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REVIEW 5 major objections 6 minor 25 references

Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper releases 30,440 labeled underwater gesture frames from 892 instances as a visual baseline for diver-robot communication.

desk verdict Useful new underwater gesture dataset, but the snippet labeling likely includes the preceding 'look at me' gesture, so treat it as a starting point rather than a gold standard. read the letter →

arxiv 2506.08974 v1 pith:QFL455G7 submitted 2025-06-10 eess.IV

Igor Kvasić , Derek Orbaugh Antillon , {DH}ula Na{j̣ , Christopher Walker , Iain Anderson , Nikola Mišković This is my paper · ORCID
classification eess.IV
keywords underwaterhandgesturerecognitiondiver-robotcommunicationgloveCADDIANgesturesacousticimagingdatasetbenchmarkhuman-robotinteraction
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 paper presents an open dataset designed to give visual gesture recognition a fair baseline in underwater diver-robot communication, where acoustic glove commands are reliable but slow. Ten divers, five in the sea and five in a pool, performed eight CADDIAN diving gestures plus two protocol gestures at 1, 2, and 3 meters from a fixed camera, yielding 30,440 frames organized into 892 gesture-instance snippets. Each snippet is labeled by a smart glove that detects the gesture on board and timestamps it, so the visual data come with a sensor-derived ground truth rather than hand-drawn boxes. The glove's reaction times, gesture times, transmission delays, and detection rates are also reported, allowing vision algorithms to be compared directly with the glove-and-acoustics pipeline.

What carries the argument

The object that carries the dataset is the gesture-detection glove plus the timing equation that turns its acoustic transmissions into video labels. The glove embeds dielectric-elastomer stretch sensors on each finger and an IMU on the back of the hand; when it recognizes a gesture, it confirms haptically and acoustically transmits a command. Equation (1) reconstructs the gesture time $T_g$ by subtracting a fixed $0.330$ s protocol delay and a $3/1520$ s acoustic travel time from the receiver timestamp $T_a$. That reconstructed time defines a 3-second snippet spanning from 2.25 s before to 0.75 s after detection, which becomes one labeled instance for machine learning.

What would settle it

Measure the true delay by sending a test glove detection through the same acoustic chain while recording a synchronized electrical pulse; if the receiver timestamp minus the true detection time differs from $0.330 + 3/1520$ s by more than one frame at 30 fps, the published snippet boundaries are misaligned.

Watch

Extended reading notes

Core claim

On its own terms, the paper's contribution is a public dataset and the claim that it can serve as a baseline for visual diving-gesture recognition. The dataset holds 30,440 frames organized into 892 gesture-instance snippets, recorded in the sea and in a pool by ten divers at 1, 2, and 3 m from a single wall-mounted camera. The ground truth is not manual annotation of the images: snippets are aligned to detection times from a smart glove, with the gesture time $T_g$ recovered from the acoustic arrival time $T_a$ via $T_a = T_g + 0.330\,\mathrm{s} + 3/1520\,\mathrm{s}$. The paper also reports the glove's recognition statistics, including an average detection rate near 85% and an acoustic reception rate of 79.83%, to serve as reference numbers for comparison with vision-based methods.

Load-bearing premise

The dataset's frame labels assume that every glove detection reaches the acoustic receiver after exactly $0.330$ s of protocol delay plus a $3/1520$ s acoustic travel time; if that delay varies between trials, the annotated snippets are systematically offset.

Editorial extensions

If this is right

  • Visual recognition systems can be trained and evaluated on 30,440 raw 1920x1080 frames of ten gestures without needing the glove hardware, using the snippet folders as labels.
  • Because the same trials contain glove detection timestamps and acoustic reception timestamps, the dataset supports a head-to-head comparison of vision speed and accuracy against the reported glove detection rate near 85% and acoustic reception rate of 79.83%.
  • The 3-second snippet structure preserves temporal context, so dynamic gestures such as "turn" can be studied as frame sequences rather than only as static images.
  • The distance and environment metadata let future benchmarks quantify how visibility conditions affect recognition, since the images are deliberately not color-corrected or preprocessed.

Reading between the lines

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

  • The paper does not itself train a recognizer; a natural next check is whether a simple visual classifier reproduces the glove's timing labels from the frames alone, which would validate the 2.25 s before and 0.75 s after window.
  • Because the metadata splits instances by environment, distance, and diver, one can test whether recognition accuracy drops more with distance or with turbidity, a comparison the paper reports the raw material for but does not perform.
  • If the fixed acoustic delay in Eq. (1) is wrong by a few tens of milliseconds, retiming the snippets with a corrected offset could shift benchmark results, so the timing assumption deserves direct measurement.
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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

5 major / 6 minor

Summary. This paper presents a new underwater diving-gesture dataset intended for training and benchmarking visual gesture recognition systems for diver–robot communication. The data were collected with a stationary underwater camera while divers performed CADDIAN-based gestures at distances of 1, 2, and 3 meters in both pool and sea environments. In parallel, the divers wore a smart glove that detects gestures with on-board sensors and transmits the recognized command acoustically. The dataset contains 892 gesture instances and over 30,000 frames, organized into 3-second snippet folders that were extracted around the glove-reported gesture detection time. The paper also reports glove recognition statistics such as reaction time, gesture execution time, transmission time, and recognition success rates. The authors position the dataset as a baseline for comparing visual and glove-based underwater gesture recognition.

Significance. If the dataset is reliable, it fills a genuine gap: publicly available underwater gesture datasets with synchronized camera and sensor data are scarce, and the CADDIAN gesture language provides a principled vocabulary. The detailed experimental setup, the inclusion of five divers per environment, and the publication of raw video and sensor logs are valuable contributions. The paper also reports quantitative glove performance statistics, which could be useful for system-level comparisons. However, the dataset's utility hinges on the quality of its visual labels, and the current methodological description leaves important questions about label correctness and circularity insufficiently addressed. With appropriate documentation and validation, this dataset could become a useful benchmark; in its present form, the central claim of a reliable visual baseline is not fully established.

major comments (5)
  1. [Section 3.3] The snippet extraction uses the glove's detection time as the sole ground-truth anchor, and no independent frame-level annotation is reported. Since the glove is the authors' own system (references [3] and [19]) and the dataset is explicitly positioned to compare visual methods against the glove, this creates a circularity that undermines the claim that the dataset provides an independent visual benchmark. The paper should either provide a human-annotated validation subset or clearly demonstrate that the glove-derived timestamps are accurate enough to serve as ground truth, for example by reporting agreement with manual gesture boundaries on a sample of videos.
  2. [Section 3.3 and Table 4] The snippet window starts 2.25 seconds before the glove detection time. Given the glove protocol in Table 4, the diver must perform a 'look at me' gesture immediately before the main semantic gesture. Consequently, a large portion of each snippet likely contains the preceding 'look at me' rather than the labeled gesture. The paper only states that reviewing footage showed 3 seconds covers the movements 'in most of the cases,' without quantifying how often or to what extent the preceding gesture appears in the window. This contamination directly affects the label fidelity for the main gestures. The authors should quantify the fraction of snippets that include the preceding gesture and either adjust the window or annotate the active gesture interval within each snippet.
  3. [Equation (1), Section 3.2] The gesture detection time T_g is reconstructed from the acoustic arrival time T_a using a fixed protocol delay of 0.330 seconds and a fixed travel time of 3/1520 seconds. This assumes that the on-board processing time, modem startup, and sound speed are constant across trials. Any variation in these quantities will systematically shift the center of every snippet and thus misalign frame labels. The paper reports no uncertainty analysis for this timing reconstruction. The authors should quantify the expected timing error (e.g., by comparing multiple trials or using the redundant acoustic receivers) and discuss its impact on the snippet alignment.
  4. [Section 3.5, Table 3] The dataset contains only 205 instances of the eight main semantic gestures, with per-class counts ranging from 17 to 37. In contrast, the auxiliary gestures 'Look at me' and 'Start communication' account for 688 instances. If the dataset is intended as a training or evaluation corpus for visual recognition of the main gestures, these numbers are quite small and highly imbalanced. The paper should either provide additional instances, clearly report the small per-class sizes as a limitation, and/or supply a recommended train/test split and evaluation metric that account for the imbalance, so that the baseline claim is scoped appropriately.
  5. [Section 3.6] The glove recognition statistics in Table 5 and Figures 11–12 (recognition success rates, reaction times, transmission times) are reported without any error bars, confidence intervals, or per-user breakdown for the success rates. Since the paper presents these as benchmarks, the variability across the ten divers and across pool/sea conditions should be quantified. At minimum, the authors should report standard deviations or interquartile ranges for the timing metrics and the number of trials underlying the success rates.
minor comments (6)
  1. [Table 5] The column header 'Totoal gesture time' contains a typo; it should read 'Total gesture time.'
  2. [Table 1] The 'Turn' gesture description contains '180°degrees' with a duplicated degree symbol; please correct to '180 degrees.'
  3. [Section 2.4] The paragraph states the video is stored in .mkv format 'along with frame timestamped data,' but it is not clear whether the frame timestamps are embedded in the video or stored in a separate file. This should be clarified for users who need to synchronize with the .csv logs.
  4. [Section 3.2] The synchronization procedure for the video is described only as recording 'a microsecond timer ... at multiple time difference samplings of each mission and averaged.' This is too vague to be reproducible. Please specify the exact method used to map video frame timestamps to the acquisition PC clock.
  5. [Figure 7] The figure caption says 'Relative distribution of image samples per gesture by distance' but the y-axis and the exact definition of 'relative distribution' are not explained in the text. Please describe how the percentages were computed and whether they are based on frames or instances.
  6. [Section 3.1] The sentence 'The first mentioned gesture starts the gesture recognition process on the smart diving glove and it is a gesture that cannot be easily done by accident during regular diving activities' is awkwardly phrased and should be revised for clarity. Additionally, the paper does not specify the finger numbering convention used in Tables 1 and 2, which would help readers interpret the descriptions.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the dataset construction is self-contained; glove detection time is a measured input, not a derived prediction.

full rationale

The paper's central contribution is a dataset, not a derivation. The only equation, Eq. (1), reconstructs the glove detection time T_g from the acoustic arrival time T_a using a fixed 0.330 s protocol delay and a 3/1520 s sound-speed travel-time term drawn from the cited modem specification [24]. This is a synchronization formula, not a fitted parameter, and it is not used to predict any quantity that the dataset is supposed to validate. Section 3.3 uses T_g only to cut a 3-second snippet window (2.25 s before to 0.75 s after detection) and to assign each snippet the gesture detected by the glove. The glove is a parallel measurement instrument whose detection output serves as the label source; the paper reports glove statistics but makes no claim of visually predicting the glove's output in this article. The phrase 'Reviewing footage of such gestures showed that 3 seconds cover all the integral movements needed to form an individual gesture in most of the cases' is a heuristic observation about window duration, not a logically circular step. The possible contamination of snippets by the preceding 'look at me' gesture or misalignment from a fixed 0.330 s delay is a data-quality and benchmark-validity risk, which belongs in correctness review rather than circularity analysis. No fitted input is renamed as a prediction, no load-bearing argument depends on a self-citation chain, and no uniqueness claim is imported from the authors' prior work. The dataset is therefore self-contained with respect to circularity concerns.

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

The paper is a data-collection report, not a derivation, so no fitted model parameters appear. The only hand-chosen number is the snippet window geometry. Three domain assumptions carry the central claim: glove timestamps are treated as visual ground truth, the fixed acoustic delay model of Eq. (1) is exact, and the fixed 3 s snippet covers each gesture. None of these are independently verified in the paper.

free parameters (1)
  • Snippet window geometry = 3 s total; 2.25 s before and 0.75 s after glove detection
    Hand-chosen in Section 3.3 ('3 seconds cover all the integral movements', ratio 3/4 before and 1/4 after detection). The claimed per-gesture annotation depends on this window always containing the gesture, but it is not validated for every gesture type, distance, or diver.
assumptions (3)
  • domain assumption Glove detection timestamps define visual ground truth.
    Section 3.3 extracts snippets around glove detection events, with no independent manual check of each frame; the semantic labels of the visual dataset inherit the glove detector's output.
  • domain assumption Fixed acoustic delay model in Eq. (1) is accurate for every trial.
    Eq. (1) subtracts 0.330 s modem protocol delay plus 3/1520 s acoustic travel from arrival time; if this fixed offset varies per message or trial, all gesture times and hence snippet alignments shift.
  • ad hoc to paper A 3 s snippet around detection fully covers each gesture.
    Stated in Section 3.3 from observation of footage, not validated per gesture type or distance; dynamic gestures like 'turn' may need more or less time.

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Cite this review

Pith. "Pith review of Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition." pith.science (2026). https://pith.science/paper/QFL455G7

@misc{pith2026250608974,
  author       = {Pith},
  title        = {Pith review of: Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QFL455G7}},
  note         = {Machine review of arXiv:2506.08974}
}
read the original abstract

In this paper, we present a dataset of diving gesture images used for human-robot interaction underwater. By offering this open access dataset, the paper aims at investigating the potential of using visual detection of diving gestures from an autonomous underwater vehicle (AUV) as a form of communication with a human diver. In addition to the image recording, the same dataset was recorded using a smart gesture recognition glove. The glove uses elastomer sensors and on-board processing to determine the selected gesture and transmit the command associated with the gesture to the AUV via acoustics. Although this method can be used under different visibility conditions and even without line of sight, it introduces a communication delay required for the acoustic transmission of the gesture command. To compare efficiency, the glove was equipped with visual markers proposed in a gesture-based language called CADDIAN and recorded with an underwater camera in parallel to the glove's onboard recognition process. The dataset contains over 30,000 underwater frames of nearly 900 individual gestures annotated in corresponding snippet folders. The dataset was recorded in a balanced ratio with five different divers in sea and five different divers in pool conditions, with gestures recorded at 1, 2 and 3 metres from the camera. The glove gesture recognition statistics are reported in terms of average diver reaction time, average time taken to perform a gesture, recognition success rate, transmission times and more. The dataset presented should provide a good baseline for comparing the performance of state of the art visual diving gesture recognition techniques under different visibility conditions.

Figures

Figures reproduced from arXiv: 2506.08974 by the authors.

Figure 1
Figure 1. Experiment overview: data acquisition PC and control center on the left, un￾derwater camera and tablet placed on the waterside facing the diver, acoustic modems mounted on the sidewalls and the diver performing gestures using the gesture recognition glove at 1, 2 and 3 meter marks. 2.1. Gesture Recognition Glove The diver’s glove investigated in [17] and replicated in this paper has been customized by incorporating … view at source ↗
Figure 2
Figure 2. Gesture recognition glove used in collecting the dataset, modified with color [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Swimpro [21] camera used for recording underwater images on the telescopic [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Tablet in the underwater casing used to interface with the diver once underwater [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: User interface of the Unity application for communicating with the diver under [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Workflow diagram of the underwater gesture experiment recording with tablet [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Relative distribution of image samples per gesture by distance from the camera [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: "OK" gesture at 1, 2 and 3 meters from the camera in the sea. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: "OK" gesture at 1, 2 and 3 meters from the camera in the pool. [PITH_FULL_IMAGE:figures/full_fig_p019_9.png]
Figure 10
Figure 10. Figure 10: Time needed to complete and transmit a gesture command in seconds split in [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: Percentage of gestures successfully detected and received through acoustics [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: Percentage of gestures successfully detected on the gesture recognition glove [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 11
Figure 11. Figure 11: In addition to the unsuccessful detections on the glove itself, some [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 21 canonical work pages

  1. [3]

    Ferreira, I

    F. Ferreira, I. Kvasić, D. Nađ, L. Mandić, N. Mišković, C. Walker, D. O. Antillon, I. Anderson, Diver-robot communication using wearable sens- ing: Remote pool experiments, Marine technology society journal 56 (5) (2022) 26–35. doi:10.4031/mtsj.56.5.5

    work page doi:10.4031/mtsj.56.5.5 2022

  2. [19]

    D. W. O. Antillon, C. R. Walker, S. Rosset, I. A. Anderson, Glove-based hand gesture recognition for diver communication, IEEE Transactions on Neural Networks and Learning Systems (2022) 1– 13doi:10.1109/TNNLS.2022.3161682

    arXiv 2022

  3. [1]

    Birk, A survey of underwater human-robot interaction (u-hri), Cur- rent Robotics Reports 3 (2022) 1–13

    A. Birk, A survey of underwater human-robot interaction (u-hri), Cur- rent Robotics Reports 3 (2022) 1–13. doi:10.1007/s43154-022-00092-7

    work page doi:10.1007/s43154-022-00092-7 2022

  4. [2]

    D. Nađ, F. Ferreira, I. Kvasić, L. Mandić, V. Slošić, C. Walker, D. O. Antillon, I. Anderson, Towards robot-aided diver navigation in mapped environments (roadmap), in: OCEANS 2022, Hampton Roads, 2022, pp. 1–5. doi:10.1109/OCEANS47191.2022.9977173. 25

    arXiv 2022

  5. [4]

    K. J. DeMarco, M. E. West, A. M. Howard, Autonomous robot-diver assistance through joint intention theory, in: 2014 Oceans - St. John’s, 2014, pp. 1–5. doi:10.1109/OCEANS.2014.7003003

    arXiv 2014

  6. [5]

    Mišković, M

    N. Mišković, M. Egi, D. Nad, A. Pascoal, L. Sebastiao, M. Bibuli, Human-robot interaction underwater: Communication and safety requirements, in: 2016 IEEE Third Underwater Communica- tions and Networking Conference (UComms), 2016, pp. 1–5. doi:10.1109/UComms.2016.7583471

    work page arXiv 2016

  7. [6]

    G. Ho, N. Pavlovic, R. Arrabito, R. Abdalla, Human factors issues when operating unmanned underwater vehicles, Proceedings of the Hu- man Factors and Ergonomics Society Annual Meeting 55 (09 2011). doi:10.1177/1071181311551088

    work page doi:10.1177/1071181311551088 2011

  8. [7]

    T. Y. Loh, M. P. Brito, N. Bose, J. Xu, K. Tenekedjiev, A fuzzy- based risk assessment framework for autonomous underwater ve- hicle under-ice missions, Risk Analysis 39 (12) (2019) 2744–2765. doi:https://doi.org/10.1111/risa.13376

    work page doi:10.1111/risa.13376 2019

Show all 25 references
  1. [8]

    A. N. Jaafar, H. Ja’afar, I. Pasya, R. Abdullah, Y. Yamada, Overview of underwater communication technology, in: K. Isa, Z. Md. Zain, R. Mohd-Mokhtar, M. Mat Noh, Z. H. Ismail, A. A. Yusof, A. F. Mo- hamad Ayob, S. S. Azhar Ali, H. Abdul Kadir (Eds.), Proceedings of the 12th N...

    2020

  2. [9]

    Bjørnø, Chapter 14 - underwater acoustic measurements and their applications, in: T

    L. Bjørnø, Chapter 14 - underwater acoustic measurements and their applications, in: T. H. Neighbors, D. Bradley (Eds.), Applied Underwater Acoustics, Elsevier, 2017, pp. 889–947. doi:https://doi.org/10.1016/B978-0-12-811240-3.00014-X

    2017 doi

  3. [10]

    Wright, A

    J. Wright, A. Colling, Chapter 5 - light and sound in seawater, in: J. Wright, A. Colling (Eds.), Seawater: its Composition, Properties and 26 Behaviour (Second Edition), second edition Edition, Pergamon, 1995, pp. 61–84

    1995

  4. [11]

    Buelow, A

    H. Buelow, A. Birk, Gesture-recognition as basis for a human robot interface (hri) on a auv, in: OCEANS’11 MTS/IEEE KONA, 2011, pp. 1–9. doi:10.23919/OCEANS.2011.6107118

    2011

  5. [12]

    Mišković, A

    N. Mišković, A. Pascoal, M. Bibuli, M. Caccia, J. A. Neasham, A. Birk, M. Egi, K. Grammer, A. Marroni, A. Vasilijević, Z. Vukić, Caddy project, year 1: Overview of technological developments and cooper- ative behaviours, IFAC-PapersOnLine 48 (2) (2015) 125–130, 4th IFAC Worksh...

    2015 doi

  6. [13]

    Chiarella, M

    D. Chiarella, M. Bibuli, G. Bruzzone, M. Caccia, A. Ranieri, E. Zereik, L. Marconi, P. Cutugno, Gesture-based language for diver-robot un- derwater interaction, in: OCEANS 2015 - Genova, 2015, pp. 1–9. doi:10.1109/OCEANS-Genova.2015.7271710

    2015

  7. [14]

    Denny, M, PADI Blog - Scuba Diving and Freediving Tips, Dive Travel Insights, Scuba diving hand signals,https://blog.padi.com/ scuba-diving-hand-signals, accessed: 2023-9-06 (Jul 2022)

    2023

  8. [15]

    Recreational Scuba Training Council, NEADC online PDF, Com- mon hand signals for scuba diving,http://www.neadc.org/ CommonHandSignalsforScubaDiving.pdf, accessed: 2023-10-11 (2005)

    2005

  9. [16]

    Chiarella, M

    D. Chiarella, M. Bibuli, G. Bruzzone, M. Caccia, A. Ranieri, E. Zereik, L. Marconi, P. Cutugno, A novel gesture-based language for underwater human–robot interaction, Journal of Marine Science and Engineering 6 (3) (2018). doi:10.3390/jmse6030091. URLhttps://www.mdpi.com/2077-...

    2018 doi

  10. [17]

    Gomez Chavez, A

    A. Gomez Chavez, A. Ranieri, D. Chiarella, E. Zereik, A. Babić, A. Birk, Caddy underwater stereo-vision dataset for human–robot interaction (hri) in the context of diver activities, Journal of Marine Science and Engineering 7 (1) (2019). doi:10.3390/jmse7010016. URLhttps://www...

    2019 doi

  11. [18]

    H. Lu, Y. Li, Y. Zhang, M. Chen, S. Serikawa, H. Kim, Underwater op- tical image processing: A comprehensive review, CoRR abs/1702.03600 (2017). arXiv:1702.03600. URLhttp://arxiv.org/abs/1702.03600

    2017 arXiv

  12. [20]

    D. W. O. Antillon, C. Walker, S. Rosset, I. A. Anderson, The chal- lenges of hand gesture recognition using dielectric elastomer sensors, in: Y. Bar-Cohen (Ed.), Electroactive Polymer Actuators and Devices (EA- PAD) XXII, Vol. 11375, International Society for Optics and Photon...

    2020 doi

  13. [21]

    au/, accessed: 2023-10-09 (2023)

    Swimpro, Underwater cameras for swimmers,https://swimpro.com. au/, accessed: 2023-10-09 (2023)

    2023

  14. [22]

    Juliani, V.-P

    A. Juliani, V.-P. Berges, E. Teng, A. Cohen, J. Harper, C. Elion, C. Goy, Y. Gao, H. Henry, M. Mattar, D. Lange, Unity: A general platform for intelligent agents (2020). arXiv:1809.02627

    2020 arXiv

  15. [23]

    M. J. Islam, Understanding human motion and gestures for un- derwater human-robot collaboration, CoRR abs/1804.02479 (2018). arXiv:1804.02479. URLhttp://arxiv.org/abs/1804.02479

    2018 arXiv

  16. [24]

    Sherlock, N

    B. Sherlock, N. Morozs, J. Neasham, P. Mitchell, Ultra-low- cost and ultra-low-power, miniature acoustic modems using multi- path tolerant spread-spectrum techniques, Electronics 11 (9) (2022). doi:10.3390/electronics11091446. URLhttps://www.mdpi.com/2079-9292/11/9/1446

    2022 doi

  17. [25]

    J. M. Hovem, H. Dong, Understanding ocean acoustics by eigenray analysis, Journal of Marine Science and Engineering 7 (4) (2019). doi:10.3390/jmse7040118. URLhttps://www.mdpi.com/2077-1312/7/4/118 28

    2019 doi

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Reviewed August 7, 2026 · model on record in the stance chip above.