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REVIEW 3 major objections 5 minor 184 references

Human Centric Embodied Intelligence for Soft Wearable Robotics

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Soft wearable robots should be designed around perception and cognition first, with actuation chosen last, a new review argues.

desk verdict Useful integrative framework, but its central design-ordering prescription is asserted, not shown, and the paper's own passive and perception-free examples pull against it. read the letter →

arxiv 2608.03556 v1 pith:XJV7CULI submitted 2026-08-04 cs.RO

Rainier Natividad , Raye Chen-Hua Yeow This is my paper
classification cs.RO
keywords softwearablerobotsembodiedintelligencePCAAframeworkhuman-centricdesignperceptionaugmentationhumandigitaltwinexosuit
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 review proposes that soft wearable robotics has reached a point where its hardest problems are not mechanical but integrative: making a device that a specific person will wear, trust, and benefit from over months. To organize that integration, it introduces Human-Centric Embodied Intelligence (HCEI), a paradigm in which intelligence is distributed across the wearer's body, the soft device, and their ongoing co-adaptation, and the PCAA framework, which splits any soft wearable into four coupled functions: Perception, Cognition, Actuation, and Augmentation. The central claim is that these four functions must be committed together from the outset, and that the field's usual actuator-first sequence (pick an actuator, then add sensing and control, then find a use case) inverts the dependency structure. If the review is right, the field should reorganize its design sequence around the augmentation objective and human-integration constraints, and select actuation only after perception and cognition have been specified.

What carries the argument

PCAA (Perception–Cognition–Actuation–Augmentation) is the paper's central analytical framework: a decomposition of any soft wearable robot into four coupled functions arranged as a vertical stack grounded in physical embodiment, with an ascending information path from perception through cognition, a descending energy path through actuation, and a closing feedback path from augmentation back to perception. Its work is to make the field's design sequence legible: because the pillars are mutually dependent, the framework implies that design must commit them concurrently, and that actuator-first design inverts the dependency structure of the problem. HCEI is the paradigm that motivates the frame

What would settle it

A controlled comparison in which two teams design the same soft ankle exosuit for the same user population—one following PCAA co-design (augmentation objective and human constraints first, actuation last), the other following actuator-first practice—measuring generalization across users, comfort, adherence, and augmentation effect over several weeks; if the actuator-first device matches or outperforms on these outcomes, the framework's central prescription is falsified. A cheaper falsifier: identify a fielded soft wearable with no dedicated perception and minimal cognition (such as the shape-m

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Extended reading notes

Core claim

The paper's central claim is that a soft wearable robot is best understood as a coupled human–robot ensemble whose intelligence is shared across morphology, multimodal body-linked sensing, adaptive cognition, compliant actuation, and the wearer's own physiological and behavioral adaptation. It names this paradigm Human-Centric Embodied Intelligence (HCEI) and operationalizes it through the PCAA framework, which decomposes any such device into four mutually dependent functions—Perception (sensing the joined human–robot state), Cognition (turning that state into a decided action), Actuation (delivering the force), and Augmentation (the realized change in the wearer's function). The paper argue

Load-bearing premise

The framework's normative prescription depends on the assumption that every soft wearable robot can be decomposed into exactly four mutually dependent functions (Perception, Cognition, Actuation, Augmentation) that must be committed concurrently; if some functions are effectively independent, or actuator-first design succeeds in many cases, the central prescription loses force.

Editorial extensions

If this is right

  • Design processes for soft wearables should start from the augmentation objective and human-integration constraints, not from an actuator choice.
  • The recurring field failures (non-generalizing assistance, intent detection defeated by the interface, abandonment despite bench benefit) are reinterpreted as symptoms of actuator-first commitment order.
  • Perception and cognition become the pillars that bound achievable augmentation, so robust all-day self-calibrating sensing is a precondition for anything downstream.
  • Actuator selection becomes an application-conditioned trade-off over multiple wearable-relevant axes, with no universally optimal family; hybridization and quasi-passive designs are promising directions.
  • The roadmap implies successive capability generations from context-aware wearables to personalized intelligence, digital twins, and human–AI symbiosis, each gated by a coupled grand challenge.

Reading between the lines

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

  • If the PCAA coupling claim is correct, evaluation protocols for soft wearables should include longitudinal co-adaptation measures as primary outcomes, not just single-session biomechanics.
  • The framework may extend beyond soft wearables as a heuristic for rigid exoskeletons and prosthetics, though the paper restricts its scope to soft systems.
  • The human digital twin concept, if validated, could turn personalization into a bookkeeping problem across time, making privacy of inferred physiological states a first-order regulatory issue.
  • A testable extension would compare designs developed under PCAA co-design versus actuator-first practice for the same augmentation target, measuring generalization, comfort, and long-term adherence.
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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 / 5 minor

Summary. This paper is an integrative narrative review of soft wearable robotics. It introduces Human-Centric Embodied Intelligence (HCEI) as a proposed field-level paradigm and the Perception–Cognition–Actuation–Augmentation (PCAA) framework as the analytical structure built on it. The central claim is that soft wearable robots should be designed by first stating the augmentation objective and human-integration constraints, then specifying perception and cognition, and finally selecting actuation, thereby inverting what the authors call the field's inherited actuator-first design sequence. The review applies this lens to a broad literature spanning soft materials, sensing, cognition, control, actuation, augmentation applications, clinical translation, regulation, and ethics, and closes with a five-generation capability roadmap and fifty open questions. The paper is carefully hedged: original constructs are explicitly labeled as proposed organizing devices, forward-looking elements are marked as projections, and Section 9.4 states the limitations of the narrative-review method, selection bias, the fast-moving literature, and the uneven evidence base.

Significance. If the HCEI/PCAA framing is accepted, it would reorganize how the field states design problems and evaluates devices, moving the intelligence core ahead of actuation and making human integration a first-order design axis. The paper's strengths are real: it is unusually self-aware about the status of its own constructs; the actuator comparison matrix (Table 2) and clinical readiness scorecard (Table 3) use transparent ordinal grades rather than spurious precision; the fifty open questions in the Supplementary Material are concrete and falsifiable; and the review marks its own limitations (Sections 1.1, 9.2, 9.4). These strengths make the paper a useful organizing resource even if its normative prescription is not yet established. However, the strongest actionable claim—that recurring translation failures share a common cause in actuation-first design, and that actuation must be selected last—is a causal and universal claim that the paper does not support, and it is in tension with its own successful examples. That does not sink the paper as a perspective piece, but it does prevent the central prescription from being taken as demonstrated. The contribution is better characterized as

major comments (3)
  1. [§3.3] The sentence 'The recurring translation failures reported across the field share a common source in this inverted commitment order' is a causal claim about the history of soft wearable robotics. The cited support ([39]–[41], and the surrounding text) shows that assistance needs per-user tuning, that comfort affects adherence, and that interfaces can perturb sensing; none of these citations compares design sequences or shows that an augmentation-first, actuation-last order would have avoided the failures. This is load-bearing because Table 4 and the paper's design-ordering message rest on it. The claim should be reframed as a hypothesis or a consistent-reading claim ('can be read as symptoms of...') rather than a demonstrated causal explanation, or supplemented with comparative historical cases.
  2. [§2.1 and §3.4] The universal prescription that the four pillars 'must be reasoned about together and committed concurrently from the outset' (Section 3) is contradicted by the paper's own examples. Section 2.1 presents the unpowered ankle exoskeleton [14] as a success story, yet it has no perception or cognition; Section 3.4 maps the SMA fabric-muscle suit as 'perception absent' and 'cognition minimal,' while treating it as a legible PCAA instance. If successful devices can lack one or two pillars entirely, then the normative statement needs qualification: specify the class of devices for which concurrent commitment is necessary (e.g., adaptive, learning, personalized systems) and acknowledge that morphology-only devices may legitimately occupy a different design region. Without this, the central prescription is overbroad.
  3. [§3.2] The exhaustiveness and coupling of the four pillars is stipulated rather than argued. The text asserts that PCAA names 'the four functional pillars of an embodied wearable and asserts their mutual dependence,' and that 'any soft wearable robot is decomposed into four coupled functions.' This is a definitional claim that cannot be falsified in its current form because every device can be described as sensing, deciding, acting, and having an effect. To make the framework's normative force clear, the paper should state what would count as a counterexample (e.g., a device where the augmentation target is independent of perception, or where actuator selection does not constrain cognition) or explicitly label the exhaustiveness as a design stance, not an empirical generalization. The current wording gives the appearance of circularity: the pillars are defined so that they are always present, a
minor comments (5)
  1. [§4 opening] The text calls Perception 'the first pillar of the PCAA pipeline,' but Section 3.2 explicitly says the pillars 'are not a pipeline that one walks once from end to end.' The terminology should be harmonized (e.g., 'first pillar of the PCAA framework').
  2. [§3.2 and §4.1] The intelligence ladder in Section 3.2 lists 'Level 1 [38], Level 2 [33], and Level 3 [34]' without naming the levels. Readers cannot tell what each level denotes. A one-line descriptor per level would make the ordinal scale usable.
  3. [Table 2] The legend correctly warns that ratings are not comparable as numbers across columns, but the table would benefit from a sentence in the main text explaining how a reader should use the matrix for an application-specific choice, rather than leaving this to the three observations that follow.
  4. [§9.4] The limitations paragraph is honest and should be retained. In particular, the statement that readiness judgements 'inherit the uncertainty' of the underlying evidence should be carried more explicitly into Table 3, where 'Moderate' and 'Emerging' grades are presented without error bars or confidence qualifiers in the table itself.
  5. [References] A number of references are self-citations of the authors' previous work (e.g., [50], [75], [81], [82], [110]). This is not inappropriate in a review, but the text sometimes cites these as if they were independent validations of a trend. A brief note on the authors' own contributions would improve transparency.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: PCAA/HCEI are proposed organizing constructs, stipulated rather than derived, and self-citations are illustrative device examples, not load-bearing premises.

full rationale

This is an integrative narrative review. The central contributions (HCEI paradigm and PCAA framework) are presented as proposed organizing devices and are not derived from the cited literature by equations or fitted parameters; they are stipulated definitions (‘HCEI is this review’s central thesis: a field-level paradigm, or lens, for what soft wearable robotics should become, not a design procedure’; ‘Each original construct is labelled as a proposed organizing device at first mention’). The normative claim in Sec. 3.3 that actuation-first design causes translation failures is an empirical causal assertion supported by external citations; even if the evidence is indirect, the claim is not equivalent to its inputs by construction, so it is a correctness/evidence concern rather than circularity. The authors do cite their own prior works ([50], [75], [81], [82], [88], [110], [129]), but these function as concrete examples of soft actuators, sensors, and clinical outcomes, not as authority for the novel framework, and they are externally validated empirical results. The paper also explicitly lists limitations (purposive selection, uneven evidence, provisional readiness judgements), further reducing any impression that the framework is being justified by its own conclusions. No step reduces to self-definition or fitted-input-as-prediction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 5 invented entities

The paper's central claim is a conceptual framework rather than an empirical result. It rests on stipulated definitions, a purposive literature selection, and value judgments about design objectives. There are no free parameters or fitted values. The novel constructs (HCEI, PCAA, HDS, Wearable Intelligence Stack, roadmap) are organizational devices with no independent falsifiable evidence; they are all explicitly labeled as proposals or projections.

assumptions (4)
  • ad hoc to paper The four PCAA pillars (Perception, Cognition, Actuation, Augmentation) are exhaustive and mutually coupled for soft wearable robots.
    Proposed in Section 3.2 without independent justification. The framework's normative guidance, that all four must be designed concurrently, depends on this decomposition being both exhaustive and coupled.
  • domain assumption Human agency, comfort, and long-term well-being are primary design objectives.
    Stated in Section 2.2 as a design stance ('the human is treated as a co-equal agent... a design stance adopted here'), not as an empirically demonstrated property. This value judgment underpins the entire HCEI paradigm.
  • domain assumption The purposively selected literature is representative enough to support the synthesis.
    Acknowledged as a limitation in Section 9.4: 'purposive selection introduces the possibility of literature-selection bias.' The central narrative depends on the selection not omitting decisive counter-evidence.
  • ad hoc to paper The five-generation capability roadmap is a useful periodization of the field's future.
    Introduced in Section 9.2 as 'the review's own capability periodization, stated as projection.' It is not derived from data but proposed as an organizing device.
invented entities (5)
  • Human-Centric Embodied Intelligence (HCEI)
    purpose: Central paradigm reframing soft wearable robotics as a coupled human-robot system with distributed intelligence.
    New label for a synthesis of existing concepts. It makes no falsifiable predictions outside the paper.
  • Perception-Cognition-Actuation-Augmentation (PCAA) framework
    purpose: Organizational framework decomposing any soft wearable robot into four coupled functions to guide co-design.
    Structural taxonomy proposed by the authors. Its utility is asserted, not empirically validated.
  • Human Digital Signature (HDS)
    purpose: Personalized, multimodal, time-evolving representation of the wearer to enable personalized assistance.
    Proposed abstraction over wearable data (Section 4.2). It is a conceptual model with no independent testable handle.
  • Wearable Intelligence Stack
    purpose: Layered model within the Cognition pillar showing where intelligence should be located, with learning inside a safety envelope.
    Section 5.3 conceptual model. Its value is illustrative, not empirically grounded.
  • Five-generation roadmap
    purpose: Stages the field's evolution from assistive wearables to human-AI symbiosis, with explicit hedges on generations IV and V.
    Projection stated in Section 9.2 as the review's own periodization. No data or validation are provided.

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Cite this review

Pith. "Pith review of Human Centric Embodied Intelligence for Soft Wearable Robotics." pith.science (2026). https://pith.science/paper/XJV7CULI

@misc{pith2026260803556,
  author       = {Pith},
  title        = {Pith review of: Human Centric Embodied Intelligence for Soft Wearable Robotics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XJV7CULI}},
  note         = {Machine review of arXiv:2608.03556}
}
read the original abstract

Soft wearable robots have evolved rapidly from proof-of-concept devices into promising platforms for rehabilitation, occupational assistance, and human augmentation. As the field matures, its central challenge extends beyond the development of softer materials and more capable actuators to the integration of sensing, intelligence, and human adaptation into systems that users can wear comfortably, trust, and benefit from over extended periods. This transition motivates the concept of Human-Centric Embodied Intelligence (HCEI), in which intelligence emerges from the coupled human-robot system through the interaction of morphology, multimodal sensing, adaptive cognition, compliant actuation, and the wearer's own physiological and behavioral adaptation. To organize this perspective, this review introduces the Perception-Cognition-Actuation-Augmentation (PCAA) framework, which positions perception and cognition as the primary drivers of design, shifting development beyond the conventional actuator-first paradigm. Using this framework, the review synthesizes advances in soft materials, wearable sensing, artificial intelligence, actuation, human-robot interaction, digital twins, clinical translation, manufacturing, regulation, and ethics, highlighting how these interdependent components collectively shape long-term personalization and real-world deployment. By providing a unified conceptual framework and design perspective, this review aims to guide future research, foster interdisciplinary collaboration, and accelerate the translation of next-generation soft wearable robots toward personalized, predictive, and human-centric wearable intelligence.

Figures

Figures reproduced from arXiv: 2608.03556 by the authors.

Figure 1
Figure 1. Evolution toward Human-Centric Embodied Intelligence. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Historical evolution of soft wearable robotics across four overlapping eras. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. The PCAA framework as an integrative co-design lens. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The Human Digital Signature (HDS): a model operationalizing the Perception [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: The Wearable Intelligence Stack: a layered view within the Cognition pillar. [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Roadmap to Human-Centric Embodied Intelligence across five capability [PITH_FULL_IMAGE:figures/full_fig_p032_6.png]

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Works this paper leans on

184 extracted references · 67 canonical work pages

  1. [39]

    Human-in-the-loop optimization of hip assistance with a soft exosuit during walking,

    Y . Ding, M. Kim, S. Kuindersma, and C. J. Walsh, “Human-in-the-loop optimization of hip assistance with a soft exosuit during walking,” Science Robotics, vol. 3, p. eaar5438, 2018, doi: 10.1126/scirobotics.aar5438

    work page doi:10.1126/scirobotics.aar5438 2018

  2. [41]

    Design and evaluation of an independent 4-week, exosuit-assisted, post-stroke community walking program,

    R. W. Nuckols and L. N. Awad, “Design and evaluation of an independent 4-week, exosuit-assisted, post-stroke community walking program,” Annals of the New York Academy of Sciences, 2023, doi: 10.1111/nyas.14998

    work page doi:10.1111/nyas.14998 2023

  3. [14]

    Reducing the energy cost of human walking using an unpowered exoskeleton,

    S. H. Collins, M. B. Wiggin, and G. S. Sawicki, “Reducing the energy cost of human walking using an unpowered exoskeleton,” Nature, vol. 522, pp. 212–215, 2015, doi: 10.1038/nature14288

    work page doi:10.1038/nature14288 2015

  4. [1]

    Wearable and implantable soft robots,

    S. Yin, D.R. Yao, Y . Song, W Heng, X. Ma, H. Han, and W. Gao, “Wearable and implantable soft robots,” Chemical Reviews, vol. 124, no. 20, pp. 11585–11636, 2024, doi: 10.1021/acs.chemrev.4c00513

    work page doi:10.1021/acs.chemrev.4c00513 2024

  5. [2]

    Global, regional, and national burden of stroke and its risk factors, 1990–2021,

    GBD 2021 Stroke Collaborators, “Global, regional, and national burden of stroke and its risk factors, 1990–2021,” The Lancet Neurology, 2024, doi: 10.1016/S1474- 4422(24)00369-7

    doi:10.1016/s1474- 2021

  6. [3]

    Biomechanical and metabolic effectiveness of an industrial exoskeleton for overhead work,

    T. Schmalz, J. Schandlinger, M. Schuler, J. Bornmann, B. Schirrmeister, A. Kannenberg, and M. Ernst, “Biomechanical and metabolic effectiveness of an industrial exoskeleton for overhead work,” International Journal of Environmental Research and Public Health, vol. 16, no. 23, p. 4792, 2019, doi: 10.3390/ijerph16234792

    work page doi:10.3390/ijerph16234792 2019

  7. [4]

    Biomechanical evaluation of a new passive back support exoskeleton,

    A. S. Koopman, T. Nef, S. J. Baltrusch, and M. P. de Looze, “Biomechanical evaluation of a new passive back support exoskeleton,” Journal of Biomechanics, vol. 105, p. 109795, 2020, doi: 10.1016/j.jbiomech.2020.109795

    arXiv 2020

  8. [5]

    Reducing the metabolic rate of walking and running with a versatile, portable exosuit,

    J. Kim, G. Lee, R. Heimgartner, D. Arumukhom Revi, N. Karavas, D. Nathanson, I. Galiana, A. Eckert-Erdheim, P. Murphy, D. Perry, N. Menard, D. K. Choe, P. Malcolm, and C. J. Walsh, “Reducing the metabolic rate of walking and running with a versatile, portable exosuit,” Science, vol. 365, pp. 668–672, 2019, doi: 10.1126/science.aav7536

    work page doi:10.1126/science.aav7536 2019

Show all 184 references
  1. [6]

    Introduction to the soft wearable sensors themed collection,

    W. Cheng, D. H. Kim, N. Lu, J. Rogers, and A. Rwei, “Introduction to the soft wearable sensors themed collection,” Materials Horizons, vol. 12, no. 24, pp. 10388– 10389, 2025, doi: 10.1039/d5mh90105g

    2025 doi

  2. [7]

    Soft upper-limb wearable robotic devices: Technology and applications,

    B. Sharma, P. T. Phan, J. Davies, and T. N. Do, “Soft upper-limb wearable robotic devices: Technology and applications,” Advanced Intelligent Systems, vol. 6, 2024, doi: 10.1002/aisy.202400266

    2024 doi

  3. [8]

    Wearable robots for rehabilitation and assistance of gait: a narrative review,

    J. M. Cha, J. Hong, J. Yoo, and D. Rha, “Wearable robots for rehabilitation and assistance of gait: a narrative review,” Annals of Rehabilitation Medicine, vol. 49, pp. 187–195, 2025, doi: 10.5535/arm.250093

    2025 doi

  4. [9]

    A review of soft wearable robots that provide active assistance: Trends, common actuation methods, fabrication, and applications,

    C. Thalman and P. Artemiadis, “A review of soft wearable robots that provide active assistance: Trends, common actuation methods, fabrication, and applications,” Wearable Technologies, vol. 1, p. e3, 2020, doi: 10.1017/wtc.2020.4

    2020 doi

  5. [10]

    Robot-aided neurorehabilitation,

    H. I. Krebs, N. Hogan, M. L. Aisen, and B. T. V olpe, “Robot-aided neurorehabilitation,” IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 1, pp. 75–87, 1998, doi: 10.1109/86.662623

    1998 doi

  6. [11]

    Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX),

    A. Zoss, H. Kazerooni, and A. Chu, “Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX),” IEEE/ASME Transactions on Mechatronics, vol. 11, no. 2, pp. 128–138, 2006, doi: 10.1109/TMECH.2006.871087

    2006

  7. [12]

    The ReWalk powered 35 exoskeleton to restore ambulatory function to individuals with thoracic-level motor- complete spinal cord injury,

    A. Esquenazi, M. Talaty, A. Packel, and M. Saulino, “The ReWalk powered 35 exoskeleton to restore ambulatory function to individuals with thoracic-level motor- complete spinal cord injury,” American Journal of Physical Medicine & Rehabilitation, vol. 91, no. 11, pp. 911–921, 2...

    2012 doi

  8. [13]

    A biologically inspired soft exosuit for walking assistance,

    A. T. Asbeck, S. M. M. De Rossi, K. G. Holt, and C. J. Walsh, “A biologically inspired soft exosuit for walking assistance,” International Journal of Robotics Research, vol. 34, no. 6, pp. 744–762, 2015, doi: 10.1177/0278364914562476

    2015 doi

  9. [15]

    A soft robotic exosuit improves walking in patients after stroke,

    L. N. Awad, J. Bae, K. O’Donnell, S. M. M. De Rossi, K. Hendron, L. H. Sloot, P. Kudzia, S. Allen, K. G. Holt, T. D. Ellis, and C. J. Walsh, “A soft robotic exosuit improves walking in patients after stroke,” Science Translational Medicine, vol. 9, p. eaai9084, 2017, doi: 10.1...

    2017 doi

  10. [16]

    Improving grasp function after spinal cord injury with a soft robotic glove,

    C. Correia, K. Nuckols, D. Wagner, and C. J. Walsh, “Improving grasp function after spinal cord injury with a soft robotic glove,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, pp. 1407–1415, 2020, doi: 10.1109/TNSRE.2020.2988260

    2020

  11. [17]

    Self-organization, embodiment, and biologically inspired robotics,

    R. Pfeifer, M. Lungarella, and F. Iida, “Self-organization, embodiment, and biologically inspired robotics,” Science, vol. 318, pp. 1088–1093, 2007, doi: 10.1126/science.1145803

    2007 doi

  12. [18]

    Towards a theoretical foundation for morphological computation with compliant bodies,

    H. Hauser, A. J. Ijspeert, R. M. Füchslin, R. Pfeifer, and W. Maass, “Towards a theoretical foundation for morphological computation with compliant bodies,” Biological Cybernetics, vol. 105, pp. 355–370, 2011, doi: 10.1007/s00422-012-0471-0

    2011 doi

  13. [19]

    Efficient bipedal robots based on passive-dynamic walkers,

    S. Collins, A. Ruina, R. Tedrake, and M. Wisse, “Efficient bipedal robots based on passive-dynamic walkers,” Science, vol. 307, pp. 1082–1085, 2005, doi: 10.1126/science.1107799

    2005 doi

  14. [20]

    Embodied intelligence via learning and evolution,

    A. Gupta, S. Savarese, S. Ganguli, and L. Fei-Fei, “Embodied intelligence via learning and evolution,” Nature Communications, vol. 12, p. 5721, 2021, doi: 10.1038/s41467- 021-25874-z

    2021 doi

  15. [21]

    Perspectives on intelligence in soft robotics,

    V . G. Kortman, B. Mazzolai, A. Sakes, and J. Jovanova, “Perspectives on intelligence in soft robotics,” Advanced Intelligent Systems, vol. 7, 2024, doi: 10.1002/aisy.202400294

    2024 doi

  16. [22]

    Exploring embodied intelligence in soft robotics: a review,

    Z. Zhao, Q. Wu, J. Wang, B. Zhang, C. Zhong, and A. A. Zhilenkov, “Exploring embodied intelligence in soft robotics: a review,” Biomimetics, vol. 9, p. 248, 2024, doi: 10.3390/biomimetics9040248

    2024 doi

  17. [23]

    Embodied intelligence in wearable robotics,

    C. Hu and L. Gionfrida, “Embodied intelligence in wearable robotics,” IOP Conference Series: Materials Science and Engineering, vol. 1343, p. 012014, 2026, doi: 10.1088/1757-899X/1343/1/012014

    2026 doi

  18. [24]

    A soft inductive bimodal sensor for proprioception and tactile sensing of soft machines,

    Y . Peng, H. Wu, Z. Wang, Y . Wang, and H. Wang, “A soft inductive bimodal sensor for proprioception and tactile sensing of soft machines,” Soft Robotics, vol. 11, pp. 1055– 1067, 2024, doi: 10.1089/soro.2023.0249

    2024

  19. [25]

    Embedded fluidic sensing and control with soft open-cell foams,

    A. Rajappan, Z. Liu, T. F. Yap, R. M. Rasheed, and D. J. Preston, “Embedded fluidic sensing and control with soft open-cell foams,” Advanced Functional Materials, vol. 34, 2024, doi: 10.1002/adfm.202403379

    2024 doi

  20. [26]

    Integrating flexible wearable sensors and embodied intelligence: Reach the unreachable,

    Z. Xing, F. Wang, W. Qi, and H. Su, “Integrating flexible wearable sensors and embodied intelligence: Reach the unreachable,” 2024 IEEE Conference on Telepresence, pp. 188–191, 2024, doi: 10.1109/telepresence63209.2024.10841524

    2024

  21. [27]

    Humans can 36 continuously optimize energetic cost during walking,

    J. C. Selinger, S. M. O’Connor, J. D. Wong, and J. M. Donelan, “Humans can 36 continuously optimize energetic cost during walking,” Current Biology, vol. 25, no. 18, pp. 2452–2456, 2015, doi: 10.1016/j.cub.2015.08.016

    2015 doi

  22. [28]

    Academic review and perspectives on robotic exoskeletons,

    G. Bao, L. Pan, H. Fang, and Y . Wan, “Academic review and perspectives on robotic exoskeletons,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, pp. 2294–2304, 2019, doi: 10.1109/TNSRE.2019.2944655

    2019

  23. [29]

    Advancements in soft wearable robots: a systematic review of actuation mechanisms and physical interfaces,

    S. Hussain and F. Ficuciello, “Advancements in soft wearable robots: a systematic review of actuation mechanisms and physical interfaces,” IEEE Transactions on Medical Robotics and Bionics, vol. 6, no. 3, pp. 903–929, 2024, doi: 10.1109/tmrb.2024.3407374

    2024

  24. [30]

    Upper limb soft robotic wearable devices: a systematic review,

    E. Bardi, M. Gandolla, F. Braghin, F. Resta, A. L. G. Pedrocchi, and E. Ambrosini, “Upper limb soft robotic wearable devices: a systematic review,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, p. 87, 2022, doi: 10.1186/s12984- 022-01065-9

    2022 doi

  25. [31]

    Wearable upper limb robotics for pervasive health: a review,

    C. Ochieze, S. Zare, and Y . Sun, “Wearable upper limb robotics for pervasive health: a review,” Progress in Biomedical Engineering, vol. 5, p. 032003, 2023, doi: 10.1088/2516-1091/acc70a

    2023 doi

  26. [32]

    Soft wearable robots: Development status and technical challenges,

    Y . Shi, W. Dong, W. Lin, and Y . Gao, “Soft wearable robots: Development status and technical challenges,” Sensors, vol. 22, no. 19, p. 7584, 2022, doi: 10.3390/s22197584

    2022 doi

  27. [33]

    A stretchable sensor for force estimation in soft wearable robots,

    C. Basla, A.-M. Georgarakis, M. Reichmuth, H. Chen, P. Wolf, S. Lacour, R. Riener, and M. Xiloyannis, “A stretchable sensor for force estimation in soft wearable robots,” 2022 IEEE ICORR, 2022, doi: 10.1109/ICORR55369.2022.9896479

    2022

  28. [34]

    Locomotion joint angle and moment estimation with soft wearable sensors for personalized exosuit control,

    L. Feng, L. Gui, W. Xu, X. Wang, C. Yang, Y . Jin, and W. Yang, “Locomotion joint angle and moment estimation with soft wearable sensors for personalized exosuit control,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025, doi: 10.1109/TNSRE.2025.3547361

    2025

  29. [35]

    Soft, wearable robotics and haptics: Technologies, trends, and emerging applications,

    M. Zhu, S. Biswas, S. I. Dinulescu, N. Kastor, E. W. Hawkes, and Y . Visell, “Soft, wearable robotics and haptics: Technologies, trends, and emerging applications,” Proceedings of the IEEE, vol. 110, pp. 246–272, 2022, doi: 10.1109/JPROC.2021.3140049

    2022

  30. [36]

    Soft robotic glove for combined assistance and at-home rehabilitation,

    P. Polygerinos, Z. Wang, K.C. Galloway, R.J. Wood, and C.J. Walsh, “Soft robotic glove for combined assistance and at-home rehabilitation,” Robotics and Autonomous Systems, vol. 73, pp. 135–143, 2015, doi: 10.1016/j.robot.2014.08.014

    2015 doi

  31. [37]

    Exo-glove poly II,

    B. B. Kang, H. Choi, H. Lee, and K. J. Cho, “Exo-glove poly II,” Soft Robotics, vol. 6, no. 2, pp. 214–227, 2019, doi: 10.1089/soro.2018.0006

    2019

  32. [38]

    Pneumatic quasi-passive actuation for soft assistive lower limbs exoskeleton,

    C. Di Natali, A. Sadeghi, A. Mondini, and J. Ortiz, “Pneumatic quasi-passive actuation for soft assistive lower limbs exoskeleton,” Frontiers in Neurorobotics, vol. 14, p. 31, 2020, doi: 10.3389/fnbot.2020.00031

    2020

  33. [40]

    Human-in-the-loop optimization of exoskeleton assistance during walking,

    J. Zhang, P. Fiers, K. Witte, R. Jackson, K. Poggensee, C. Atkeson, and S. Collins, “Human-in-the-loop optimization of exoskeleton assistance during walking,” Science, vol. 356, pp. 1280–1283, 2017, doi: 10.1126/science.aal5054

    2017 doi

  34. [42]

    Suit-type wearable robot powered by shape-memory-alloy- based fabric muscle,

    S. J. Park and C. H. Park, “Suit-type wearable robot powered by shape-memory-alloy- based fabric muscle,” Scientific Reports, vol. 9, 2019, doi: 10.1038/s41598-019-45722- 37 x

    2019 doi

  35. [43]

    Soft sensors and actuators for wearable human–machine interfaces,

    J. Park, Y . Lee, S. Cho, A. Choe, J. Yeom, Y .G. Ro, J. Kim, D. Kang, S. Lee, and H. Ko, “Soft sensors and actuators for wearable human–machine interfaces,” Chemical Reviews, vol. 124, no. 4, pp. 1464–1534, 2024, doi: 10.1021/acs.chemrev.3c00356

    2024 doi

  36. [44]

    Soft mechanical sensors for wearable and implantable applications,

    R. Papani, Y . Li, and S. Wang, “Soft mechanical sensors for wearable and implantable applications,” WIREs Nanomedicine and Nanobiotechnology, vol. 16, p. e1961, 2024, doi: 10.1002/wnan.1961

    2024 doi

  37. [45]

    Soft and stretchable optical waveguide: Light delivery and manipulation at complex biointerfaces,

    C. Wu, X. Liu, and Y . Ying, “Soft and stretchable optical waveguide: Light delivery and manipulation at complex biointerfaces,” ACS Sensors, vol. 6, pp. 1446–1460, 2021, doi: 10.1021/acssensors.0c02566

    2021 doi

  38. [46]

    Sensing and control of a multi-joint soft wearable robot for upper- limb assistance and rehabilitation,

    T. Proietti, C O’Neill, C.J. Hohimer, K. Nuckols, M.E. Clarke, Y .M. Zhou, D. J. Lin, and C.J. Walsh, “Sensing and control of a multi-joint soft wearable robot for upper- limb assistance and rehabilitation,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2381–2388, 2...

    2021

  39. [47]

    Batch fabrication of customizable silicone-textile composite capacitive strain sensors for human motion tracking,

    A. Atalay, V . Sanchez, O. Atalay, and C. J. Walsh, “Batch fabrication of customizable silicone-textile composite capacitive strain sensors for human motion tracking,” Advanced Materials Technologies, vol. 2, p. 1700136, 2017, doi: 10.1002/admt.201700136

    2017 doi

  40. [48]

    A real-time surface EMG decomposition system for non-invasive human–machine interfaces,

    D. Y . Barsakcioglu, M. Bräcklein, A. Holobar, and D. Farina, “A real-time surface EMG decomposition system for non-invasive human–machine interfaces,” IEEE Transactions on Biomedical Circuits and Systems, 2021, doi: 10.1109/BIOCAS.2018.8584659

    2021

  41. [49]

    Washable garment-embedded textile electrodes can measure high-quality surface EMG data across a range of motor tasks,

    N. R. Lyons, M. T. O. Worsey, D. Devaprakash, Y . A. Salchak, D. V . Thiel, S. Canning, D. G. Lloyd, and C. Pizzolato, “Washable garment-embedded textile electrodes can measure high-quality surface EMG data across a range of motor tasks,” IEEE Journal of Biomedical and Health ...

    2023

  42. [50]

    Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove,

    H. K. Yap, A. Mao, J. C. H. Goh, and C.-H. Yeow, “Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove,” in 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob), Singapore, ...

    2016

  43. [51]

    Clinical validation of a wearable ultrasound sensor of blood pressure,

    S. Zhou, G. Park, K. Longardner, M. Lin, B. Qi, X. Yang, X. Gao, H. Huang, X. Chen, Y . Bian, H. Hu, R. S. Wu, W. Yue, M. Li, C. Lu, R. Wang, S. Qin, E. Tasali, T. Karrison, I. Thomas, B. Smarr, E. B. Kistler, B. Al Khiami, I. Litvan, And S. Xu, “Clinical validation of a weara...

    2024 doi

  44. [52]

    Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis,

    W. Gao, S. Emaminejad, H. Y . Y . Nyein, and A. Javey, “Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis,” Nature, vol. 529, pp. 509– 514, 2016, doi: 10.1038/nature16521

    2016 doi

  45. [53]

    Better interaction experience: human–machine interface for soft robotic systems,

    Z. Gao, Z. Liao, and C. Li, “Better interaction experience: human–machine interface for soft robotic systems,” Intelligence & Robotics, vol. 5, no. 3, pp. 520–540, 2025, doi: 10.20517/ir.2025.27

    2025 doi

  46. [54]

    Control of soft wearable robots for elbow assistance and rehabilitation: a simplified solution using disturbance observer and nonsmooth feedback,

    W. Wang, Y . He, F. Li, J. Li, J. Liu, and X. Wu, “Control of soft wearable robots for elbow assistance and rehabilitation: a simplified solution using disturbance observer and nonsmooth feedback,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025, doi: ...

    2025

  47. [55]

    Control strategies for soft robot systems,

    J. Wang and A. Chortos, “Control strategies for soft robot systems,” Advanced Intelligent Systems, vol. 4, 2022, doi: 10.1002/aisy.202100165. 38

    2022 doi

  48. [56]

    IMU-based real-time estimation of gait phase using multi-resolution neural networks,

    L. Tang, M. Shushtari, and A. Arami, “IMU-based real-time estimation of gait phase using multi-resolution neural networks,” Sensors, vol. 24, no. 8, p. 2390, 2024, doi: 10.3390/s24082390

    2024 doi

  49. [57]

    Subject-independent continuous locomotion mode classification for robotic hip exoskeleton applications,

    J. Camargo, D. Molinaro, and A. Young, “Subject-independent continuous locomotion mode classification for robotic hip exoskeleton applications,” IEEE Transactions on Biomedical Engineering, 2021, doi: 10.1109/TBME.2022.3165547

    2021

  50. [58]

    Intention prediction for active upper-limb exoskeletons in industrial applications: a systematic literature review,

    D. Hochreiter, K. Schmermbeck, M. Vazquez-Pufleau, and A. Ferscha, “Intention prediction for active upper-limb exoskeletons in industrial applications: a systematic literature review,” Sensors, vol. 25, no. 17, p. 5225, 2025, doi: 10.3390/s25175225

    2025 doi

  51. [59]

    Experiment-free exoskeleton assistance via learning in simulation,

    S. Luo, M. Jiang, S. Zhang, and H. Su, “Experiment-free exoskeleton assistance via learning in simulation,” Nature, vol. 630, pp. 353–359, 2024, doi: 10.1038/s41586- 024-07382-4

    2024 doi

  52. [60]

    Towards adaptive optimal personalization control of robotic hip exoskeleton assistance for individuals with paretic stroke,

    Q. Zhang, Y . Chen, M. Lewek, J. Si, and H. Huang, “Towards adaptive optimal personalization control of robotic hip exoskeleton assistance for individuals with paretic stroke,” IEEE Transactions on Biomedical Engineering, pp. 1–12, 2026, doi: 10.1109/tbme.2026.3685273

    2026

  53. [61]

    Editorial: Next generation user-adaptive wearable robots,

    T. C. Bulea, N. Sharma, S. Sikdar, and H. Su, “Editorial: Next generation user-adaptive wearable robots,” Frontiers in Robotics and AI, vol. 9, p. 920655, 2022, doi: 10.3389/frobt.2022.920655

    2022

  54. [62]

    Mirror adaptive impedance control of multi-mode soft exoskeleton with reinforcement learning,

    J. Xu, K. Huang, T. Zhang, M. Zhao, A. Ji, and Y . Li, “Mirror adaptive impedance control of multi-mode soft exoskeleton with reinforcement learning,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 6773–6785, 2025, doi: 10.1109/tase.2024.3454444

    2025

  55. [63]

    Scaling wearable foundation models,

    G. Narayanswamy, X. Liu, K. Ayush, Y . Yang, X. Xu, S. Liao, J. Garrison, S. Tailor, J. Sunshine, Y . Liu, T. Althoff, S. Narayanan, P. Kohli, J. Zhan, M. Malhotra, S. Patel, S. Abdel-Ghaffar, and D. McDuff, “Scaling wearable foundation models,” arXiv, p. :2410.13638, 2024, do...

    2024 doi arXiv

  56. [64]

    An overview of wearable haptic technologies and their performance in virtual object exploration,

    M. Van Wegen, J. L. Herder, R. Adelsberger, M. Pastore-Wapp, E. E. H. Van Wegen, S. Bohlhalter, T. Nef, P. Krack, and T. Vanbellingen, “An overview of wearable haptic technologies and their performance in virtual object exploration,” Sensors, vol. 23, no. 3, p. 1563, 2023, doi...

    2023 doi

  57. [65]

    Wearable haptics for virtual reality and beyond,

    A. Frisoli and D. Leonardis, “Wearable haptics for virtual reality and beyond,” Nature Reviews Electrical Engineering, vol. 1, no. 10, pp. 666–679, 2024, doi: 10.1038/s44287-024-00089-8

    2024 doi

  58. [66]

    Design and evaluation of a wearable skin stretch device for haptic guidance,

    F. Chinello, C. Pacchierotti, J. Bimbo, D. Prattichizzo, and M. Malvezzi, “Design and evaluation of a wearable skin stretch device for haptic guidance,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 524–531, 2018, doi: 10.1109/LRA.2017.2766244

    2018

  59. [67]

    What drives acceptance of occupational exoskeletons? Focus group insights from workers in food retail and corporate logistics,

    S. M. Siedl and M. Mara, “What drives acceptance of occupational exoskeletons? Focus group insights from workers in food retail and corporate logistics,” International Journal of Human–Computer Interaction, 2022, doi: 10.1080/10447318.2022.2108969

    2022

  60. [68]

    High-Speed Electrically Actuated Elastomers with Strain Greater Than 100%,

    R. Pelrine, R. Kornbluh, Q. Pei, and J. Joseph, “High-Speed Electrically Actuated Elastomers with Strain Greater Than 100%,” Science, vol. 287, pp. 836–839, 2000, doi: 10.1126/science.287.5454.836

    doi

  61. [69]

    Highly stretchable and tough hydrogels,

    J. Y . Sun, X. Zhao, W. R. K. Illeperuma, and Z. Suo, “Highly stretchable and tough hydrogels,” Nature, vol. 489, pp. 133–136, 2012, doi: 10.1038/nature11409

    2012 doi

  62. [70]

    Strain-programmable fiber-based 39 artificial muscle,

    M. Kanik, S. Orguc, G. Varnavides, and Y . Fink, “Strain-programmable fiber-based 39 artificial muscle,” Science, vol. 365, pp. 145–150, 2019, doi: 10.1126/science.aaw2502

    2019 doi

  63. [71]

    Functional fibers and fabrics for soft robotics, wearables, and human–robot interface,

    J. Xiong, J. Chen, and P. S. Lee, “Functional fibers and fabrics for soft robotics, wearables, and human–robot interface,” Advanced Materials, vol. 33, no. 19, p. 2002640, 2021, doi: 10.1002/adma.202002640

    2021 doi

  64. [72]

    A method for building self- folding machines,

    S. Felton, M. Tolley, E. Demaine, D. Rus, and R. Wood, “A method for building self- folding machines,” Science, vol. 345, pp. 644–646, 2014, doi: 10.1126/science.1252610

    2014 doi

  65. [73]

    Kirigami skins make a simple soft actuator crawl,

    A. Rafsanjani, Y . Zhang, B. Liu, S. M. Rubinstein, and K. Bertoldi, “Kirigami skins make a simple soft actuator crawl,” Science Robotics, vol. 3, p. eaar7555, 2018, doi: 10.1126/scirobotics.aar7555

    2018 doi

  66. [74]

    Embodied auxetic intelligence in a glove-type wearable haptic interface connecting humans to robots and the metaverse,

    S. Oh, J. Jang, W. Ji, C. Majidi, and I. K. Oh, “Embodied auxetic intelligence in a glove-type wearable haptic interface connecting humans to robots and the metaverse,” Advanced Functional Materials, vol. 35, 2025, doi: 10.1002/adfm.202502222

    2025 doi

  67. [75]

    A hybrid plastic- fabric soft bending actuator with reconfigurable bending profiles,

    R. F. Natividad, M. R. Del Rosario, P. C. Y . Chen, and C.-H. Yeow, “A hybrid plastic- fabric soft bending actuator with reconfigurable bending profiles,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore: IEEE, May 2017, pp. 6700–67...

    2017

  68. [76]

    Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides,

    H. Zhao, K. O’Brien, S. Li, and R. F. Shepherd, “Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides,” Science Robotics, vol. 1, p. eaai7529, 2016, doi: 10.1126/scirobotics.aai7529

    2016 doi

  69. [77]

    Self-healing soft pneumatic robots,

    S. Terryn, J. Brancart, D. Lefeber, G. Van Assche, and B. Vanderborght, “Self-healing soft pneumatic robots,” Science Robotics, vol. 2, p. eaan4268, 2017, doi: 10.1126/scirobotics.aan4268

    2017 doi

  70. [78]

    A wearable textile-based pneumatic energy harvesting system for assistive robotics,

    R. A. Shveda, A. Rajappan, and D. J. Preston, “A wearable textile-based pneumatic energy harvesting system for assistive robotics,” Science Advances, vol. 8, p. eabo2418, 2022, doi: 10.1126/sciadv.abo2418

    2022 doi

  71. [79]

    Epidermal electronics,

    D. H. Kim, N. Lu, R. Ma, and J. A. Rogers, “Epidermal electronics,” Science, vol. 333, pp. 838–843, 2011, doi: 10.1126/science.1206157

    2011 doi

  72. [80]

    Pneumatic networks for soft robotics that actuate rapidly,

    B. Mosadegh, P. Polygerinos, C. Keplinger, C. J. Walsh, and G. M. Whitesides, “Pneumatic networks for soft robotics that actuate rapidly,” Advanced Functional Materials, vol. 24, pp. 2163–2170, 2014, doi: 10.1002/adfm.201303288

    2014 doi

  73. [81]

    A fully fabric-based bidirectional soft robotic glove for assistance and rehabilitation of hand impaired patients,

    H. K. Yap, P. M. Khin, T. H. Koh, Y . Sun, X. Liang, J. H. Lim, and C. H. Yeow, “A fully fabric-based bidirectional soft robotic glove for assistance and rehabilitation of hand impaired patients,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1383 –1390, 2017, doi: ...

    2017

  74. [82]

    A Wearable Soft Robotic Exoskeleton for Hip Flexion Rehabilitation,

    T. M. Miller-Jackson, R. F. Natividad, D. Y . L. Lim, L. Hernandez-Barraza, J. W. Ambrose, and R. C.-H. Yeow, “A Wearable Soft Robotic Exoskeleton for Hip Flexion Rehabilitation,” Front. Robot. AI, vol. 9, p. 835237, Apr. 2022, doi: 10.3389/frobt.2022.835237

    2022

  75. [83]

    Hydraulically amplified self-healing electrostatic actuators with muscle-like performance,

    E. Acome, S. K. Mitchell, T. G. Morrissey, and C. Keplinger, “Hydraulically amplified self-healing electrostatic actuators with muscle-like performance,” Science, vol. 359, pp. 61–65, 2018, doi: 10.1126/science.aao6139

    2018 doi

  76. [84]

    Peano- HASEL actuators: muscle-mimetic, electrohydraulic transducers that linearly contract on activation,

    N. Kellaris, V . G. Venkata, G. M. Smith, S. K. Mitchell, and C. Keplinger, “Peano- HASEL actuators: muscle-mimetic, electrohydraulic transducers that linearly contract on activation,” Science Robotics, vol. 3, no. 14, p. eaar3276, 2018, doi: 10.1126/scirobotics.aar3276

    2018 doi

  77. [85]

    Buckling pneumatic linear actuators 40 inspired by muscle,

    D. Yang, M. S. Verma, and G. M. Whitesides, “Buckling pneumatic linear actuators 40 inspired by muscle,” Advanced Materials Technologies, vol. 1, p. 1600055, 2016, doi: 10.1002/admt.201600055

    2016 doi

  78. [86]

    The design and development of a wearable cable-driven shoulder exosuit (CDSE) for multi-DOF upper-limb assistance,

    H. Vatan, T. Theodoridis, G. Wei, Z. Saffari, and W. Holderbaum, “The design and development of a wearable cable-driven shoulder exosuit (CDSE) for multi-DOF upper-limb assistance,” Applied Sciences, vol. 15, no. 19, p. 10673, 2025, doi: 10.3390/app151910673

    2025 doi

  79. [87]

    Development of an active physical interface for physical human-robot interaction,

    C. van Vlerken, F Ballen-Moreno, E. Roels, P. Ferrentino, K. Langlois, B. Vanderborght, and T. Verstraten, “Development of an active physical interface for physical human-robot interaction,” Actuators, vol. 12, no. 6, p. 241, 2023, doi: 10.3390/act12060241

    2023 doi

  80. [88]

    Force Measurement Toward the Instability Theory of Soft Pneumatic Actuators,

    Y . Sun, X. Liang, H. K. Yap, J. Cao, M. H. Ang, and R. C. Hua Yeow, “Force Measurement Toward the Instability Theory of Soft Pneumatic Actuators,” IEEE Robot. Autom. Lett., vol. 2, no. 2, pp. 985–992, Apr. 2017, doi: 10.1109/LRA.2017.2656943

    2017

  81. [89]

    Series elastic actuators,

    G. A. Pratt and M. M. Williamson, “Series elastic actuators,” Proceedings of the IEEE/RSJ IROS, pp. 399–406, 1995, doi: 10.1109/IROS.1995.525827

    1995

  82. [90]

    A lightweight, low-power electroadhesive clutch and spring for exoskeleton actuation,

    S. Diller, C. Majidi, and S. H. Collins, “A lightweight, low-power electroadhesive clutch and spring for exoskeleton actuation,” Proceedings of the IEEE ICRA, pp. 682– 689, 2016, doi: 10.1109/ICRA.2016.7487194

    2016

  83. [91]

    Human augmentation by wearable supernumerary robotic limbs: review and perspectives,

    D. Prattichizzo, M. Pozzi, T. L. Baldi, M. Malvezzi, I. Hussain, S. Rossi, and G. Salvietti, “Human augmentation by wearable supernumerary robotic limbs: review and perspectives,” Progress in Biomedical Engineering, vol. 3, no. 4, p. 042005, 2021, doi: 10.1088/2516-1091/ac2294

    2021 doi

  84. [92]

    Mapping the evidence on occupational exoskeleton use for the workforce in healthcare, social care, and industry: a systematic scoping review,

    S. Bhat, J. Gavin, M. Warner, and M. Myall, “Mapping the evidence on occupational exoskeleton use for the workforce in healthcare, social care, and industry: a systematic scoping review,” Wearable Technologies, vol. 6, p. e53, 2025, doi: 10.1017/wtc.2025.10033

    2025

  85. [93]

    Evaluation of the physiological benefits of a passive back-support exoskeleton during lifting and working in forward leaning postures,

    R. M. Van Sluijs, M. Wehrli, A. Brunner, and O. Lambercy, “Evaluation of the physiological benefits of a passive back-support exoskeleton during lifting and working in forward leaning postures,” Journal of Biomechanics, vol. 149, p. 111489, 2023, doi: 10.1016/j.jbiomech.2023.111489

    2023

  86. [94]

    Lockable lower-limb exoskeleton based on a novel variable-stiffness joint: reducing physical fatigue at squatting,

    Z. Ma, J. Liu, G. Ma, J. Gao, B. Chen, and S. Zuo, “Lockable lower-limb exoskeleton based on a novel variable-stiffness joint: reducing physical fatigue at squatting,” ASME Journal of Mechanisms and Robotics, vol. 15, no. 5, p. 051008, 2023, doi: 10.1115/1.4055964

    2023 doi

  87. [95]

    Thermophysiological aspects of wearable robotics: challenges and opportunities,

    K. Rykaczewski, “Thermophysiological aspects of wearable robotics: challenges and opportunities,” Temperature, vol. 10, pp. 313–325, 2022, doi: 10.1080/23328940.2022.2113725

    2022

  88. [96]

    WearaCob: a unified bidirectional framework for adaptive synergy between wearable and collaborative robotics,

    S. Ferrari, I. Terrile, F. Missiroli, F. Masiero, M. Casadio, and L. Masia, “WearaCob: a unified bidirectional framework for adaptive synergy between wearable and collaborative robotics,” IEEE Robotics and Automation Letters, vol. 11, no. 4, pp. 5222 –5229, 2026, doi: 10.1109/...

    2026

  89. [97]

    A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking,

    F. A. Panizzolo, I. Galiana, A. T. Asbeck, and C. J. Walsh, “A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking,” Journal of NeuroEngineering and Rehabilitation, vol. 13, p. 43, 2016, doi: 10.1186/s12984-016- 0150-9

    2016 doi

  90. [98]

    Optimized hip-knee-ankle exoskeleton assistance 41 reduces the metabolic cost of walking with worn loads,

    G. M. Bryan, P. W. Franks, S. Song, A. S. V oloshina, R. Reyes, M. P. O’Donovan, K. N. Gregorczyk, and S. H. Collins, “Optimized hip-knee-ankle exoskeleton assistance 41 reduces the metabolic cost of walking with worn loads,” Journal of NeuroEngineering and Rehabilitation, vol...

    2021 doi

  91. [99]

    Review of wearable robotic assistive devices for integration with pressure suit arms,

    A. Salmoiraghi and D. Akin, “Review of wearable robotic assistive devices for integration with pressure suit arms,” 42nd International Conference on Environmental Systems, 2012, doi: 10.2514/6.2012-3487

    2012 doi

  92. [100]

    The ReWalk ReStore soft robotic exosuit: a multi-site clinical trial of exosuit-augmented post-stroke gait rehabilitation,

    L. N. Awad, A. Esquenazi, G. E. Francisco, K. J. Nolan, and A. Jayaraman, “The ReWalk ReStore soft robotic exosuit: a multi-site clinical trial of exosuit-augmented post-stroke gait rehabilitation,” Journal of NeuroEngineering and Rehabilitation, vol. 17, p. 80, 2020, doi: 10....

    2020 doi

  93. [101]

    Effects of soft robotic exoskeleton for gait training on clinical and biomechanical gait outcomes in sub-acute stroke: a randomized controlled pilot study,

    R. Xie, Y . Zhang, H. Jin, F. Yang, Y . Feng, and Y . Pan, “Effects of soft robotic exoskeleton for gait training on clinical and biomechanical gait outcomes in sub-acute stroke: a randomized controlled pilot study,” Frontiers in Neurology, vol. 14, p. 1296102, 2023, doi: 10.3...

    2023

  94. [102]

    Ankle-targeted exosuit resistance increases paretic propulsion in people post-stroke,

    K. Swaminathan, F. Porciuncula, S. Park, H. Kannan, J. Erard, N. Wendel, T. Baker, T. D. Ellis, L. N. Awad, and C. J. Walsh, “Ankle-targeted exosuit resistance increases paretic propulsion in people post-stroke,” Journal of NeuroEngineering and Rehabilitation, vol. 20, p. 81, ...

    2023 doi

  95. [103]

    Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke,

    J. Hidler, D. Nichols, M. Pelliccio, K. Brady, D. D. Campbell, J. H. Kahn, and T. G. Hornby, “Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke,” Neurorehabilitation and Neural Repair, vol. 23, no. 1, pp. 5–13, 2009, doi: 10.1...

    2009 doi

  96. [104]

    A lower-extremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy,

    Z. F. Lerner, D. L. Damiano, and T. C. Bulea, “A lower-extremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy,” Science Translational Medicine, vol. 9, p. eaam9145, 2017, doi: 10.1126/scitranslmed.aam9145

    2017 doi

  97. [105]

    Exercise with a wearable hip- assist robot improved physical function and walking efficiency in older adults,

    S.-H. Lee, J. Kim, B. Lim, H.-J. Lee, and Y .-H. Kim, “Exercise with a wearable hip- assist robot improved physical function and walking efficiency in older adults,” Scientific Reports, vol. 13, p. 5071, 2023, doi: 10.1038/s41598-023-32335-8

    2023 doi

  98. [106]

    Brain- computer interface-controlled exoskeleton training for lower-limb rehabilitation in spinal cord injury: a pilot randomized clinical trial,

    X. Hu, N. Li, M. Pang, S. Bai, J. Mo, S. Yao, Y . Lu, M. Huang, J. Di, Y . Kang, J. Tang, H. Zhang, T. Zhao, J. He, L. He, R. Xie, B. Liu, G. Xu, X. Hu, and L. Rong, “Brain- computer interface-controlled exoskeleton training for lower-limb rehabilitation in spinal cord injury:...

    2026 doi

  99. [107]

    Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis,

    K. C. Tseng, L. Wang, C. Hsieh, and A. M. Wong, “Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis,” Annals of Medicine, vol. 56, no. 1, p. 2337735, 2024, doi: 10.1080/07853890.2024.2337735

    2024

  100. [108]

    Assisting hand function after spinal cord injury with a fabric-based soft robotic glove,

    L. Cappello, J. T. Meyer, K. C. Galloway, and C. J. Walsh, “Assisting hand function after spinal cord injury with a fabric-based soft robotic glove,” Journal of NeuroEngineering and Rehabilitation, vol. 15, p. 59, 2018, doi: 10.1186/s12984-018- 0391-x

    2018 doi

  101. [109]

    Home rehabilitation supported by a wearable soft-robotic device for improving hand function in older adults: a pilot randomized controlled trial,

    B. Radder, G. B. Prange-Lasonder, A. I. R. Kottink, J. Holmberg, K. Sletta, M. van Dijk, T. Meyer, A. Melendez-Calderon, J. H. Buurke, and J. S. Rietman, “Home rehabilitation supported by a wearable soft-robotic device for improving hand function in older adults: a pilot rando...

    2019 doi

  102. [110]

    A bidirectional fabric-based soft robotic glove for hand function assistance in patients with chronic stroke,

    D. Y .-L. Lim, H.-S. Lai, and R. C.-H. Yeow, “A bidirectional fabric-based soft robotic glove for hand function assistance in patients with chronic stroke,” J NeuroEngineering Rehabil, vol. 20, no. 1, p. 120, Sep. 2023, doi: 10.1186/s12984-023- 42 01250-4

    2023 doi

  103. [111]

    Wearable neurotechnology systems for upper extremity rehabilitation in children with cerebral palsy: a scoping review,

    S. J. Burchfield, A. Shierk, C. Truong, and R. Blankenship, “Wearable neurotechnology systems for upper extremity rehabilitation in children with cerebral palsy: a scoping review,” Frontiers in Neurology, vol. 16, p. 1663596, 2025, doi: 10.3389/fneur.2025.1663596

    2025

  104. [112]

    Wearable robotics for impaired upper-limb assistance and rehabilitation: State of the art and future perspectives,

    T. Proietti, E. Ambrosini, A. Pedrocchi, and S. Micera, “Wearable robotics for impaired upper-limb assistance and rehabilitation: State of the art and future perspectives,” IEEE Access, vol. 10, pp. 106117–106134, 2022, doi: 10.1109/ACCESS.2022.3210514

    2022

  105. [113]

    An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation,

    D. Leonardis, C. Chisari, M. Bergamasco, A. Frisoli, M. Barsotti, C. Loconsole, M. Solazzi, M. Troncossi, C. Mazzotti, V . P. Castelli, C. Procopio, and G. Lamola, “An EMG-controlled robotic hand exoskeleton for bilateral rehabilitation,” IEEE Transactions on Haptics, vol. 8, ...

    2015

  106. [114]

    Effects of a soft robotic hand for hand rehabilitation in chronic stroke survivors,

    X. Q. Shi, H. L. Heung, Z.Q. Tang, Z. Li, and K. Y . Tong, “Effects of a soft robotic hand for hand rehabilitation in chronic stroke survivors,” Journal of Stroke and Cerebrovascular Diseases, 2021, doi: 10.1016/j.jstrokecerebrovasdis.2021.105812

    2021

  107. [115]

    Home- based hand rehabilitation with a robotic glove in hemiplegic patients after stroke: a pilot feasibility study,

    P. Bernocchi, C. Mulè, F. Vanoglio, G. Taveggia, A. Luisa, and S. Scalvini, “Home- based hand rehabilitation with a robotic glove in hemiplegic patients after stroke: a pilot feasibility study,” Topics in Stroke Rehabilitation, vol. 25, no. 2, pp. 114–119, 2018, doi: 10.1080/1...

    2018

  108. [116]

    A scoping review of human digital twins in healthcare applications and usage patterns,

    B. H. Tudor, R. Shargo, G. M. Gray, and L. M. Ahumada, “A scoping review of human digital twins in healthcare applications and usage patterns,” npj Digital Medicine, vol. 8, no. 1, p. 587, 2025, doi: 10.1038/s41746-025-01910-w

    2025 doi

  109. [117]

    Digital twin rehabilitation system based on self-balancing lower limb exoskeleton,

    W. Wang, Y . He, F. Li, J. Li, J. Liu, and X. Wu, “Digital twin rehabilitation system based on self-balancing lower limb exoskeleton,” Technology and Health Care, 2022, doi: 10.3233/THC-220087

    2022 doi

  110. [118]

    OpenSim: open-source software to create and analyze dynamic simulations of movement,

    S. L. Delp, F. C. Anderson, A. S. Arnold, and D. G. Thelen, “OpenSim: open-source software to create and analyze dynamic simulations of movement,” IEEE Transactions on Biomedical Engineering, vol. 54, no. 11, pp. 1940–1950, 2007, doi: 10.1109/TBME.2007.901024

    1940

  111. [119]

    Robust real-time musculoskeletal modeling driven by electromyograms,

    G. Durandau, D. Farina, and M. Sartori, “Robust real-time musculoskeletal modeling driven by electromyograms,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 3, pp. 556–564, 2018, doi: 10.1109/TBME.2017.2704085

    2018

  112. [120]

    Development of a personalized digital biomarker of vaccine-associated reactogenicity using wearable sensors and digital twin technology,

    S. R. Steinhubl, J. Sekaric, M. Gendy, and S. Wegerich, “Development of a personalized digital biomarker of vaccine-associated reactogenicity using wearable sensors and digital twin technology,” Communications Medicine, vol. 5, 2025, doi: 10.1038/s43856-025-00840-8

    2025 doi

  113. [121]

    Implementing ethical, legal, and societal considerations in wearable robot design,

    A. Kapeller, H. Felzmann, E. Fosch-Villaronga, K. Nizamis, and A. M. Hughes, “Implementing ethical, legal, and societal considerations in wearable robot design,” Applied Sciences, vol. 11, no. 15, p. 6705, 2021, doi: 10.3390/app11156705

    2021 doi

  114. [122]

    Privacy, ethics, transparency, and accountability in AI systems for wearable devices,

    P. Radanliev, “Privacy, ethics, transparency, and accountability in AI systems for wearable devices,” Frontiers in Digital Health, vol. 7, p. 1431246, 2025, doi: 10.3389/fdgth.2025.1431246

    2025

  115. [123]

    Privacy and regulatory issues in wearable health technology,

    R. Bouderhem, “Privacy and regulatory issues in wearable health technology,” Engineering Proceedings (ECSA-10), vol. 87, 2023, doi: 10.3390/ecsa-10-16206

    2023 doi

  116. [124]

    Bystander privacy implications of robots in everyday spaces: a scoping review,

    M. Dietrich, A. Sarkisian, and T. H. Weisswange, “Bystander privacy implications of robots in everyday spaces: a scoping review,” Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 196–205, 2026, doi: 43 10.1145/3757279.3785586

    2026

  117. [125]

    Personalized health monitoring using explainable AI: bridging trust in predictive healthcare,

    M. S. Vani, R. V . Sudhakar, A. Mahendar, S. Ledalla, M. Radha, and M. Sunitha, “Personalized health monitoring using explainable AI: bridging trust in predictive healthcare,” Scientific Reports, vol. 15, 2025, doi: 10.1038/s41598-025-15867-z

    2025 doi

  118. [126]

    A survey on security and privacy issues in wearable health monitoring devices,

    B. Zhang, C. Chen, I. Lee, K. Lee, and K. L. Ong, “A survey on security and privacy issues in wearable health monitoring devices,” Computers & Security, vol. 155, p. 104453, 2025, doi: 10.1016/j.cose.2025.104453

    2025

  119. [127]

    A privacy-preserving framework for federated learning in smart healthcare systems,

    W. Wang, X. Li, X. Qiu, X. Zhang, V . Brusic, and J. Zhao, “A privacy-preserving framework for federated learning in smart healthcare systems,” Information Processing & Management, vol. 60, no. 1, p. 103167, 2023, doi: 10.1016/j.ipm.2022.103167

    2023

  120. [128]

    Benchmarking wearable robots: challenges and recommendations from functional, user experience, and methodological perspectives,

    D. Torricelli, C. Rodriguez-Guerrero, J.F. Veneman, S. Crea, K. Briem, B. Lenggenhager, and P. Beckerle, “Benchmarking wearable robots: challenges and recommendations from functional, user experience, and methodological perspectives,” Frontiers in Robotics and AI, vol. 7, p. 5...

    2020

  121. [129]

    A critical review on factors affecting the user adoption of wearable and soft robotics,

    B. W. K. Ang, C. H. Yeow, and J. H. Lim, “A critical review on factors affecting the user adoption of wearable and soft robotics,” Sensors, vol. 23, no. 6, p. 3263, 2023, doi: 10.3390/s23063263

    2023 doi

  122. [130]

    Biomechanical energy harvesting: generating electricity during walking with minimal user effort,

    J. M. Donelan, Q. Li, V . Naing, J. A. Hoffer, D. J. Weber, and A. D. Kuo, “Biomechanical energy harvesting: generating electricity during walking with minimal user effort,” Science, vol. 319, pp. 807–810, 2008, doi: 10.1126/science.1149860

    2008 doi

  123. [131]

    An integrated design and fabrication strategy for entirely soft, autonomous robots,

    M. Wehner, R. L. Truby, D. J. Fitzgerald, J. A. Lewis, and R. J. Wood, “An integrated design and fabrication strategy for entirely soft, autonomous robots,” Nature, vol. 536, pp. 451–455, 2016, doi: 10.1038/nature19100

    2016 doi

  124. [132]

    The global importance of machine learning-based wearables and digital twins for rehabilitation,

    M. Piechowiak, A. Goch, E. Panas, J. Masiak, D. Mikolajewski, I. Rojek, and E. Mikolajewska, “The global importance of machine learning-based wearables and digital twins for rehabilitation,” Electronics, vol. 14, no. 23, p. 4699, 2025, doi: 10.3390/electronics14234699

    2025 doi

  125. [133]

    HAL: Hybrid assistive limb based on cybernics,

    Y . Sankai, “HAL: Hybrid assistive limb based on cybernics,” Robotics Research (ISRR), Springer Tracts in Advanced Robotics, vol. 66, 2010, doi: 10.1007/978-3-642- 14743-2_3

    2010 doi

  126. [134]

    Soft robotics for personalized and sustainable wearables,

    A. van Oosterhout, M. A. Robertson, and J. Paik, “Soft robotics for personalized and sustainable wearables,” Nature Reviews Bioengineering, vol. 4, no. 1, pp. 30–46, 2025, doi: 10.1038/s44222-025-00359-6. 44 Supplementary Material Table S1. Top 50 Open Research Questions in So...

    2025 doi

  127. [135]

    mechanical transparency

    What standardized metric best captures “mechanical transparency” during prolonged, multi-task daily use rather than in a single instrumented session?

  128. [136]

    How can pressure at soft-tissue anchoring points be distributed to prevent injury without sacrificing force transmission?

  129. [137]

    What thermal-management strategies allow all-day wear of fluidic and electroactive wearables under occupational heat load?

  130. [138]

    How should donning and doffing burden be quantified and bounded for unsupervised home use?

  131. [139]

    What objective markers predict long-term device abandonment from short-term wear data?

  132. [140]

    How does social acceptability of visible wearables vary across age, culture, and setting, and how can design reduce stigma?

  133. [141]

    What is the minimal interface footprint that preserves assistance while restoring natural freedom of movement?

  134. [142]

    How can comfort be co-optimized with actuation efficiency rather than traded against it?

  135. [143]

    What materials provide skin biocompatibility over months of continuous contact and perspiration?

  136. [144]

    How should wearability be specified as a system-level requirement that constrains actuation, sensing, and control jointly? Cluster B: Energy and Untethered Systems [78], [130]

  137. [145]

    What power-source-to-assistance ratio defines a practically untethered multi-joint wearable?

  138. [146]

    How can actuator efficiency be improved enough to make all-day untethered fluidic assistance feasible?

  139. [147]

    To what extent can biomechanical and textile energy harvesting offset on-board power demand during real activity?

  140. [148]

    How should assistance strategies be designed to minimize energy per unit of delivered benefit?

  141. [149]

    What control architectures reduce sensing and computation energy without degrading adaptation?

  142. [150]

    How can on-board control and power be embodied softly without reintroducing rigid components?

  143. [151]

    What duty-cycle models best predict real-world battery life across heterogeneous daily use?

  144. [152]

    How can self-powering subsystems be integrated without compromising comfort or washability?

  145. [153]

    What is the achievable mass budget for an untethered exosuit that assists multiple 45 joints?

  146. [154]

    How should energy autonomy be benchmarked across devices with different assistance goals? Cluster C: Sensing, AI, and Reliable Adaptation

  147. [155]

    How can intent detection remain robust to sensor-placement change and signal drift across days?

  148. [156]

    What validation protocols establish reliability across users, tasks, and contexts rather than within one cohort?

  149. [157]

    How should adaptive controllers detect and fail safely when their state estimates degrade?

  150. [158]

    What level of explainability is required for users versus clinicians versus regulators of adaptive wearables?

  151. [159]

    How can multimodal fusion be made resilient to the loss or corruption of individual sensing channels?

  152. [160]

    What metrics distinguish genuine longitudinal adaptation from short-term performance fluctuation?

  153. [161]

    How can learning-based estimation generalize across body types and movement styles with limited per-user data?

  154. [162]

    What is the safe envelope of control authority for an artificial-intelligence-driven wearable in unstructured daily life?

  155. [163]

    How can model updates after deployment be validated without re-running full clinical evaluation?

  156. [164]

    What shared benchmark tasks would make intelligent-wearable results comparable across laboratories? Cluster D: Digital Twins and Personalization [132]

  157. [165]

    How should digital-twin fidelity be validated for wearable assistance rather than for clinical diagnosis?

  158. [166]

    How can assistance be personalized with under five minutes of calibration while remaining robust across days?

  159. [167]

    What minimal sensing set supports a useful adaptive twin without intrusive data collection?

  160. [168]

    How can twin submodels operating at different timescales be kept interoperable and synchronized?

  161. [169]

    What update-reliability guarantees are needed before a twin may influence control or therapy decisions?

  162. [170]

    How can high-fidelity personalization be delivered without amplifying inequity across resourced and under-resourced users?

  163. [171]

    What are appropriate quality metrics for a closed-loop wearable digital twin?

  164. [172]

    How can twins generalize a person’s model across changing tasks, contexts, and recovery states?

  165. [173]

    What governance keeps a person’s twin private while still permitting model improvement?

  166. [174]

    How can predictive intervention be evaluated for benefit and safety before routine deployment? 46 Cluster E: Translation, Benchmarking, and Governance [123], [128]

  167. [175]

    What shared outcome set would harmonize clinical and wearable-specific endpoints across trials?

  168. [176]

    How should comfort, don/doff burden, and real-world use time be standardized as reportable trial endpoints?

  169. [177]

    What multicenter study designs are feasible given the heterogeneity of wearable systems?

  170. [178]

    How should adaptive, post-deployment-updating devices be regulated across their lifecycle?

  171. [179]

    What health-economic evidence would justify reimbursement for soft wearable assistance?

  172. [180]

    How can benchmarking account for task, user, interaction mode, and adaptation strategy simultaneously?

  173. [181]

    What implementation-science models best fit wearable robots into therapist and home workflows?

  174. [182]

    How should governance handle the dual-use tension between workplace safety monitoring and surveillance?

  175. [183]

    What risk-tiered framework appropriately classifies body-coupled, data-rich, adaptive wearables?

  176. [184]

    What certification would evaluate longitudinal use quality, not only instantaneous actuator output? 47 Table S2. Representative Device-by-Device Timeline of Wearable Robotics Era Representative system Contribution I: Mechanical assistance (≈1960–2000) MIT-Manus [10] End-effect...

    1960

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.