  145. Steele, K.M.; Rozumalski, A.; Schwartz, M.H. Muscle Synergies and Complexity of Neuromuscular Control during Gait in Cerebral Palsy. Dev. Med. Child Neurol. 2015, 57, 1176–1182. [Google Scholar] [CrossRef] [PubMed]
  146. Tang, L.; Li, F.; Cao, S.; Zhang, X.; Wu, D.; Chen, X. Muscle Synergy Analysis in Children with Cerebral Palsy. J. Neural Eng. 2015, 12, 046017. [Google Scholar] [CrossRef]
  147. Singh, R.E.; Iqbal, K.; White, G.; Hutchinson, T.E. A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool. Appl. Bionics Biomech. 2018, 2018, e3615368. [Google Scholar] [CrossRef] [PubMed]
  148. Zhvansky, D.S.; Sylos-Labini, F.; Dewolf, A.; Cappellini, G.; d’Avella, A.; Lacquaniti, F.; Ivanenko, Y. Evaluation of Spatiotemporal Patterns of the Spinal Muscle Coordination Output during Walking in the Exoskeleton. Sensors 2022, 22, 5708. [Google Scholar] [CrossRef]
Bioengineering 11 00793 g001
Figure 1. PRISMA flowchart for study inclusion/exclusion.
Figure 1. PRISMA flowchart for study inclusion/exclusion.
Bioengineering 11 00793 g001
Bioengineering 11 00793 g002
Figure 2. Percentage of the selected studies in which the activity of a muscle is collected.
Figure 2. Percentage of the selected studies in which the activity of a muscle is collected.
Bioengineering 11 00793 g002
Table 1. Search query used in all systematic searches (PubMed version shown in table).
Table 1. Search query used in all systematic searches (PubMed version shown in table).
Search Query (PubMed, Scopus, WoS)(“gait”[Title/Abstract] OR “walk*”[Title/Abstract])
AND
(“therapy”[Title/Abstract] OR “rehabilit*”[Title/Abstract] OR “neurorehabilit*”[Title/Abstract] OR “training”[Title/Abstract])
AND
(“muscle synerg*”[Title/Abstract] OR “synergies”[Title/Abstract] OR “muscle coordination”[Title/Abstract] OR “motor module*”[Title/Abstract] OR “primitive*”[Title/Abstract])
Table 3. Effect of the rehabilitation therapy on measured clinical scales.
Table 3. Effect of the rehabilitation therapy on measured clinical scales.
Clinical Scales
ImprovedNot ImprovedNot Altered
Allen et al., 2017 [102]UPDRS-III, BBS, FAB, DGI, TUG, 6MWT
Ambrosini et al., 2020 [93]MI, TCT, BBS, FIMM
Conner et al., 2021 [104]
Ferrante et al., 2016 [94]mini best test, Fugl-Meyer motor
Ghislieri et al., 2023 [103]UPDRS-III, FAB MMSE
Jonsdottir et al., 2020 [105]2MWT, 10MWT BBS
Kadone et al., 2020 [106]FIM motor, Barthel, FAC10MWT
Kinugawa et al., 2022 [107]10MWTFMA, BRS
Lim et al., 2021 [95]10MWT BBS
Routson et al., 2013 [96]FMA DGI
Srivastava et al., 2016 [97]FMA, FGA, 6MWT, TUG
Tan et al., 2020 [99]FIM motor, FIM locomotion, FMA lower ex
Tan et al., 2018 [98]FIM motor, FIM locomotion, FMA lower ex
Van Criekinge et al., 2021 [100]FAC, TIS, POMA Tinetti, Barthel
Zhu et al., 2021 [101]10MWT, 6MWTTUG
Table 4. Type of rehabilitation therapy (R = robotic based, F = FES-based, O = other techniques) and its effect of the rehabilitation therapy on muscle synergies characteristics (+: improvement post-rehabilitation, −: no improvement post-rehabilitation, n/a: comparison not performed).
Table 4. Type of rehabilitation therapy (R = robotic based, F = FES-based, O = other techniques) and its effect of the rehabilitation therapy on muscle synergies characteristics (+: improvement post-rehabilitation, −: no improvement post-rehabilitation, n/a: comparison not performed).
Type of Therapy Number of
Synergies 		Spatial Synergies Temporal Activations Coordination Symmetry
Allen et al., 2017 [102]O−+n/an/a
Ambrosini et al., 2020 [93]F−++−
Conner et al., 2021 [104]R+n/an/an/a
Ferrante et al., 2016 [94]F+n/an/an/a
Ghislieri et al., 2023 [103]O+++n/a
Jonsdottir et al., 2020 [105]O−−+n/a
Kadone et al., 2020 [106]R−n/an/an/a
Kinugawa et al., 2022 [107]F+n/an/an/a
Lim et al., 2021 [95]Fn/a++n/a
Routson et al., 2013 [96]O+++n/a
Srivastava et al., 2016 [97]Rn/a+−n/a
Tan et al., 2020 [99]R+n/an/a+
Tan et al., 2018 [98]R−n/an/a+
Van Criekinge et al., 2021 [100]O−+n/an/a
Zhu et al., 2021 [101]R−−+n/a
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Borzelli, D.; De Marchis, C.; Quercia, A.; De Pasquale, P.; Casile, A.; Quartarone, A.; Calabrò, R.S.; d’Avella, A. Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective. Bioengineering 2024, 11, 793. https://doi.org/10.3390/bioengineering11080793

AMA Style

Borzelli D, De Marchis C, Quercia A, De Pasquale P, Casile A, Quartarone A, Calabrò RS, d’Avella A. Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective. Bioengineering. 2024; 11(8):793. https://doi.org/10.3390/bioengineering11080793

Chicago/Turabian Style

Borzelli, Daniele, Cristiano De Marchis, Angelica Quercia, Paolo De Pasquale, Antonino Casile, Angelo Quartarone, Rocco Salvatore Calabrò, and Andrea d’Avella. 2024. "Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective" Bioengineering 11, no. 8: 793. https://doi.org/10.3390/bioengineering11080793

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop