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Effects of Muscle Synergy during Overhead Work with a Passive Shoulder Exoskeleton: A Case Study

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Overhead exoskeleton keeps two-synergy plan, cuts muscle effort

desk verdict A plausible first case study that shoulder exoskeletons reduce activation of the primary muscle synergy, but the 'induces new synergy' claim rests on a fragile and partly circular synergy-sorting step. read the letter →

arxiv 2411.15504 v1 pith:VWPPW43N submitted 2024-11-23 physics.med-ph cs.RO

Jin Tian , Baichun Wei , Chifu Yang , Suo Luo , Jiadong Feng , Ping Li , Changbing Chen , Yingjie Liu
show 2 more authors
Haiqi Zhu Chunzhi Yi
This is my paper · ORCID
classification physics.med-phcs.RO
keywords musclesynergypassiveshoulderexoskeletonoverheadworknon-negativematrixfactorizationEMGtopographicmapsurfaceelectromyographyscrewingtaskmotorcoordination
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This case study asks whether a passive shoulder exoskeleton changes how the nervous system coordinates muscles during overhead work. Eight healthy men performed the same overhead screwing task with and without the HIT-POSE exoskeleton while eight shoulder and trunk muscles were recorded by surface EMG. The paper argues that the exoskeleton leaves the basic organization of movement intact: the number of muscle synergies stays at two and the dominant shoulder-flexion synergy (anterior and middle deltoid, $r=0.94$) is unchanged. What changes is the secondary synergy, whose leading muscle shifts from pectoralis major to middle deltoid, and the amount of neural drive, since the first synergy's activation profile, average recruitment, and activation duration all decrease significantly. The EMG topographic maps add that the exoskeleton lowers overall muscle activation and makes the spatial distribution more uniform, which the authors read as a sign of reduced effort without added motor complexity.

What carries the argument

The analysis rests on non-negative matrix factorization (NMF) of eight-channel surface EMG envelopes, written as $E=W\times H$, where $W$ holds each muscle's weight in each synergy and $H$ holds each synergy's activation over time. The number of synergies is chosen by variance accounted for: the smallest $s$ with total VAF above 90% and each muscle's VAF above 75%. Because NMF output ordering is arbitrary, the paper aligns synergies across subjects and conditions with k-means clustering followed by manual labeling based on anterior/middle deltoid dominance, naming the AD/MD module the first synergy. A second tool, the EMG topographic map, stacks averaged RMS features across the eight channels and over time, and quantifies the resulting image by mean value, center-of-gravity coordinates, and Shannon entropy; these indices capture the spatial uniformity and timing of muscle activation. The regression-based similarity measures ($R$, $S_s$, $\sigma_{S_s}$) are used to test whether assisted-condition synergies arise by merging or fractionating the normal-condition synergies.

What would settle it

Re-run the same overhead screwing protocol with a stricter or alternative synergy-selection rule, for example requiring total VAF above 95% or per-muscle VAF above 90%, and with synergies aligned by similarity rather than by AD/MD dominance. If the optimal count becomes three in either condition, or if the first-synergy correlation between conditions drops below the values reported, the paper's central claim of unchanged synergy number and preserved primary synergy would be falsified. A second decisive check would be a sham-exoskeleton condition with no assistive torque: if the second-synergy shift from PM to MD still appears, the assistance itself is not what induces the new synergy.

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Extended reading notes

Core claim

The paper's central claim is that wearing the HIT-POSE passive shoulder exoskeleton during overhead screwing does not change the number or the primary structure of muscle synergies, but it does change the secondary synergy and the magnitude of neural activation. With non-negative matrix factorization, two synergies explained the eight-channel EMG in both conditions under the variance-accounted-for criterion. The first synergy, dominated by anterior and middle deltoid, was essentially identical across conditions ($r=0.94$); its activation profile, average recruitment level, and activation duration fell significantly in the assisted condition ($p<0.05$). The second synergy was not the same: its highest-weight muscle changed from pectoralis major to middle deltoid and the two were weakly negatively correlated ($r=-0.45$), which the authors interpret as the exoskeleton inducing a new synergy rather than merely scaling the old one. In the EMG topographic maps, the mean value dropped ($p<0.001$) and entropy rose ($p<0.01$), while the center of gravity did not shift, supporting the conclusion that the exoskeleton reduces and homogenizes muscle activation without changing which muscle groups dominate or when they are active.

Load-bearing premise

The conclusions depend on the procedural choices of how many synergies to count (VAF above 90% globally and 75% per muscle) and how to label which synergy is which (k-means clustering plus manual AD/MD dominance); if those choices were different, the claims that the count stays at two and the first synergy is identical could change.

Editorial extensions

If this is right

  • For overhead screwing tasks, wearing the HIT-POSE exoskeleton does not add a new muscle-synergy module, so the motor system appears able to perform the task with the same coordination complexity as without assistance.
  • The primary shoulder-flexion synergy (AD and MD) is preserved under assistance, indicating that the dominant neural command for the task is not rewritten by the exoskeleton.
  • The change of the second synergy from a pectoralis-major-dominant to a middle-deltoid-dominant pattern implies that assistance can shift the stabilizing role among muscles, possibly creating a new synergy without increasing the synergy count.
  • Significant reductions in the first synergy's activation profile, average recruitment level, and activation duration suggest that the exoskeleton lowers the neural drive to shoulder agonist muscles and may delay fatigue during overhead work.
  • The topographic-map results (lower mean, higher entropy, unchanged center of gravity) indicate that the exoskeleton reduces the overall level of monitored muscle activation and spreads it more uniformly while preserving the timing and dominant muscle groups of the task.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct test of the 'new synergy' interpretation would be to record the same screwing task with a passive exoskeleton that applies no torque; if the second-synergy shift from PM to MD persists in a sham condition, it may stem from the device's physical interface rather than from assistance itself.
  • The entropy increase may partly reflect the normalization step, which divides all RMS values by the global maximum across conditions; re-running the topographic analysis on per-condition normalization or on raw scaled amplitudes would clarify whether the exoskeleton truly homogenizes activation or simply reduces its overall amplitude.
  • Because the study enrolled only eight healthy right-handed men, the key invariance claims (same synergy count, preserved first synergy) should be treated as hypotheses about the general worker population; extending the protocol to women, left-handed workers, and fatigued states would show whether the motor adaptation generalizes.
  • If the synergy-count invariance holds for other overhead tasks, it would suggest that passive shoulder exoskeletons act by scaling and rerouting existing motor modules rather than by recruiting new ones, giving device designers a concrete target: minimize unintended shifts in non-primary synergies through interface and torque-profile design.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The manuscript presents a case study (n=8 healthy male participants) on the effects of a passive shoulder exoskeleton (HIT-POSE) on muscle synergies during a simulated overhead screwing task. Using non-negative matrix factorization (NMF) and EMG topographic maps, the authors report that the exoskeleton does not change the number of synergies (n = 2), leaves the first synergy (AD/MD) unchanged, changes the second synergy (from PM to MD), significantly reduces activation of the first synergy, and increases the entropy of the EMG topographic map. The paper is framed as a first investigation of shoulder-exoskeleton effects on muscle synergy during overhead work.

Significance. If the central conclusions were robust, this would be a useful contribution to exoskeleton assessment, being among the first to combine NMF-based synergy analysis and EMG topographic entropy for a shoulder exoskeleton in an occupational task. The decrease in first-synergy activation is supported by several metrics (activation profile, Recr, Ad) with p-values below 0.05, and the topographic mean and entropy differences are highly significant (p < 0.001 and p < 0.01, respectively). The authors also explicitly enumerate limitations (small sample, case-study scope, limited neurophysiological correlation). However, the main preservation/induction claims about synergies are weakened by the analysis pipeline, as described in the major comments.

major comments (3)
  1. [II.D.1 and III.A.2] The claim that Normal S1 and Intervention S1 are identical (r = 0.94) is not supported as independent evidence because the synergy identification step pools all W columns from both conditions and clusters them jointly with k-means. Since the clustering is performed on both conditions simultaneously, Normal S1 and Intervention S1 are placed in the same cluster by construction, making the subsequent correlation between them partly an artifact of the assignment rule. To test preservation of the primary synergy, the synergies should be identified independently within each condition (or matched after independent clustering) and then compared, reporting the distribution of similarity across subjects. As it stands, the headline claim that the exoskeleton 'does not alter existing major synergies' rests on a circular step.
  2. [III.A.2 and Fig. 4(d)-(e)] The comparison of the second synergy is based on only 6/8 participants in Normal and 7/8 in Intervention, with the remaining participants excluded after 'visually checking.' The reported negative correlation (r = -0.45) between Normal S2 and Intervention S2 is computed on this subset and is not accompanied by a statistical test (e.g., whether r differs from zero). Because the S2 change is the only evidence for the claimed 'induction of a new synergy,' the authors should either include all subjects with a principled labeling procedure or explicitly present the subset analysis as exploratory and temper the corresponding conclusion.
  3. [II.E, III.A.3-4, and III.B] The statistical analysis applies many paired tests across activation profiles, Recr, Ad, topographic mean, CoGx/CoGy, and entropy, without multiple-comparison correction. Several of the reported p-values (p = 0.0371, p = 0.03, p = 0.04) are close to the 0.05 threshold and would not survive a Bonferroni correction for the number of comparisons. In addition, the conclusions depend on several arbitrary thresholds (VAF global >90% and per-muscle >75% in II.D.1, activation duration >0.5 in Eq. (5), and the acceleration variance threshold in II.C.1); no sensitivity analysis is reported. The authors should at least state the number of comparisons and report adjusted or exact p-values, and ideally show that the main results (synergy count = 2, S1 unchanged, S2 different) are robust to reasonable variations of these thresholds.
minor comments (6)
  1. [Eq. (5)] The notation for At is inconsistent with the text: the equation uses 'XOR' while the text says 'OR operation,' and the symbol At is later called Ad in the Results. Please define the operation and unify the symbol.
  2. [Fig. 4(f) caption] The caption refers to 'the mean of the normalized activation profiles (Wnorm)' but the figure displays Hnorm activation profiles; Wnorm is the synergy weight matrix. Please correct the caption.
  3. [Introduction, last paragraph] The sentence 'The objective of this study was to systematically how shoulder exoskeleton...' is missing a verb (e.g., 'investigate'). Please revise.
  4. [II.D.1, Eq. (2)] The normalization in Eq. (2) divides each element by the sum across k (synergies), but the index i runs over muscles and the matrix is m x s; this is not the standard column normalization. Please clarify the intended normalization.
  5. [III.A.5] The null-distribution construction for the non-negative regression is described very briefly; it is unclear how the p-values were obtained and what exact comparison was made. Please provide more detail or move the merging/fractionation interpretation to the Discussion as clearly exploratory.
  6. [References] Reference [43] (Li and Qin, 'Evolutionary LSTM... sleep prediction') appears unrelated to the acceleration variance threshold for task detection; please verify the citation.

Circularity Check

1 steps flagged · score 2.0 of 10

Mild circularity in synergy labeling: the S1 identity conclusion is partly structured by the k-means/AD-MD naming procedure, while the core EMG activation and topographic-map findings are empirical and self-contained.

  1. self definitional [Methods II.D.1 (NMF) and Results III.A.2 (Muscle Synergy Identification)]
    "Consequently, we performed the k-means clustering algorithm on muscle synergies (i.e. each column of W ) from all the subjects and clustered then into two groups, in order to sort the two synergies for each subject [33]. ... The synergy with the highest weight of AD and MD muscle (shoulder agonist muscles) was named as the first synergy, and the other synergy was assigned to the second one. ... There was a strong correlation between Normal S1 and Intervention S1 (r = 0.94), but Normal S2 and Intervention S2 showed opposite correlation ( r = -0.45)."

    S1 labels are assigned by pooling all W columns from both conditions, k-means clustering them, and then naming the AD/MD-dominant cluster as the first synergy. Because Normal S1 and Intervention S1 are selected as members of the same named category, the reported r=0.94 measures within-cluster agreement that was used to form the label, rather than independent evidence that the primary synergy is unchanged. The subsequent conclusion that the two first synergies 'can be considered the same synergy due to the strong correlation' thus rests in part on a similarity that was built into the labeling procedure. The finding is not wholly forced, since the cluster structure could in principle have separated the two conditions, but the correlation is not an independent confirmation.

full rationale

This paper is an empirical NMF-based comparison rather than a derivation of predictions from fitted parameters. The VAF-based selection of n=2, activation-profile comparisons, Recr/Ad statistics, topographic-map mean and entropy, and the permutation-based regression checks are all computed directly from the recorded EMG data and do not reduce to their inputs by construction. The only notable circularity is the synergy-sorting step: k-means clustering on the pooled W columns plus naming by AD/MD dominance is used to define S1 and S2, and the 'S1 identical' conclusion is supported by a correlation that is inflated by that same grouping. Because the main quantitative claims (reduced activation, increased entropy, altered S2) do not depend on the r=0.94 identity claim, the circularity is localized and not load-bearing for most results. The self-citation to the authors' prior device paper [41] supports device validation and EMG normalization, not the synergy conclusions. Overall score reflects one mild, partial circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim relies on standard NMF assumptions and several hand-set thresholds. No new physical entities are introduced. The main free parameters are analysis thresholds and the subjectively tuned assistance level.

free parameters (4)
  • Synergy count selection thresholds = VAF > 90% global and > 75% per muscle
    Hand-chosen thresholds determine s=2 in both conditions; changing them could change the number of synergies and the 'no change in complexity' conclusion (Methods II.D.1).
  • Activation duration threshold = 0.5
    Hnorm > 0.5 defines 'obvious activation' in Ad; arbitrary and affects duration comparisons (Eq. 5).
  • Task-phase detection parameters = mean + 2 SD variance, duration > 5 s
    Used to isolate task execution phases; phase boundary differences between conditions could confound all metrics (Methods II.C.1).
  • Individualized assistance level (PATA and magnitude) = subject-tuned, values not reported
    Assistance was adjusted by user experience and feedback; no quantitative record is provided, so the intervention dose is uncontrolled (Methods II.A).
assumptions (4)
  • domain assumption NMF can extract physiologically meaningful muscle synergies from EMG envelopes.
    The paper relies on this standard premise when interpreting W and H as neural coordination structures (Methods II.D.1, Refs [25,35]).
  • domain assumption VAF thresholds of 90% and 75% are sufficient to determine the true number of synergies.
    No justification is given for these specific thresholds; the main count result depends on them (Methods II.D.1).
  • domain assumption k-means clustering and manual labeling can correctly align the same synergy across subjects and conditions.
    Synergy matching is required before comparing Normal S1 to Intervention S1 and S2 (Results III.A.2).
  • domain assumption EMG topographic map entropy reflects homogeneity of neural activation.
    The interpretation of higher entropy as more uniform muscle activation follows Refs [39,50] and is assumed in the discussion (Methods II.D.2).

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Cite this review

Pith. "Pith review of Effects of Muscle Synergy during Overhead Work with a Passive Shoulder Exoskeleton: A Case Study." pith.science (2026). https://pith.science/paper/VWPPW43N

@misc{pith2026241115504,
  author       = {Pith},
  title        = {Pith review of: Effects of Muscle Synergy during Overhead Work with a Passive Shoulder Exoskeleton: A Case Study},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VWPPW43N}},
  note         = {Machine review of arXiv:2411.15504}
}
read the original abstract

Objective: Shoulder exoskeletons can effectively assist with overhead work. However, their impacts on muscle synergy remain unclear. The objective is to systematically investigate the effects of the shoulder exoskeleton on muscle synergies during overhead work.Methods: Eight male participants were recruited to perform a screwing task both with (Intervention) and without (Normal) the exoskeleton. Eight muscles were monitored and muscle synergies were extracted using non-negative matrix factorization and electromyographic topographic maps. Results: The number of synergies extracted was the same (n = 2) in both conditions. Specifically, the first synergies in both conditions were identical, with the highest weight of AD and MD; while the second synergies were different between conditions, with highest weight of PM and MD, respectively. As for the first synergy in the Intervention condition, the activation profile significantly decreased, and the average recruitment level and activation duration were significantly lower (p<0.05). The regression analysis for the muscle synergies across conditions shows the changes of muscle synergies did not influence the sparseness of muscle synergies (p=0.7341). In the topographic maps, the mean value exhibited a significant decrease (p<0.001) and the entropy significantly increased (p<0.01). Conclusion: The exoskeleton does not alter the number of synergies and existing major synergies but may induce new synergies. It can also significantly decrease neural activation and may influence the heterogeneity of the distribution of monitored muscle activations. Significance: This study provides insights into the potential mechanisms of exoskeleton-assisted overhead work and guidance on improving the performance of exoskeletons.

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Reference graph

Works this paper leans on

50 extracted references · 50 canonical work pages

  1. [1]

    Global estimates of the need for rehabilitation based on the global burden of disease study 2019: a systematic analysis for the global burden of disease study 2019,

    A. Cieza et al. , “Global estimates of the need for rehabilitation based on the global burden of disease study 2019: a systematic analysis for the global burden of disease study 2019,” Lancet., vol. 396, no. 10267, pp. 2006–2017, Dec. 2020

    work page 2019

  2. [2]

    Muscle fatigue: what, why and how it influences muscle function,

    R. M. Enoka and J. Duchateau, “Muscle fatigue: what, why and how it influences muscle function,” J. Physiol., vol. 586, no. 1, pp. 11–23, Jan. 2008

    work page 2008

  3. [3]

    Prevalence and incidence of work-related mus- culoskeletal disorders in secondary industries of 21st century europe: a systematic review and meta-analysis,

    R. Govaerts et al. , “Prevalence and incidence of work-related mus- culoskeletal disorders in secondary industries of 21st century europe: a systematic review and meta-analysis,” BMC Musculoskelet Disord. , vol. 22, pp. 1–30, Aug. 2021

    work page 2021

  4. [4]

    Design and experimental evaluation of a semi-passive upper-limb exoskeleton for workers with motorized tuning of assistance,

    L. Grazi et al. , “Design and experimental evaluation of a semi-passive upper-limb exoskeleton for workers with motorized tuning of assistance,” IEEE Trans Neural Syst Rehabil Eng. , vol. 28, no. 10, pp. 2276–2285, Oct. 2020

    work page 2020

  5. [5]

    Evaluation of a passive exoskeleton for static upper limb activities,

    K. Huysamen et al., “Evaluation of a passive exoskeleton for static upper limb activities,” Appl. Ergonom., vol. 70, pp. 148–155, Jul. 2018

    work page 2018

  6. [6]

    The influence of using exoskeletons during occupational tasks on acute physical stress and strain compared to no exoskeleton– a systematic review and meta-analysis,

    M. B ¨ar et al., “The influence of using exoskeletons during occupational tasks on acute physical stress and strain compared to no exoskeleton– a systematic review and meta-analysis,” Appl. Ergonom. , vol. 94, p. 103385, Jul. 2021

    work page 2021

  7. [7]

    Industrial exoskeletons: Need for intervention effec- tiveness research,

    J. Howard et al. , “Industrial exoskeletons: Need for intervention effec- tiveness research,” Am J Ind Med. , vol. 63, no. 3, pp. 201–208, Dec. 2020

    work page 2020

  8. [8]

    Shoulder-support exoskeletons for overhead work: Current state, challenges and future directions,

    F. A. Reyes et al. , “Shoulder-support exoskeletons for overhead work: Current state, challenges and future directions,” IEEE Trans Med Robot Bionics., vol. 5, no. 3, pp. 516–527, Aug. 2023

    work page 2023

Show all 50 references
  1. [9]

    A passive upper limb exoskeleton with tilted and offset shoulder joints for assisting overhead tasks,

    J. Kim et al. , “A passive upper limb exoskeleton with tilted and offset shoulder joints for assisting overhead tasks,” IEEE/ASME Trans. Mechatronics., vol. 27, no. 6, pp. 4963–4973, Dec. 2022

    2022

  2. [10]

    Passive shoulder exoskeleton support partially mit- igates fatigue-induced effects in overhead work,

    S. DeBock et al. , “Passive shoulder exoskeleton support partially mit- igates fatigue-induced effects in overhead work,” Appl. Ergonom. , vol. 106, p. 103903, Jan. 2023

    2023

  3. [11]

    Ojelade et al

    A. Ojelade et al. , “Three passive arm-support exoskeletons have in- consistent effects on muscle activity, posture, and perceived exertion during diverse simulated pseudo-static overhead nutrunning tasks,” Appl. Ergonom., vol. 110, p. 104015, Jul. 2023

    2023

  4. [12]

    The exo4work shoulder exoskeleton effectively reduces muscle and joint loading during simulated occupational tasks above shoulder height,

    A. van der Have et al., “The exo4work shoulder exoskeleton effectively reduces muscle and joint loading during simulated occupational tasks above shoulder height,” Appl. Ergonom., vol. 103, p. 103800, Sep. 2022

    2022

  5. [13]

    Influence of different passive shoulder exoskele- tons on shoulder and torso muscle activation during simulated horizontal and vertical aircraft squeeze riveting tasks,

    M. J. Jorgensen et al., “Influence of different passive shoulder exoskele- tons on shoulder and torso muscle activation during simulated horizontal and vertical aircraft squeeze riveting tasks,” Appl. Ergonom. , vol. 104, p. 103822, Oct. 2022

    2022

  6. [14]

    Passive shoulder exoskeletons: More effective in the lab than in the field?

    S. De Bock et al., “Passive shoulder exoskeletons: More effective in the lab than in the field?” IEEE Trans. Neural. Syst. Rehabil. Eng. , vol. 29, pp. 173–183, Dec. 2020. 10 GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2023

    2020

  7. [15]

    Ergonomics assessment of passive upper-limb exoskele- tons in an automotive assembly plant,

    S. Iranzo et al., “Ergonomics assessment of passive upper-limb exoskele- tons in an automotive assembly plant,” Appl. Ergonom. , vol. 87, p. 103120, Sep. 2020

    2020

  8. [16]

    Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part ii–“unexpected

    S. Kim et al. , “Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part ii–“unexpected” effects on shoulder motion, balance, and spine loading,” Appl. Ergonom., vol. 70, pp. 323–330, Jul. 2018

    2018

  9. [17]

    Assessing the efficiency of exoskeletons in physical strain reduction by biomechanical simulation with anybody modeling system,

    L. Fritzsche et al., “Assessing the efficiency of exoskeletons in physical strain reduction by biomechanical simulation with anybody modeling system,” Wearable Technol., vol. 2, p. e6, Jun. 2021

    2021

  10. [18]

    An occupational shoulder exoskeleton reduces muscle activity and fatigue during overhead work,

    S. De Bock et al., “An occupational shoulder exoskeleton reduces muscle activity and fatigue during overhead work,” IEEE Trans. Biomed. Eng. , vol. 69, no. 10, pp. 3008–3020, Oct. 2022

    2022

  11. [19]

    Objective and subjective effects of a passive exoskele- ton on overhead work,

    P. Maurice et al., “Objective and subjective effects of a passive exoskele- ton on overhead work,” IEEE Trans. Neural. Syst. Rehabil. Eng. , vol. 28, no. 1, pp. 152–164, Jan. 2019

    2019

  12. [20]

    Exoskeletons for workers: A case series study in an enclosures production line,

    I. Pacifico et al. , “Exoskeletons for workers: A case series study in an enclosures production line,” Appl. Ergonom., vol. 101, p. 103679, May. 2022

    2022

  13. [21]

    Muscle synergy alteration of human during walking with lower limb exoskeleton,

    Z. Li et al. , “Muscle synergy alteration of human during walking with lower limb exoskeleton,” Front Neurosci., vol. 12, p. 1050, Jan. 2019

    2019

  14. [22]

    Effects of an exoskeleton-assisted gait training on post- stroke lower-limb muscle coordination,

    F. Zhu et al. , “Effects of an exoskeleton-assisted gait training on post- stroke lower-limb muscle coordination,” J Neural Eng. , vol. 18, no. 4, p. 046039, Jun. 2021

    2021

  15. [23]

    Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton,

    H. Jeong et al. , “Muscle coordination and recruitment during squat assistance using a robotic ankle–foot exoskeleton,” Sci Rep. , vol. 13, no. 1, p. 1363, Jan. 2023

    2023

  16. [24]

    Surface electromyography-based analysis of the lower limb muscle network and muscle synergies at various gait speeds,

    T. Liang et al. , “Surface electromyography-based analysis of the lower limb muscle network and muscle synergies at various gait speeds,” IEEE Trans Neural Syst Rehabil Eng. , vol. 31, pp. 1230–1237, Feb. 2023

    2023

  17. [25]

    Muscle synergies: implications for clinical eval- uation and rehabilitation of movement,

    S. Safavynia et al. , “Muscle synergies: implications for clinical eval- uation and rehabilitation of movement,” Top Spinal Cord Inj Rehabil. , vol. 17, no. 1, pp. 16–24, 2011

    2011

  18. [26]

    Synergy analysis of back muscle activities in patients with adolescent idiopathic scoliosis based on high-density electromyo- gram,

    W. Wang et al. , “Synergy analysis of back muscle activities in patients with adolescent idiopathic scoliosis based on high-density electromyo- gram,” IEEE Trans Biomed Eng. , vol. 69, no. 6, pp. 2006–2017, Jun. 2021

    2006

  19. [27]

    Motor modules during adaptation to walking in a powered ankle exoskeleton,

    D. A. Jacobs et al. , “Motor modules during adaptation to walking in a powered ankle exoskeleton,” J Neuroeng Rehabil. , vol. 15, pp. 1–15, Jan. 2018

    2018

  20. [28]

    Effects of robotic exoskeleton control options on lower limb muscle synergies during overground walking: An exploratory study among able-bodied adults,

    M. J. Escalona et al., “Effects of robotic exoskeleton control options on lower limb muscle synergies during overground walking: An exploratory study among able-bodied adults,” Neurophysiol Clin., vol. 50, no. 6, pp. 495–505, Nov. 2020

    2020

  21. [29]

    Lateral symmetry of synergies in lower limb muscles of acute post-stroke patients after robotic intervention,

    C. K. Tan et al. , “Lateral symmetry of synergies in lower limb muscles of acute post-stroke patients after robotic intervention,” Front Neurosci., vol. 12, p. 276, Apr. 2018

    2018

  22. [30]

    Evaluation of muscle synergy during exoskeleton- assisted walking in persons with multiple sclerosis,

    T. Afzal et al. , “Evaluation of muscle synergy during exoskeleton- assisted walking in persons with multiple sclerosis,” IEEE Trans Biomed Eng., vol. 69, no. 10, pp. 3265–3274, Oct. 2022

    2022

  23. [31]

    Exoskeleton-assisted sit-to-stand training improves lower-limb function through modifications of muscle synergies in sub- acute stroke survivors,

    Y . A. Li et al. , “Exoskeleton-assisted sit-to-stand training improves lower-limb function through modifications of muscle synergies in sub- acute stroke survivors,” IEEE Trans Neural Syst Rehabil Eng. , vol. 31, pp. 3095–3105, Jul. 2023

    2023

  24. [32]

    The effects of hand dominance, fatigue, and sex on muscle activation during a repetitive overhead fatiguing task,

    E. Renda et al. , “The effects of hand dominance, fatigue, and sex on muscle activation during a repetitive overhead fatiguing task,” Hum Mov Sci., vol. 92, p. 103149, Sep. 2023

    2023

  25. [33]

    Muscle synergy assessment during single-leg stance,

    M. Ghislieri et al. , “Muscle synergy assessment during single-leg stance,” IEEE Trans Neural Syst Rehabil Eng. , vol. 28, no. 12, pp. 2914– 2922, Oct. 2020

    2020

  26. [34]

    Mapping responses of lumbar paravertebral muscles to single-pulse cortical tms using high-density surface electromyography,

    N. Jiang et al. , “Mapping responses of lumbar paravertebral muscles to single-pulse cortical tms using high-density surface electromyography,” IEEE Trans Neural Syst Rehabil Eng. , vol. 29, pp. 831–840, Apr. 2021

    2021

  27. [35]

    Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running,

    M. F. Rabbi et al. , “Non-negative matrix factorisation is the most appropriate method for extraction of muscle synergies in walking and running,” Sci Rep. , vol. 10, no. 1, p. 8266, May. 2020

    2020

  28. [36]

    Muscle synergies during repetitive stoop lifting with a bioelectrically-controlled lumbar support exoskeleton,

    C. K. Tan et al. , “Muscle synergies during repetitive stoop lifting with a bioelectrically-controlled lumbar support exoskeleton,” Front Hum Neurosci., vol. 13, p. 142, Apr. 2019

    2019

  29. [37]

    Differences in muscle synergy symmetry between subacute post-stroke patients with bioelectrically-controlled exoskeleton gait training and conventional gait training,

    C. K. Tan et al. , “Differences in muscle synergy symmetry between subacute post-stroke patients with bioelectrically-controlled exoskeleton gait training and conventional gait training,” Front Bioeng Biotechnol. , vol. 8, p. 770, Jul. 2020

    2020

  30. [38]

    Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses,

    K. M. Steele et al., “Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses,” J Neurophysiol. , vol. 113, no. 7, pp. 2102–2113, Apr. 2015

    2015

  31. [39]

    Effect analysis of wearing an lumbar exoskeleton on coordinated activities of the low back muscles using semg topographic maps,

    N. Jiang et al. , “Effect analysis of wearing an lumbar exoskeleton on coordinated activities of the low back muscles using semg topographic maps,” IEEE Trans Neural Syst Rehabil Eng. , vol. 32, pp. 259–270, Jan. 2024

    2024

  32. [40]

    Lumbar muscle electromyographic dynamic topography during flexion-extension,

    Y . Hu et al. , “Lumbar muscle electromyographic dynamic topography during flexion-extension,” J Electromyogr Kinesiol. , vol. 20, no. 2, pp. 246–255, Apr. 2010

    2010

  33. [41]

    A novel passive occupational shoulder exoskeleton with adjustable peak assistive torque angle for overhead tasks,

    J. Tian et al. , “A novel passive occupational shoulder exoskeleton with adjustable peak assistive torque angle for overhead tasks,” IEEE Trans Biomed Eng , 2024

    2024

  34. [42]

    Development of recommendations for semg sensors and sensor placement procedures,

    H. J. Hermens et al. , “Development of recommendations for semg sensors and sensor placement procedures,” J. Electromyogr . Kinesiol., vol. 10, no. 5, pp. 361–374, Oct. 2000

    2000

  35. [43]

    Evolutionary lstm neural network as sleep prediction model on smartphone,

    X. Li and X. Qin, “Evolutionary lstm neural network as sleep prediction model on smartphone,” Computer Systems & Applications. , vol. 29, no. 11, pp. 196–203, Oct. 2020

    2020

  36. [44]

    Common muscle synergies for balance and walking,

    S. A. Chvatal and L. H. Ting, “Common muscle synergies for balance and walking,” Front Comput Neurosci. , vol. 7, p. 48, May. 2013

    2013

  37. [45]

    Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy,

    K. M. Steele et al., “Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy,” Dev Med Child Neurol. , vol. 57, no. 12, pp. 1176–1182, Dec. 2015

    2015

  38. [46]

    Choosing a wavelet for single-trial emg,

    M. Flanders, “Choosing a wavelet for single-trial emg,” J Neurosci Methods., vol. 116, no. 2, pp. 165–177, May. 2002

    2002

  39. [47]

    Plasticity of muscle synergies through fractionation and merging during development and training of human runners,

    V . C. Cheung et al., “Plasticity of muscle synergies through fractionation and merging during development and training of human runners,” Nat Commun., vol. 11, no. 1, p. 4356, Aug. 2020

    2020

  40. [48]

    Sample entropy-based surface electromyographic examina- tion with a linear electrode array in survivors with spinal cord injury,

    L. Li et al., “Sample entropy-based surface electromyographic examina- tion with a linear electrode array in survivors with spinal cord injury,” IEEE Trans Neural Syst Rehabil Eng. , vol. 31, pp. 2944 – 2952, Jun. 2023

    2023

  41. [49]

    Merging of healthy motor modules predicts re- duced locomotor performance and muscle coordination complexity post- stroke,

    D. J. Clark et al. , “Merging of healthy motor modules predicts re- duced locomotor performance and muscle coordination complexity post- stroke,” J Neurophysiol., vol. 103, no. 2, pp. 844–857, Feb. 2010

    2010

  42. [50]

    The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration,

    D. Farina et al. , “The change in spatial distribution of upper trapezius muscle activity is correlated to contraction duration,” J Electromyogr Kinesiol., vol. 18, no. 1, pp. 16–25, Feb. 2008

    2008

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.