The search strategy was applied in PubMed/MEDLINE, Web of Science, Scopus, CINAHL, and SPORTDiscus using a combination of Medical Subject Headings terms, keywords, and Boolean operators. Specific search terms and combinations can be found in Table 1. In addition, the reference lists of eligible articles were manually searched in Google Scholar, and experts in the field were consulted to identify studies that were not found with the search strategy.

The PECO framework was used as inclusion criteria (Populations, Exposures, Comparators and Outcomes) (Morgan et al., 2018; Dekkers et al., 2019): (i) populations: any human subject; (ii) exposures: any musculoskeletal chronic condition associated to clinical pain or experimentally induced pain; (iii) comparators: a non-exposed reference population, which includes healthy individuals with no history of musculoskeletal disease or dysfunction in the last 6 months, or healthy individuals serving as their own control when comparing results before (baseline) and after exposure to experimental pain (e.g., hypertonic saline injection); (iv) outcomes: spatial distribution of muscle activity, defined as the relative localization sEMG amplitude across a muscle (Gallina et al., 2022). This distribution can be assessed either by comparing the amplitude of the sEMG signal—such as root mean square (RMS) or average rectified value (ARV)—in specific regions, or by analyzing changes across multiple sEMG electrodes (i.e., HD-sEMG arrays). The latter approach involves computing displacements in the center of activity (also referred to as barycenter, centroid, center of mass, or locus) to summarize spatial shifts in muscle activity (Gallina et al., 2022). Cross-sectional studies of peer-reviewed articles written in English or Spanish, published from inception to June 6, 2025, were included. Exclusion criteria for this study were: (i) research on neurological disease; (ii) all editorials, letters, reviews, and meta-analyses.

Two independent reviewers (IO-C and JM-V) used Rayyan web software (http://rayyan.qcri.org) to analyze the results (Ouzzani et al., 2016). After removing duplicates, studies were selected by title and abstract. Those potentially eligible studies were read in full text, and the inclusion and exclusion criteria were applied. In case of disagreement during any of the phases, a third author was consulted to resolve (GM-R).

A standardized table was used for data collection. Two independent reviewers (IO-C and JM-V) extracted data from the studies. In case of disagreement, a third reviewer (GM-R) resolved the disagreement. Data collected for each study included: author, muscle group and electrode location, signal derivation and electrode specifications, sample size, sex, sEMG outcomes, task, pain intensity, and spatial distribution results. For studies with missing data, we attempted to contact the authors 3 times, by email.

The Newcastle-Ottawa Scale (NOS), adapted for this study, was utilized to assess the methodological quality of cross-sectional studies (Modesti et al., 2016). Previous systematic reviews on observational studies involving sEMG have employed this scale (Mendez-Rebolledo et al., 2021b). The adapted NOS comprises seven items, focusing on sample selection, comparability, and outcome. Each subitem is rated from 0 to 2 stars, with a maximum total score of 9. The comparability item examines the control of potential confounding factors. A single star is awarded when the study considered confounders related to the presence of clinical or experimental pain and conducted subgroup analyses accordingly (e.g., by pain type, pain location, or interaction with contraction type or movement phase). Methodological quality was classified using established thresholds from prior systematic reviews (Modesti et al., 2016; Mendez-Rebolledo et al., 2021b, 2022): studies scoring 0–4 stars were rated as low quality, 5–7 as moderate quality, and 8–9 as high quality. Discrepancies in scores will be resolved through consensus, and the agreed-upon rating will be assigned to each study. The reviewers must achieve substantial agreement (kappa coefficient ≥ 0.80) in the final classification of the studies. Additionally, an adaptation of the Consensus for Experimental Design in Electromyography (CEDE) checklist was implemented to assess the methodological quality and reporting transparency of the sEMG procedures employed in the included studies (Besomi et al., 2024). This checklist originally contained 40 items, divided into two sections. For this study, only the ‘Procedure for sEMG Recording' section was considered. Items that were not applicable due to study design or the type of electromyography (i.e., needle or wireless) were excluded, resulting in a modified 20-point checklist.

The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to grade the certainty of the evidence for each outcome (Guyatt et al., 2008). Two reviewers (J S-M and G M-R) used GRADEpro (https://gradepro.org) to produce a summary table of results. The certainty of the evidence was determined in two stages. In the first, it was considered to reduce the certainty according to the following criteria: (i) limitation of the included studies: decrease one level if 25% or more of the included articles had a high risk of bias evaluated with NOS; (ii) inconsistency: decrease one level if there was high heterogeneity (I² ≥ 75%); (iii) indirectness: down one level if there were differences between participants, interventions, outcome measures or indirect comparisons; (iv) imprecision: a markdown level was considered if there was a wide confidence interval, crosses the line of no effect, and small sample size (n < 300); (v) risk of publication bias: decrease one level if there was asymmetry in the funnel plot.

ReviewManager version 5.3 (The Nordic Cochrane Center, The Cochrane Collaboration) was used for statistical analysis. The standardized mean difference (SMD) (Anzures-Cabrera et al., 2011) and 95% confidence intervals (CIs) were calculated to estimate the differences in regional activity (e.g., center of activity) between patients with clinical pain (musculoskeletal injuries), experimental pain, and a combination of both, compared to healthy controls. When the standard deviation (SD) was not reported by the studies, standard formulas were used to derive it based on the standard error (SE), the 95% CI, or the p-value of a t-test (Deeks et al., 2021). Studies were pooled using a random-effects model with the DerSimonian and Laird method, as heterogeneity in true effect sizes was assumed between included studies (Borenstein et al., 2010). An SMD of 0.0 to 0.2 represented a trivial effect, 0.2 to 0.6 a small effect, 0.6 to 1.2 a moderate effect, 1.2 to 2.0 a large effect, 2.0–4.0 a very large effect, and 4.0 an extremely large effect (Hopkins et al., 2009). Heterogeneity was assessed using the I² statistic, considering values of < 25% as low, 25%−75% as moderate, and >75% as high heterogeneity (Higgins, 2003). In addition, if there was a high level of heterogeneity (i.e. I² > 75%), a sensitivity analysis was applied to remove one study at a time to determine the impact on the heterogeneity of the results (Higgins, 2003).

The results of the search are reported in Figure 1. In total, 1,214 articles were identified from databases. After the removal of duplicates (n = 147), 1,067 articles were screened by title and abstract, excluding 1,040 articles. The remaining 27 articles were included in the review process. Eleven articles were excluded due to outcomes not aligned with the review's focus on the spatial distribution of muscle activity (Supplementary Table S1). The reasons for exclusion were as follows: the absence of center of activity or comparable spatial analyses (n = 5) (Finneran et al., 2003; Yong et al., 2004; Gaudreault et al., 2005; Sung et al., 2005; Gallina et al., 2018a), use of non-HD-sEMG systems or the absence of a multichannel electrode configuration capable of spatially sampling muscle activity (n = 3) (Pirouzi et al., 2006; Schabrun et al., 2017; Claus et al., 2018), non-eligible populations (n = 2) (Kubo et al., 2019; Abboud et al., 2021), and studies applying therapeutic interventions that may have influenced the spatial distribution of muscle activity (n = 1) (Mendez-Rebolledo et al., 2025). Additionally, four articles were identified from reference citations, including a total of twenty articles in this review (Figure 1), of which fifteen were included in the meta-analysis based on available quantitative data on center of activity displacement.

The characteristics of the 20 studies included in this review are summarized in Tables 2, 3. The sample was made up of 562 participants [231 control (healthy); 266 clinical pain; 65 experimental pain], including 245 females and 317 males. The reported age ranged from 17.8 to 46.6 years, with a pooled mean age of 30.5 ± 6.7 years. Four musculoskeletal dysfunctions associated with clinical pain were identified in the included studies: CLBP (n = 12) (Abboud et al., 2014; Falla et al., 2014; Martinez-Valdes et al., 2019; Sanderson et al., 2019a,b, 2024; Hao et al., 2020; Arvanitidis et al., 2021, 2022, 2023; Serafino et al., 2021; Sampieri et al., 2025), patellofemoral pain syndrome (n = 1) (Gallina et al., 2019), and chronic ankle instability (n = 1) (Mendez-Rebolledo et al., 2023a), and lumbar myofascial trigger point (n = 1) (Li et al., 2024). Two experimental pain conditions were identified: upper trapezius pain by hypertonic saline injection (n = 4) (Madeleine et al., 2006; Dideriksen et al., 2016; Falla et al., 2017; Ducas et al., 2024) and erector spinae pain by nociceptive electrical stimulation (n = 1) (Ducas et al., 2024). Neuromuscular activity was measured across different tasks and differentiated by the type of muscle contraction involved. This allowed the same study to provide information on both the concentric and eccentric phases when dynamic tasks were performed. Neuromuscular activity was measured during isometric (n = 11) (Madeleine et al., 2006; Abboud et al., 2014; Dideriksen et al., 2016; Gallina et al., 2018b; Sanderson et al., 2019b; Hao et al., 2020; Arvanitidis et al., 2022; Mendez-Rebolledo et al., 2023a; Ducas et al., 2024; Li et al., 2024; Sampieri et al., 2025), concentric (n = 9) (Falla et al., 2014, 2017; Gallina et al., 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a, 2024; Arvanitidis et al., 2021, 2023; Serafino et al., 2021), and eccentric (n = 8) (Falla et al., 2014, 2017; Gallina et al., 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a, 2024; Serafino et al., 2021; Arvanitidis et al., 2023) tasks.

Regarding the spatial distribution of muscle activity, it was primarily characterized by the displacement of the center of activity (barycenter, center of mass, centroid, or locus) along the cephalocaudal axis (Y-axis) (n = 16) (Madeleine et al., 2006; Falla et al., 2014, 2017; Dideriksen et al., 2016; Gallina et al., 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a,b, 2024; Hao et al., 2020; Arvanitidis et al., 2021, 2023; Mendez-Rebolledo et al., 2023a; Ducas et al., 2024; Li et al., 2024; Sampieri et al., 2025). In addition to center-of-activity analyses, several studies included other spatial or signal-based sEMG outcomes. Two studies assessed RMS dispersion (Abboud et al., 2014; Hao et al., 2020), one study performed coherence analysis (Arvanitidis et al., 2022), and one study used principal component analysis (Gallina et al., 2019). Additionally, several studies reported amplitude-based outcomes, including RMS (n = 12) (Falla et al., 2014, 2017; Martinez-Valdes et al., 2019; Sanderson et al., 2019a,b, 2024; Arvanitidis et al., 2021, 2022; Mendez-Rebolledo et al., 2023a; Ducas et al., 2024; Li et al., 2024; Sampieri et al., 2025), microvolts (μV) (n = 1) (Serafino et al., 2021), and ARV (Gallina et al., 2018b). The pain intensity was reported in eighteen articles using the visual analog scale (n = 3) (Abboud et al., 2014; Hao et al., 2020; Arvanitidis et al., 2021), the numerical pain rating scale (n = 14) (Madeleine et al., 2006; Falla et al., 2014, 2017; Dideriksen et al., 2016; Gallina et al., 2018b, 2019; Sanderson et al., 2019a,b, 2024; Serafino et al., 2021; Arvanitidis et al., 2022, 2023; Mendez-Rebolledo et al., 2023a; Ducas et al., 2024), and the chronic pain grade questionnaire (n = 1) (Sampieri et al., 2025). Only two article did not report pain data (Martinez-Valdes et al., 2019; Li et al., 2024). The average pain intensity varied from 1.8–4.43 (numerical pain rating scale, NPRS) in CLBP subjects and from 4.3–5.5 (NPRS) in upper trapezius experimental pain. Control subjects reported a pain value of 0.

The evaluation of methodological quality with the adapted Newcastle-Ottawa Scale for cross-sectional studies is shown in Table 4. Three studies presented moderate methodological quality with a total score of 5 stars (Gallina et al., 2019; Mendez-Rebolledo et al., 2023a; Sanderson et al., 2024). The remaining 17 studies presented low methodological quality. All studies included a selected demographic group of participants and only five of them performed a sample size calculation (Arvanitidis et al., 2021, 2022, 2023; Mendez-Rebolledo et al., 2023a; Ducas et al., 2024). Additionally, no study provided information about the response rate of the participants. Ten studies obtained two stars in the item ascertainment of the exposure due to the application of clinical evaluations or validated tools to determine the presence of the clinical pain in the sample (Abboud et al., 2014; Gallina et al., 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a,b, 2024; Hao et al., 2020; Serafino et al., 2021; Mendez-Rebolledo et al., 2023a; Sampieri et al., 2025). Regarding the comparability criterion, four studies received a star for including an analysis that addressed potential confounders, such as joint position (Gallina et al., 2018b; Ducas et al., 2024; Sanderson et al., 2024), or for performing multivariate analysis (Gallina et al., 2019). On the other hand, in the outcome items, 15 studies obtained one star in the assessment since they identified the presence of musculoskeletal disorders through a self-reported tool (Abboud et al., 2014; Falla et al., 2014; Gallina et al., 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a,b, 2024; Hao et al., 2020; Arvanitidis et al., 2021, 2022, 2023; Serafino et al., 2021; Mendez-Rebolledo et al., 2023a; Li et al., 2024; Sampieri et al., 2025), and four of them were categorized as not applicable because they correspond to studies of experimental pain and not to a diagnosis of clinical pain (Madeleine et al., 2006; Dideriksen et al., 2016; Falla et al., 2017; Ducas et al., 2024). In addition, all studies obtained a star in the statistical analysis item.

The results of the critical evaluation of studies using sEMG, based on the CEDE checklist (Besomi et al., 2024), are presented in Table 5. Related to the electrode placement section, all of the included studies reported electrode type, muscles evaluated and specified the location of electrodes. Except for three articles (Sanderson et al., 2019a; Hao et al., 2020; Li et al., 2024), all included studies reported the skin preparation procedure, as well as the use and location of the reference electrode. Regarding the electrode characteristics items, all included studies reported the physical configuration of the electrode system, including the type, number, size, and inter-electrode distance, as well as the spatial arrangement of the grids (i.e., 5 × 13, 8 × 8, etc.). Within the items of sEMG signal and its preprocessing section, six studies did not report the signal detection mode (Abboud et al., 2014; Falla et al., 2014, 2017; Dideriksen et al., 2016; Hao et al., 2020; Li et al., 2024). Two of the included studies did not specify the brand and model of the sEMG acquisition system (Hao et al., 2020; Serafino et al., 2021). All included studies specified the gain of amplifier and cut-off frequencies, with the sampling frequency of the sEMG system. All studies reported analog-to-digital resolution and full-scale input range, except for one (Li et al., 2024). Of the total number of studies included, only four did not report the software used for processing the sEMG signal (Martinez-Valdes et al., 2019; Hao et al., 2020; Serafino et al., 2021; Li et al., 2024). Four studies did not report techniques applied for power line interference removal (Hao et al., 2020; Mendez-Rebolledo et al., 2023a; Sanderson et al., 2024; Sampieri et al., 2025). Finally, nine studies used other devices and reported the synchronization with the sEMG system (Gallina et al., 2018b, 2019; Martinez-Valdes et al., 2019; Sanderson et al., 2019a, 2024; Arvanitidis et al., 2021, 2022, 2023; Serafino et al., 2021; Mendez-Rebolledo et al., 2023a; Sampieri et al., 2025). Considering that the design of nine studies did not extract other data at the same time as the sEMG data (Madeleine et al., 2006; Abboud et al., 2014; Falla et al., 2014, 2017; Dideriksen et al., 2016; Sanderson et al., 2019b,a; Hao et al., 2020; Ducas et al., 2024), it was considered that the item of synchronization with other devices did not apply to them.

The results of the analyses, including both clinical and experimental pain, indicate a very low certainty of evidence, downgraded due to inconsistency, indirectness, and publication bias, with a moderate effect size. For clinical pain alone, the evidence was similarly downgraded for the same reasons, with a small effect size, as shown in Supplementary Table S2. The results of the analyses including CLBP show very low certainty of evidence and suggest that higher-quality studies are needed to strengthen it, despite the observation of a small effect size.

The alterations in regional muscle activation observed in clinical pain models, as highlighted in the findings of this meta-analysis, may be attributed to several neuromuscular mechanisms. The results suggest a redistribution of muscle activity in individuals with CLBP compared to healthy controls, as indicated by differences in the location of the center of activity. However, this finding was not robust in the sensitivity analysis, highlighting the need for additional studies to validate this effect. A key concept underlying the interpretation of spatial shifts in muscle activation is the phenomenon of “non-uniform motor unit recruitment,” which proposes that pain induces a reorganization of activation patterns within a muscle (Hodges and Tucker, 2011; Hodges and Smeets, 2015; Hodges et al., 2021; Hug et al., 2025). Instead of a uniform reduction in activity across the muscle, some motor units may be inhibited while others are facilitated, resulting in altered spatial distribution of activity (Hao et al., 2020; Arvanitidis et al., 2021). For instance, Hug et al. (2025) demonstrated that during experimental muscle pain, inhibitory inputs are not homogeneously distributed among motor units within the same muscle. By analyzing intrasubject variability, they found that some motor units exhibited significant decreases in discharge rate while others remained unchanged or slightly increased, indicating a non-uniform, task-dependent modulation of motor output. This heterogeneity may reflect an adaptive strategy by the nervous system to redistribute load away from sensitized regions while maintaining overall functional performance (Hug et al., 2025). In addition, previous work has proposed that the effective neural drive to the muscle is primarily governed by the common synaptic input received by the motoneuron pool (Farina and Negro, 2015). More recent findings suggest that biomechanical properties of the muscle, such as twitch duration, can influence how these common inputs are transmitted and expressed, implying that spatial shifts in muscle activity may result from both neural and biomechanical factors that shape how motor commands are distributed across the muscle (Cabral et al., 2024). While this review did not examine within-task temporal variation, the observed between-group differences in the location of the center of activity suggest a stable, pain-related reorganization that manifests during a given motor task. This redistribution could serve an adaptive role, potentially minimizing local tissue stress, redistributing load across muscle regions, or compensating for regional fatigue vulnerability in chronic pain populations (Hodges and Tucker, 2011; Hodges and Smeets, 2015; Hodges et al., 2021). However, whether such changes represent protective strategies or maladaptive compensations remains unclear, and further investigation is needed to explore the physiological mechanisms and functional implications of within-task spatial shifts in chronic pain conditions (Hodges and Tucker, 2011; Abboud et al., 2021).

Experimental studies have shown that nociceptive input can reduce motor unit discharge rates while recruiting additional units to maintain force output (Tucker and Hodges, 2009; Martinez-Valdes et al., 2021). While such evidence is based on pre- vs. post-pain comparisons in controlled settings, how these changes translate to within-task recruitment strategies in individuals with chronic pain remains to be fully elucidated. This recruitment strategy may result in a redistribution of muscle activity either within the same muscle or between synergistic muscles, potentially to unload painful regions or optimize force production under altered conditions (Gallina et al., 2018b; Nuccio et al., 2021). In axial muscles, such as the erector spinae, this redistribution may occur without necessarily altering the global force vector but rather reflect spatial shifts in neural drive across portions of large, multifunctional muscle groups (Abboud et al., 2020). In contrast, in peripheral muscles such as the vasti, changes in recruitment may also influence the direction or orientation of the force vector produced by the muscle (Gallina et al., 2018a,b). Similar adaptations have been reported in chronic musculoskeletal disorders such as CLBP, chronic ankle instability, and patellofemoral pain syndrome. Although not all studies used center of activity metrics, changes in muscle activation patterns, based on signal amplitude or spatial distribution, have been interpreted as evidence of intra- or intermuscular redistribution in response to pain or instability (Gallina et al., 2018b; Arvanitidis et al., 2021; Mendez-Rebolledo et al., 2023a, 2025).

Our meta-analysis revealed a significant redistribution of erector spinae muscle activity toward cranial regions in individuals with CLBP, as indicated by a marked difference in the center of activity location compared to control groups. During isometric and dynamic tasks, the center of activity in people with CLBP tends to shift toward the upper part of the lumbar spine (Sanderson et al., 2019b; Hao et al., 2020; Arvanitidis et al., 2021). This cranial shift may reflect a strategy to adopt a more favorable position for posture control and spinal stabilization, suggesting an effort by the nervous system to shift the load away from potentially affected regions of the lower back. Additionally, previous research has proposed a redistribution of muscle activity from deep to superficial layers of the erector spinae as a strategy to reduce load on injured structures, albeit at the expense of reduced efficiency in spinal stabilization. However, given that HD-sEMG primarily captures superficial muscle activity, such deep-to-superficial shifts are unlikely to be directly reflected in the center of activity measure (Van Dieën et al., 2019; Abboud et al., 2021). This change may be counterproductive in the long term, as excessive activation of superficial and cranial muscle parts can lead to fatigue, deterioration of force steadiness, and alteration of postural control, potentially further aggravating the CLBP condition (Hodges and Tucker, 2011; Arvanitidis et al., 2022). The reorganization of motor recruitment in this case could not only reduce pain but also alter movement dynamics, affecting posture and global motor control (Serafino et al., 2021).

The nature of the motor task also plays a crucial role in these alterations. Tasks that require dynamic movements, different contraction speeds, or prolonged static postures (e.g., isometric contraction) can exacerbate or reveal different activation patterns due to varying demands on the musculoskeletal system (Martinez-Valdes et al., 2021; Arvanitidis et al., 2023; Cruz-Montecinos et al., 2025). For example, in dynamic tasks such as rowing, a caudal shift in the activity of the erector spinae has been reported (Martinez-Valdes et al., 2019). In isometric resistance tasks, fatigue may induce a shift of the center of activity toward more cranial regions, potentially reflecting a strategy by the nervous system to redistribute activation and delay fatigue in areas affected by pain (Hao et al., 2020; Abboud et al., 2021). Additionally, pain induces changes in motor performance, motor unit recruitment, and rate coding behavior that vary across different contraction speeds (Martinez-Valdes et al., 2021). Notably, at higher contraction speeds, the inhibitory effect of pain on lower-threshold motor units is compensated by increased recruitment of higher-threshold motor units, allowing fast submaximal contractions to be maintained. Conversely, at slower speeds, pain reduces motor unit discharge rates and prolongs the neuromechanical delay, which could increase the risk of overload in other muscle regions or adjacent muscles, potentially leading to exacerbation of pain or new injuries.

Although a secondary analysis specifically on experimental pain and its implications for regional muscle activation was not possible, the systematic review of the evidence revealed some key observations. The data suggest that in certain muscle groups, particularly the upper trapezius, there is a caudal shift of the center of activity following pain application by hypertonic saline injection (Madeleine et al., 2006; Falla et al., 2017). However, this redistribution of muscle activity was not observed across all muscle groups. For instance, a caudal shift in the center of activity of the erector spinae muscles was reported in only one study (Dideriksen et al., 2016), and no redistribution was observed in the vastus medialis and lateralis muscles (Gallina et al., 2018b). This suggests that the response may be muscle-specific and influenced by factors such as the nature of the task and the intensity of the painful stimulus (Ducas et al., 2024).

The difference in the shift of the center of activity between experimental and clinical pain models may be attributed to the nature and duration of the painful stimulus. Experimental pain, typically induced acutely by hypertonic saline injections, produces a strong and immediate pain response (Izumi et al., 2014; Christensen et al., 2022; Graven-Nielsen, 2022). In the erector spinae, this acute stimulus may elicit a protective neuromuscular response that shifts muscle activation away from the localized painful region. This has been associated with a caudal shift in activation within the muscle, interpreted as an effort to redistribute loading while maintaining spinal stability. This response could serve as a short-term strategy to minimize discomfort and prevent further irritation during sustained or intense contractions. In contrast, clinical pain, which is often chronic and persistent, likely induces distinct neuromuscular adaptations over time. In conditions such as CLBP, evidence suggests a cranial shift in the activation of the erector spinae muscles. This shift may represent a compensatory mechanism in response to the overload and fatigue that the erector spinae muscles initially endured during the onset of this condition. Holtermann et al. (2011) supports this idea by showing that pain intensity is closely related to the inability to evenly distribute muscle activity in the upper trapezius. The high intensity of pain induced by hypertonic saline in experimental models may contribute to the observed caudal shift in muscle activity in this group.

This review suggests that individuals with clinical and experimental pain exhibit altered spatial distribution of muscle activity, reflecting potential maladaptive neuromuscular responses to chronic pain. While the clinical implications of these spatial shifts require further investigation, current evidence supports the integration of HD-sEMG as both an assessment and interventional tool in neuromuscular rehabilitation. These findings underscore the need for targeted rehabilitation strategies to promote more effective motor control. Unlike conventional bipolar EMG, HD-sEMG provides a detailed topography of muscle activity, enabling clinicians to detect regional imbalances and monitor neuromuscular adaptations with high spatial precision. This capability is particularly relevant in conditions such as chronic ankle instability, patellofemoral pain, and chronic low back pain, where alterations in motor unit recruitment contribute to recurrent symptoms and functional impairments. Recent studies have demonstrated the utility of HD-sEMG-based biofeedback for retraining the spatial distribution of muscle activity. For instance, Mendez-Rebolledo et al. (2025) used HD-sEMG maps to provide real-time feedback to individuals with CAI, promoting the activation of the under-recruited posterior region of the fibularis longus and restoring a more physiological distribution pattern (Mendez-Rebolledo et al., 2025). Similarly, Arvanitidis et al. (2019) showed that healthy subjects could volitionally modulate the barycenter of trapezius activation using spatial feedback, maintaining a caudal shift in the spatial distribution of muscle activity even under fatigue, highlighting its robustness and applicability during sustained contractions (Arvanitidis et al., 2019). Extending this paradigm, Gazzoni and Cerone (2021) introduced an augmented reality system that projects HD-sEMG-based activity maps directly onto the skin surface via smart-glasses or mobile devices. This immersive visualization allows both patients and clinicians to monitor and adjust muscle activation in real time, improving motor learning through embodied feedback (Gazzoni and Cerone, 2021). In proof-of-concept applications involving lumbar and fibular muscles, this approach revealed asymmetric or maladaptive patterns that were not visible with traditional displays. Collectively, these findings underscore HD-sEMG's potential to guide personalized rehabilitation strategies, enhance patient engagement through intuitive feedback, and objectively quantify progress. Future research should expand beyond observational studies and integrate HD-sEMG with complementary methodologies, such as motor unit decomposition and elastography, to better elucidate the underlying neuromechanical mechanisms. In parallel, testing these approaches across a broader range of clinical conditions (e.g., rotator cuff disorders, cervical pain syndromes, postoperative recovery) and functional contexts (e.g., gait, dual-task balance, or fatigue-inducing tasks) may inform the development of targeted interventions addressing both spatial activation deficits and their functional consequences.