Pith. sign in

REVIEW 3 major objections 3 minor 65 references

Technology-assisted Personalized Yoga for Better Health -- Challenges and Outlook

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This vision paper argues that yoga personalization is best modeled as a three-phase decision-support problem over a large set of practice 'items'.

desk verdict The submission is broken: the full text is a different paper, so the yoga paper exists only as an unverifiable abstract; the drift paper actually included is a solid but irrelevant empirical study. read the letter →

arxiv 2508.18283 v1 pith:JLH3QV5Q submitted 2025-08-15 cs.HC cs.AIcs.LG

Vivek Kumar , Himanshu Sahu , Hari Prabhat Gupta , Biplav Srivastava This is my paper
classification cs.HCcs.AIcs.LG
keywords yogapersonalizationdecisionsupportposerecommendationSuryaNamaskarwell-beingvisionpaperadaptivehealthsystemshuman-computerinteraction
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

The paper tries to establish that personalized yoga is not a content-delivery problem but a decision-support problem: a person must first discover their subset from the large, interdependent set of yoga practices, then keep following that subset as their abilities and goals change, and finally adapt to alternative items when health or environment shifts. It introduces the term 'items' for executable actions such as postures, breath exercises, and meditation, and argues that the full pipeline—from pose sensing to correction recommendation for a complete regimen—can be examined under one framework. The authors claim this is the first comprehensive treatment of yoga personalization from this decision-support perspective, and illustrate the framing with a case study of Surya Namaskar, a fixed sequence of twelve choreographed poses. If the framework holds, it would organize future work in yoga technology around a common three-part model rather than isolated sensing or recommendation efforts.

What carries the argument

The central object is the 'item'—an executable yoga action such as a physical pose, breath exercise, or meditation—embedded in a large, interdependent practice space. The carrying mechanism is the three-phase personalization model: discover (select the fitting subset), follow (sustain engagement as abilities and goals drift), and adapt (swap in alternatives when health or environment changes). The Surya Namaskar case study serves as the concrete instance: a choreographed set of twelve interdependent poses that illustrates how the three phases play out in a real regimen.

What would settle it

Run a several-week trial in which users with similar goals follow either an algorithmically selected subset, an expert-chosen regimen, or a random subset, tracking both adherence and a chosen well-being metric: if the algorithmic subset shows no measurable advantage over the random or expert baseline, the decision-support optimization is not delivering its promised benefit. A more direct falsifier would show that the inter-dependencies among postures in Surya Namaskar cannot be represented without contradicting expert sequencing rules, which would break the item-set model at its core.

Watch

Extended reading notes

Core claim

The paper proposes that the problem of personalized yoga be reformulated as a decision-support problem over a finite set of executable 'items'—physical postures, breathing techniques, and meditative practices—that have interdependencies. Personalized benefit requires (a) discovering the subset of items suited to a person's needs, (b) continuing to follow those items with interest adjusted to changing abilities and near-term objectives, and (c) adapting to alternative items as the environment and the person's health change. The paper sketches a preliminary approach spanning pose sensing, user modeling, and recommendation of corrections for a complete regimen, and demonstrates the framing on S

Load-bearing premise

The whole agenda depends on yoga practice being representable as a finite set of executable items with tractable inter-dependencies, and on well-being benefits being measurable enough that the best subset can be algorithmically identified from person data.

Editorial extensions

If this is right

  • If the framework is accepted, yoga technology work gains a shared vocabulary of discover, follow, and adapt, giving structure to both sensing and recommendation research.
  • The full pipeline—from detecting how a pose is executed to recommending corrections for a complete daily regimen—becomes a single decision-support problem rather than a set of disconnected tools.
  • Surya Namaskar, with its fixed twelve-pose structure and documented inter-dependencies, becomes a natural benchmark for evaluating personalized yoga algorithms.
  • Personalization is reframed as ongoing adaptation over time, not a one-time selection, so systems must track changes in ability, interest, and health conditions.
  • Realizing the vision requires combining sensing, machine learning, and human factors expertise across the entire pipeline.

Reading between the lines

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

  • Inference: the discover-follow-adapt loop structurally resembles a sequential recommendation or subscription problem, so existing recommender-system and bandit algorithms could be applied once items and benefit measures are made explicit—this mapping is not made in the paper itself.
  • Inference: if well-being outcomes remain difficult to measure, adherence rates and posture-execution quality could serve as practical proxy targets, which would shift the optimization objective without abandoning the three-phase model.
  • Inference: the same item-set framing could transfer to other structured movement disciplines such as physical therapy sequences or Tai Chi forms, giving the vision reach beyond yoga into adjacent personal-health decision-support domains.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 3 minor

Summary. The submission, arXiv:2508.18283, is represented only by an abstract for a vision paper on personalized yoga decision support. The abstract states that yoga practice comprises a large set of 'items' (executable actions such as poses and breath exercises), and argues that personalization requires (a) discovering one's subset from an interdependent set, (b) sustaining engagement as abilities and near-term objectives change, and (c) adapting to alternative items as environment and health conditions change. It claims to be the first comprehensive treatment of decision-support issues for Yoga personalization, from pose sensing to recommendation of corrections, illustrated with a Surya Namaskar case study. The full text supplied, however, is arXiv:2508.18284v1, 'Multi-Modal Drift Forecasting of Leeway Objects via Navier-Stokes-Guided CNN and Sequence-to-Sequence Attention-Based Models', an unrelated paper by a different author group on maritime drift forecasting. The yoga manuscript therefore cannot be reviewed as submitted: the claims in the abstract are the only in-scope content, and they are unsupported by any body text, related-work analysis, method description, or case study.

Significance. The three-part abstraction (discover, follow, adapt) is a potentially useful organizing frame for an under-structured applied HCI / health-informatics area, and a comprehensive vision paper mapping sensing, recommendation, and correction problems for Yoga personalization would fill a real gap. If the actual paper delivers what the abstract promises, it could usefully structure subsequent work. However, in the submitted form nothing beyond the abstract is available: there is no method, system, data, evaluation, or prior-art analysis, and the supplied full text addresses an unrelated topic. The 'first comprehensive' claim is asserted without substantiation. In this state, the contribution's significance is unassessable.

major comments (3)
  1. [Full text (pages 1-43)] The submitted full text is arXiv:2508.18284v1, 'Multi-Modal Drift Forecasting of Leeway Objects via Navier-Stokes-Guided CNN and Sequence-to-Sequence Attention-Based Models', which is unrelated to Yoga personalization in topic, authorship, and contribution. The only in-scope material is the one-paragraph abstract. Every load-bearing element of the central claim—comprehensive coverage of decision-support issues, the Surya Namaskar case study, and the 'first' status—is therefore unverifiable. This is a missing-support problem, not a matter of interpretation or consensus, and it prevents review of the claimed contribution.
  2. [Abstract ('first comprehensive' claim)] The abstract's assertion that this is 'the first paper that comprehensively examines decision support issues around Yoga personalization' is a strong priority claim. It is not accompanied by any cited prior work, comparison, or literature assessment. Even for a vision paper, a claim of firstness requires at least a structured related-work discussion to delimit what 'comprehensive' means and what prior systems/approaches are being distinguished from. The submitted manuscript provides no basis for checking this claim.
  3. [Abstract (three-part problem formulation)] The formulation in sentences (a)-(c) presupposes that Yoga practice can be represented as a finite set of 'items' with tractable inter-dependencies and that a person's well-being benefit is measurable enough for a best subset to be identifiable algorithmically. The abstract does not state the objective function, the assumed input data, or the formalization of inter-dependencies. A vision paper may pose an ill-posed problem as a research challenge, but the full text is needed to see whether the authors acknowledge and address the measurement and formalization risks. In this submission, that discussion is absent.
minor comments (3)
  1. [Abstract (terminology)] The phrase 'our term for executable actions' is informal; if the paper is revised, a precise definition of 'item' with examples and a formal notation would strengthen the framing.
  2. [Abstract (case study)] The abstract mentions a Surya Namaskar case study (12 choreographed poses) but gives no hint of what the case study demonstrates or what data it uses. If the actual paper is resubmitted, the abstract should include one or two concrete outcomes of the case study.
  3. [Submission metadata] The mismatch between arXiv:2508.18283 and the supplied full-text arXiv:2508.18284v1 suggests an upload or manuscript-assembly error. The authors should be asked to confirm the correct full text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation present; abstract contains no equations, no fitted parameters, and no self-citation chain. Full-text mismatch prevents verification but is a missing-support issue, not circularity.

full rationale

The submitted full text (arXiv:2508.18284) is a different paper on maritime drift forecasting, so the claimed yoga personalization paper is represented only by its abstract. Within the abstract there is no derivation chain: no equations, no fitted parameters, no predictions computed from data, and no load-bearing self-citation. The three-part problem statement (discover, follow, adapt) is a framing argument, not a result derived from its own premise; it asserts needs rather than reducing one quantity to another. The 'first comprehensive examination' claim is a novelty assertion that is unverifiable without the full text and prior-art survey, but an unverifiable novelty claim is a missing-support/correctness problem, not circularity. No step in the abstract is equivalent by construction to its inputs, and no cited prior work is invoked to force a conclusion. Accordingly the circularity score is 0, with the caveat that the paper's central claim cannot be checked from the available material.

Assumptions & free parameters 0 free parameters · 3 assumptions · 1 invented entities

The abstract supplies no equations, data, or fitted numbers, so the ledger contains no free parameters. The central claims rest on three domain assumptions: that yoga decomposes into a formalizable item space, that well-being outcomes are measurable and optimizable, and that the paper is genuinely first in its claimed niche. All three are asserted rather than evidenced in the abstract. The supplied full text is a different paper, so it cannot be used to populate this ledger for the claimed submission.

assumptions (3)
  • domain assumption Yoga practice can be decomposed into a finite set of 'items' (executable actions such as postures and breathing exercises) with inter-dependencies that a decision-support system can exploit.
    Abstract sentence 2 defines 'items'; the personalization framing in the following sentence requires this decomposition to be tractable.
  • domain assumption A person's well-being benefit from yoga is measurable enough that the best subset of items can be identified algorithmically from person-specific data.
    The three needs (discover, follow, adapt) are only optimizable if outcomes are observable; the abstract gives no evidence for this.
  • domain assumption No prior work comprehensively examines decision support for yoga personalization.
    Supports the 'first comprehensive' claim in the abstract's final sentence; the abstract cites no prior literature, so the premise is unverifiable.
invented entities (1)
  • 'items' (the paper's term for executable yoga actions)
    purpose: Foundational unit of the proposed personalization model: the space over which discovery, adherence, and adaptation are defined.
    Introduced as a defined term in the abstract's second sentence; it is a terminology coinage, not an externally testable entity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Technology-assisted Personalized Yoga for Better Health -- Challenges and Outlook." pith.science (2026). https://pith.science/paper/JLH3QV5Q

@misc{pith2026250818283,
  author       = {Pith},
  title        = {Pith review of: Technology-assisted Personalized Yoga for Better Health -- Challenges and Outlook},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JLH3QV5Q}},
  note         = {Machine review of arXiv:2508.18283}
}
read the original abstract

Yoga is a discipline of physical postures, breathing techniques, and meditative practices rooted in ancient Indian traditions, now embraced worldwide for promoting overall well-being and inner balance. The practices are a large set of items, our term for executable actions like physical poses or breath exercises, to offer for a person's well-being. However, to get benefits of Yoga tailored to a person's unique needs, a person needs to (a) discover their subset from the large and seemingly complex set with inter-dependencies, (b) continue to follow them with interest adjusted to their changing abilities and near-term objectives, and (c) as appropriate, adapt to alternative items based on changing environment and the person's health conditions. In this vision paper, we describe the challenges for the Yoga personalization problem. Next, we sketch a preliminary approach and use the experience to provide an outlook on solving the challenging problem using existing and novel techniques from a multidisciplinary computing perspective. To the best of our knowledge, this is the first paper that comprehensively examines decision support issues around Yoga personalization, from pose sensing to recommendation of corrections for a complete regimen, and illustrates with a case study of Surya Namaskar -- a set of 12 choreographed poses.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

65 extracted references · 57 canonical work pages

  1. [1]

    Scenario analysis and disaster preparedness for port and maritime logistics risk management,

    J. Kwesi-Buor, D. A. Menachof, and R. Talas, “Scenario analysis and disaster preparedness for port and maritime logistics risk management,” Accident Analysis & Prevention , vol. 123, pp. 433–447, 2019

    work page 2019

  2. [2]

    A marine accident analysing model to evaluate potential operational causes in cargo ships,

    E. Akyuz, “A marine accident analysing model to evaluate potential operational causes in cargo ships,” Safety science , vol. 92, pp. 17–25, 2017

    work page 2017

  3. [3]

    A novel optimal route planning algorithm for searching on the sea,

    Y . Yang, Y . Mao, R. Xie, Y . Hu, and Y . Nan, “A novel optimal route planning algorithm for searching on the sea,” The Aeronautical Journal, vol. 125, no. 1288, pp. 1064–1082, 2021

    work page 2021

  4. [4]

    Validation of opendrift-based drifter trajectory prediction technique for maritime search and rescue,

    J.-C. Kim, H. Dae, J.-e. Sim, Y .-T. Son, K.-Y . Bang, S. Shin et al. , “Validation of opendrift-based drifter trajectory prediction technique for maritime search and rescue,” Journal of Ocean Engineering and Technology , vol. 37, no. 4, pp. 145–157, 2023

    work page 2023

  5. [5]

    On predicting boat drift for search and rescue,

    Z. Ni, Z. Qiu, and T. Su, “On predicting boat drift for search and rescue,” Ocean Engineering , vol. 37, no. 13, pp. 1169–1179, 2010. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0029801810001320

    work page 2010

  6. [6]

    Research on the drift prediction of marine floating debris: A case study of the south China sea maritime drift experiment,

    L. Mu, H. Tu, X. Geng, F. Qiao, Z. Chen, S. Jia, R. Zhu, T. Zhang, and Z. Chen, “Research on the drift prediction of marine floating debris: A case study of the south China sea maritime drift experiment,” Journal of Marine Science and Engineering, vol. 12, no. 2, 2024. [Online]. Available: https://www.mdpi.com/2077-1312/12/2/357

    work page 2024

  7. [7]

    How winds and ocean currents influence the drift of floating objects,

    T. J. W. Wagner, I. Eisenman, A. M. Ceroli, and N. C. Constantinou, “How winds and ocean currents influence the drift of floating objects,” Journal of Physical Oceanography , vol. 52, no. 5, pp. 907 – 916, 2022. [Online]. Available: https://journals.ametsoc.org/view/journals/phoc/52/5/JPO-D-20-0275.1.xml

    work page 2022

  8. [8]

    Evaluation of the search and rescue leeway model into the tyrrhenian sea: a new point of view,

    A. Di Maio, M. Martin, and R. Sorgente, “Evaluation of the search and rescue leeway model into the tyrrhenian sea: a new point of view,” Natural Hazards and Earth System Sciences Discussions , pp. 1–22, 04 2016

    work page 2016

Show all 65 references
  1. [9]

    Determining the drift characteristics of open lifeboats based on large-scale drift experiments,

    H. Tu, L. Mu, K. Xia, X. Wang, and K. Zhu, “Determining the drift characteristics of open lifeboats based on large-scale drift experiments,” Frontiers in Marine Science , vol. V olume 9 - 2022, 2022. [Online]. Available: https://www.frontiersin.org/journals/marine-science/arti...

    2022

  2. [10]

    Modeling the leeway drift characteristics of persons-in-water at a sea- area scale in the seas of China,

    J. Wu, L. Cheng, and S. Chu, “Modeling the leeway drift characteristics of persons-in-water at a sea- area scale in the seas of China,” Ocean Engineering , vol. 270, p. 113444, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0029801822027275

    2023

  3. [11]

    Wind-induced drift of objects at sea: The leeway field method,

    Øyvind Breivik, A. A. Allen, C. Maisondieu, and J. C. Roth, “Wind-induced drift of objects at sea: The leeway field method,” Applied Ocean Research , vol. 33, no. 2, pp. 100–109, 2011. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S014111871100006X

    2011

  4. [12]

    An operational search and rescue model for the norwegian sea and the north sea,

    Ø. Breivik and A. A. Allen, “An operational search and rescue model for the norwegian sea and the north sea,” Journal of marine systems , vol. 69, no. 1-2, pp. 99–113, 2008

    2008

  5. [13]

    A field experiment for the determination of drift characteristics of person-in-water,

    S. Kang, “A field experiment for the determination of drift characteristics of person-in-water,” J. Korean Soc. Mar. Environ. Saf, vol. 5, pp. 29–36, 1999

    1999

  6. [14]

    The leeway of an open boat and three life rafts in heavy weather,

    A. A. Allen and R. B. Fitzgerald, “The leeway of an open boat and three life rafts in heavy weather,” US Coast Guard, Operations,(GO), Tech. Rep., 1997

    1997

  7. [15]

    A novel machine-learning framework with a moving platform for maritime drift calculations,

    K. Bhaganagar, P. Kolar, S. H. A. Faruqui, D. Bhattacharjee, A. Alaeddini, and K. Subbarao, “A novel machine-learning framework with a moving platform for maritime drift calculations,” Frontiers in Marine Science , vol. V olume 9 - 2022,

    2022

  8. [16]

    Modeling of wave-induced drift based on stepwise parameter calibration,

    K. Zhu, X. Chen, L. Mu, D. Yu, R. Yu, Z. Sun, and T. Zhou, “Modeling of wave-induced drift based on stepwise parameter calibration,” Frontiers in Marine Science , vol. V olume 11 - 2024, 2025. [Online]. Available: https://www.frontiersin.org/journals/marine-science/articles/10...

    2024

  9. [17]

    Marine drifting trajectory prediction based on lstm-dnn algorithm,

    X. Li, K. Wang, M. Tang, J. Qin, P. Wu, T. Yang, and H. Zhang, “Marine drifting trajectory prediction based on lstm-dnn algorithm,” Wireless Communications and Mobile Computing , vol. 2022, pp. 1–13, 07 2022

    2022

  10. [18]

    Predicting drift characteristics of persons-in-the-water in the south China sea,

    H. Tu, X. Wang, L. Mu, and K. Xia, “Predicting drift characteristics of persons-in-the-water in the south China sea,” Ocean Engineering , vol. 242, p. 110134, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S0029801821014554

    2021

  11. [19]

    Opendrift v1. 0: a generic framework for trajectory modelling,

    K.-F. Dagestad, J. R ¨ohrs, Ø. Breivik, and B. ˚Adlandsvik, “Opendrift v1. 0: a generic framework for trajectory modelling,” Geoscientific Model Development , vol. 11, no. 4, pp. 1405–1420, 2018

    2018

  12. [20]

    The effect of vertical mixing on the horizontal drift of oil spills,

    J. R ¨ohrs, K.-F. Dagestad, H. Asbjørnsen, T. Nordam, J. Skancke, C. E. Jones, and C. Brekke, “The effect of vertical mixing on the horizontal drift of oil spills,” Ocean Science, vol. 14, no. 6, pp. 1581–1601, 2018

    2018

  13. [21]

    On predicting the leeway and drift of a survival suit clad person-in-water

    T.-C. Su, R. Q. Robe, and D. J. Finlayson, “On predicting the leeway and drift of a survival suit clad person-in-water.” US Coast Guard, Operations,(GO), Tech. Rep., 1997

    1997

  14. [22]

    Evaluating the leeway coefficient of ocean drifters using operational marine environmental prediction systems,

    G. Sutherland, N. Soontiens, F. Davidson, G. C. Smith, N. Bernier, H. Blanken, D. Schillinger, G. Marcotte, J. R ¨ohrs, K.-F. Dagestad, K. H. Christensen, and Øyvind Breivik, “Evaluating the leeway coefficient of ocean drifters using operational marine environmental prediction...

    1943

  15. [23]

    Evaluating the leeway coefficient for different ocean drifters using operational models,

    G. Sutherland, N. Soontiens, F. Davidson, G. C. Smith, N. Bernier, H. Blanken, D. Schillinger, G. Marcotte, J. R ¨ohrs, K.-F. Dagestad et al. , “Evaluating the leeway coefficient for different ocean drifters using operational models,” arXiv preprint arXiv:2005.09527, 2020

    2005 arXiv

  16. [24]

    Sentence-bert: Sentence embeddings using siamese bert-networks,

    N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 11 2019. [Online]. Available: https://arxiv.org/...

    2019 arXiv

  17. [25]

    A study on the prediction of the surface drifter trajectories in the Korean strait,

    K. Y .-T. Ha Seung Yun, Yoon Han-Sam, “A study on the prediction of the surface drifter trajectories in the Korean strait,” J Korean Soc Coast Ocean Eng , vol. 34, no. 1, pp. 11–18, 2022. [Online]. Available: http://jkscoe.or.kr/journal/view.php?number=399 ADESUNKANMI et al.: ...

    2022

  18. [26]

    The prediction and dynamic correction of drifting trajectory for unmanned maritime equipment based on fully connected neural network (fcnn) embedding model,

    Y . Song, D. Wang, X. Xiong, X. Cheng, L. Huang, and Y . Zhang, “The prediction and dynamic correction of drifting trajectory for unmanned maritime equipment based on fully connected neural network (fcnn) embedding model,” Journal of Marine Science and Engineering , vol. 12, n...

    2024

  19. [27]

    Ocean of things : Affordable maritime sensors with scalable analysis,

    J. Waterston, J. Rhea, S. Peterson, L. Bolick, J. Ayers, and J. Ellen, “Ocean of things : Affordable maritime sensors with scalable analysis,” in OCEANS 2019 - Marseille , 2019, pp. 1–6

    2019

  20. [28]

    Big data forecasting for improving maritime search operations,

    E. Martinson, J. Troyer, and A. Gillies, “Big data forecasting for improving maritime search operations,” in OCEANS 2021: San Diego – Porto , 2021, pp. 1–4

    2021

  21. [29]

    Improving numerical model predicted float trajectories by deep learning,

    D. Shen, S. Bao, L. Pietrafesa, and P. Gayes, “Improving numerical model predicted float trajectories by deep learning,” Earth and Space Science , vol. 9, 09 2022

    2022

  22. [30]

    Predicting drift characteristics of life rafts: Case study of field experiments in the south China sea,

    H. Tu, K. Xia, L. Mu, X. Chen, and X. Wang, “Predicting drift characteristics of life rafts: Case study of field experiments in the south China sea,” Ocean Engineering , vol. 262, p. 112158, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0029801822014743

    2022

  23. [31]

    A dnn framework for learning lagrangian drift with uncertainty,

    J. Jenkins, A. Paiement, Y . Ourmi `eres, J. L. Sommer, J. Verron, C. Ubelmann, and H. Glotin, “A dnn framework for learning lagrangian drift with uncertainty,” 2023. [Online]. Available: https://arxiv.org/abs/2204.05891

    2023 arXiv

  24. [32]

    An improvement on estimated drifter tracking through machine learning and evolutionary search,

    Y .-W. Nam, H.-Y . Cho, D.-Y . Kim, S.-H. Moon, and Y .-H. Kim, “An improvement on estimated drifter tracking through machine learning and evolutionary search,” Applied Sciences , vol. 10, no. 22, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/22/8123

    2020

  25. [33]

    Prediction of drifter trajectory using evolutionary computation,

    Y .-W. Nam and Y .-H. Kim, “Prediction of drifter trajectory using evolutionary computation,” Discrete Dynamics in Nature and Society , vol. 2018, no. 1, p. 6848745, 2018. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10. 1155/2018/6848745

    2018

  26. [34]

    Developing an artificial intelligence-based method for predicting the trajectory of surface drifting buoys using a hybrid multi-layer neural network model,

    M. Song, W. Hu, S. Liu, S. Chen, X. Fu, J. Zhang, W. Li, and Y . Xu, “Developing an artificial intelligence-based method for predicting the trajectory of surface drifting buoys using a hybrid multi-layer neural network model,” Journal of Marine Science and Engineering , vol. 1...

    2024

  27. [35]

    Argo buoy trajectory prediction: Multi-scale ocean driving factors and time–space attention mechanism,

    P. Ning, D. Zhang, X. Zhang, J. Zhang, Y . Liu, X. Jiang, and Y . Zhang, “Argo buoy trajectory prediction: Multi-scale ocean driving factors and time–space attention mechanism,” Journal of Marine Science and Engineering , vol. 12, no. 2,

  28. [36]

    The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-following-coordinate oceanic model,

    A. F. Shchepetkin and J. C. McWilliams, “The regional oceanic modeling system (roms): a split-explicit, free-surface, topography-following-coordinate oceanic model,” Ocean Modelling, vol. 9, no. 4, pp. 347–404, 2005. [Online]. Available: https://www.sciencedirect.com/science/a...

    2005

  29. [37]

    Mohid 2000-a coastal integrated object oriented model,

    R. Miranda, F. Braunschweig, P. Leitao, R. Neves, F. Martins, and A. Santos, “Mohid 2000-a coastal integrated object oriented model,” WIT Transactions on Ecology and the Environment , vol. 40, 2000

    2000

  30. [38]

    Influence of ocean current features on the performance of machine learning and dynamic tracking methods in predicting marine drifter trajectories,

    H. Lin, W. Yu, and Z. Lian, “Influence of ocean current features on the performance of machine learning and dynamic tracking methods in predicting marine drifter trajectories,” Journal of Marine Science and Engineering , vol. 12, no. 11,

  31. [39]

    A trajectory prediction method for drifting buoy based on ga-dnn model,

    W. Hu, M. Song, T. Hu, X. Yan, S. Ge, S. Zheng, J. Zhang, Z. Jiao, H. Song, and S. Gao, “A trajectory prediction method for drifting buoy based on ga-dnn model,” in 2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) , 2023, p...

    2023

  32. [40]

    Improving numerical model predicted float trajectories by deep learning,

    D. Shen, S. Bao, L. J. Pietrafesa, and P. Gayes, “Improving numerical model predicted float trajectories by deep learning,” Earth and Space Science , vol. 9, no. 9, p. e2022EA002362, 2022, e2022EA002362 2022EA002362. [Online]. Available: https://agupubs.onlinelibrary.wiley.com...

    2022 doi

  33. [41]

    Available: https://www.mdpi.com/2077-1312/12/11/1933

    [Online]. Available: https://www.mdpi.com/2077-1312/12/11/1933

  34. [42]

    Machine-learning mesoscale and submesoscale surface dynamics from lagrangian ocean drifter trajectories,

    N. O. Aksamit, T. Sapsis, and G. Haller, “Machine-learning mesoscale and submesoscale surface dynamics from lagrangian ocean drifter trajectories,” Journal of Physical Oceanography , vol. 50, no. 5, pp. 1179 – 1196, 2020. [Online]. Available: https://journals.ametsoc.org/view/...

    2020

  35. [43]

    Prediction of drift trajectory in the ocean using double-branch adaptive span attention,

    C. Zhang, J. Zhang, J. Zhao, and T. Zhang, “Prediction of drift trajectory in the ocean using double-branch adaptive span attention,” Journal of Marine Science and Engineering , vol. 12, no. 6, 2024. [Online]. Available: https://www.mdpi.com/2077-1312/12/6/1016

    2024

  36. [44]

    A machine learning method for the prediction of ship ADESUNKANMI et al.: MULTI-MODAL DRIFT FORECASTING OF LEEW AY OBJECTS 42 motion trajectories in real operational conditions,

    M. Zhang, P. Kujala, M. Musharraf, J. Zhang, and S. Hirdaris, “A machine learning method for the prediction of ship ADESUNKANMI et al.: MULTI-MODAL DRIFT FORECASTING OF LEEW AY OBJECTS 42 motion trajectories in real operational conditions,” Ocean Engineering , vol. 283, p. 114...

    2023

  37. [45]

    Sif-tf: A scene-interaction fusion transformer for trajectory prediction,

    F. Gao, W. Huang, L. Weng, and Y . Zhang, “Sif-tf: A scene-interaction fusion transformer for trajectory prediction,” Knowledge-Based Systems, vol. 294, p. 111744, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/ pii/S0950705124003794

    2024

  38. [46]

    Large language models for time series: A survey,

    X. Zhang, R. R. Chowdhury, R. K. Gupta, and J. Shang, “Large language models for time series: A survey,” arXiv preprint arXiv:2402.01801, 2024

    2024 arXiv

  39. [47]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023. [Online]. Available: https://arxiv.org/abs/1706.03762

    2023 arXiv

  40. [48]

    Language models can improve event prediction by few-shot abductive reasoning,

    X. Shi, S. Xue, K. Wang, F. Zhou, J. Zhang, J. Zhou, C. Tan, and H. Mei, “Language models can improve event prediction by few-shot abductive reasoning,” Advances in Neural Information Processing Systems , vol. 36, pp. 29 532–29 557, 2023

    2023

  41. [49]

    Multimodal large language models: A survey,

    J. Wu, W. Gan, Z. Chen, S. Wan, and P. S. Yu, “Multimodal large language models: A survey,” in 2023 IEEE International Conference on Big Data (BigData) . IEEE, 2023, pp. 2247–2256

    2023

  42. [50]

    Large language models are zero-shot time series forecasters,

    N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson, “Large language models are zero-shot time series forecasters,” 2024. [Online]. Available: https://arxiv.org/abs/2310.07820

    2024 arXiv

  43. [51]

    Time series forecasting with llms: Understanding and enhancing model capabilities,

    H. Tang, C. Zhang, M. Jin, Q. Yu, Z. Wang, X. Jin, Y . Zhang, and M. Du, “Time series forecasting with llms: Understanding and enhancing model capabilities,” 2024. [Online]. Available: https://arxiv.org/abs/2402.10835

    2024 arXiv

  44. [52]

    Time-llm: Time series forecasting by reprogramming large language models,

    M. Jin, S. Wang, L. Ma, Z. Chu, J. Y . Zhang, X. Shi, P.-Y . Chen, Y . Liang, Y .-F. Li, S. Pan, and Q. Wen, “Time-llm: Time series forecasting by reprogramming large language models,” 2024. [Online]. Available: https://arxiv.org/abs/2310.01728

    2024 arXiv

  45. [53]

    Short-term drift prediction of multi-functional buoys in inland rivers based on deep learning,

    F. Zeng, H. Ou, and Q. Wu, “Short-term drift prediction of multi-functional buoys in inland rivers based on deep learning,” Sensors, vol. 22, no. 14, 2022. [Online]. Available: https://www.mdpi.com/1424-8220/22/14/5120

    2022

  46. [54]

    Spectral properties of high-order element types for implicit large eddy simulation,

    C. Pereira and B. Vermeire, “Spectral properties of high-order element types for implicit large eddy simulation,” Journal of Scientific Computing , vol. 85, 2020

    2020

  47. [55]

    Julia: A fast dynamic language for technical computing,

    J. Bezanson, S. Karpinski, V . B. Shah, and A. Edelman, “Julia: A fast dynamic language for technical computing,” arXiv preprint arXiv:1209.5145, 2012

    2012 arXiv

  48. [56]

    Determining the power-law wind-profile exponent under near-neutral stability conditions at sea,

    S. A. Hsu, E. A. Meindl, and D. B. Gilhousen, “Determining the power-law wind-profile exponent under near-neutral stability conditions at sea,” Journal of Applied Meteorology and Climatology, vol. 33, no. 6, pp. 757 – 765, 1994. [Online]. Available: https://journals.ametsoc.or...

    1994

  49. [57]

    Ayachit, The paraview guide: a parallel visualization application

    U. Ayachit, The paraview guide: a parallel visualization application . Kitware, Inc., 2015

    2015

  50. [58]

    A p-adaptive lcp formulation for the compressible navier–stokes equations,

    J. Cagnone, B. Vermeire, and S. Nadarajah, “A p-adaptive lcp formulation for the compressible navier–stokes equations,” Journal of Computational Physics , vol. 233, pp. 324–338, 2013

    2013

  51. [59]

    Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities,

    C. Geuzaine and J.-F. Remacle, “Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities,” International journal for numerical methods in engineering , vol. 79, no. 11, pp. 1309–1331, 2009

    2009

  52. [60]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2017. [Online]. Available: https: //arxiv.org/abs/1412.6980

    2017 arXiv

  53. [61]

    A general method for calculating hydrodynamic forces,

    M. S ¨oylemez, “A general method for calculating hydrodynamic forces,” Ocean Engineering, vol. 23, no. 5, pp. 423–445,

  54. [62]

    Sound generation by a two-dimensional circular cylinder in a uniform flow,

    O. Inoue and N. Hatakeyama, “Sound generation by a two-dimensional circular cylinder in a uniform flow,” Journal of Fluid Mechanics, vol. 471, p. 285–314, 2002. ADESUNKANMI et al.: MULTI-MODAL DRIFT FORECASTING OF LEEW AY OBJECTS 43

    2002

  55. [1996]

    Available: https://www.sciencedirect.com/science/article/pii/0029801895000232 APPENDIX A NUMERICAL INPUT DATASET USED IN THIS STUDY Fig

    [Online]. Available: https://www.sciencedirect.com/science/article/pii/0029801895000232 APPENDIX A NUMERICAL INPUT DATASET USED IN THIS STUDY Fig. 20. Plots of numerical input dataset used in this study with respect to time

  56. [2022]

    Available: https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.831501

    [Online]. Available: https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.831501

    2022

  57. [2024]

    Available: https://www.mdpi.com/2077-1312/12/2/323

    [Online]. Available: https://www.mdpi.com/2077-1312/12/2/323

Pith tools

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