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REVIEW 5 major objections 5 minor 52 references

Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images

T0 review · 5 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Forearm ultrasound images can simultaneously identify a person's manipulation skill and measure their grasp force.

desk verdict A feasible per-subject demonstration that forearm ultrasound carries information about manipulation skill and grasp force, but the abstract's generalization claim is unsupported because skill classification is confounded with object-specific hand posture and no cross-subject test is run. read the letter →

arxiv 2502.00275 v1 pith:HYDIUDXN submitted 2025-02-01 cs.RO cs.CVcs.ETcs.HC

Keshav Bimbraw , Srikar Nekkanti , Daniel B. Tiller II , Mihir Deshmukh , Berk Calli , Robert D. Howe , Haichong K. Zhang This is my paper · ORCID
classification cs.ROcs.CVcs.ETcs.HC
keywords forearmultrasoundmanipulationskillsgraspforceestimationdeeplearningconvolutionalneuralnetworkGrad-CAMteleoperationfromdemonstration
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 sets out to establish that a single forearm-worn ultrasound probe can read, from the same stream of images, both which manipulation skill a person is performing and how hard their fingers are squeezing. If true, this would give robot teleoperation and learning-from-demonstration systems one biosignal that carries both the discrete 'what action' and the continuous 'how hard' information, potentially replacing gloves and vision-based hand tracking. The authors train a shared convolutional network on data from seven subjects performing five object-specific skills and report 94.9% ± 10.2% skill classification accuracy and 0.51 ± 0.19 N force-estimation RMSE under five-fold cross-validation. They also use gradient-weighted class activation to show the model's attention falls on forearm flexor muscles, which they take as evidence that the ultrasound signal is physiologically meaningful rather than merely correlational.

What carries the argument

The load-bearing mechanism is a shared convolutional neural network backbone, adapted from a prior forearm-ultrasound hand-estimation system, whose five convolutional layers extract spatial features from 500×500 grayscale ultrasound frames. The flattened feature vector branches into two heads: a softmax layer over five skills for classification and a linear layer for continuous force regression. Because both tasks draw features from the same backbone, any skill information in the image is also available to the force estimator, and vice versa. The interpretability leg is gradient-weighted class activation mapping with guided backpropagation, which highlights which image regions drive force predictions; these heatmaps were reviewed by a physician and matched to named muscles.

What would settle it

Have new subjects perform the same five skills on objects not used in training (for example, slide a mug to an edge, or flip a coin), and check whether the skill classifier still labels the action correctly; if accuracy collapses to near chance, the 94.9% result was driven by object-specific finger postures rather than transferable manipulation skills.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that brightness-mode ultrasound images of the forearm contain enough information to simultaneously decode a discrete manipulation-skill label and a continuous grasp-force value. The authors demonstrate this with a convolutional neural network that shares a five-layer backbone between a five-way softmax classifier and a single-output linear regressor. Across seven subjects performing five manipulation skills—Push-to-Horizontal, Push-to-Vertical, Slide-to-Edge, Flip, and Simple-Pick—the skill classifier reaches 94.9% ± 10.2% test accuracy and the force regressor reaches 0.51 ± 0.19 N RMSE, both evaluated with five-fold cross-validation. The same network with gradient-weighted class activation highlights flexor digitorum profundus, flexor pollicis longus, and flexor digitorum superficialis, which the authors read as alignment between the model's attention and known muscle functions.

Load-bearing premise

The load-bearing premise is that the skill classifier is learning the manipulation skill itself rather than memorizing the unique object and three-finger grip paired with each skill.

Editorial extensions

If this is right

  • A teleoperation system could be driven by a single forearm-worn ultrasound probe, without gloves, markers, or cameras, because the same image frames yield both the intended skill and the applied force.
  • Learning-from-demonstration pipelines could log skill labels and continuous force trajectories from human demonstrations, giving robot policies paired action-and-force targets from one sensor.
  • At inference times of roughly 7 ms per frame, the approach can run at the probe's acquisition rate of 6.3 Hz and far beyond, permitting near-real-time control.
  • The shared CNN backbone means skill classification and force regression can be trained and updated together, so improvements in one task may transfer to the other.

Reading between the lines

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

  • Because each skill was tied to a unique object and a fixed three-finger grip, the reported skill accuracy likely conflates skill with object-specific hand posture; a decisive test is to decouple objects from skills, as the authors themselves flag in Section 4.5.1.
  • The auditory-cue protocol, with beeps at frames 20, 40, 60, and 80 of every 100-frame block, makes the ground-truth force profile largely periodic, so the force regressor may partly be learning cue timing rather than muscle-driven force; a free-form force-modulation test would clarify this.
  • If the skill and force decoding is truly carried by forearm muscle activity, the same shared-backbone design could transfer to cheaper or more wearable modalities such as force myography or high-density surface electromyography, since those signals also reflect muscle state.
  • A natural next step is to feed short temporal sequences of ultrasound frames rather than single frames; motion of muscles over time should help separate skills that share similar static postures.
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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 / 5 minor

Summary. The manuscript proposes a CNN-based framework that uses B-mode forearm ultrasound images to simultaneously classify five manipulation skills (Push-to-Horizontal, Push-to-Vertical, Slide-to-Edge, Flip, Simple-Pick) and estimate continuous grasp force. Data were collected from seven subjects performing the five skills with synchronized FlexiForce sensor readings. Skill classification and force regression share a common CNN backbone, with classification trained per subject and force regressors trained per subject and per skill. The authors report a five-fold cross-validated skill classification accuracy of 94.9% ± 10.2% and a force estimation RMSE of 0.51 ± 0.19 N, together with Grad-CAM visualizations that a physician labeled for muscle-group activation. The paper argues that this single biosignal modality can support teleoperation and learning from demonstration.

Significance. If the central generalization claims held, the work would be a useful step toward a wearable ultrasound interface that provides both discrete manipulation-skill labels and continuous force estimates from one sensor modality. Strengths of the paper include the synchronized acquisition of ultrasound and force data, the explicit reporting of fold-, subject-, and skill-wise results, and the inclusion of inference-time measurements (7.1 ms for classification, 7.5 ms for force estimation in Section 4.4). The interpretability analysis, although qualitative, is a reasonable attempt to connect model attention to known forearm muscle anatomy. However, the significance of the reported numbers is currently limited by the experimental protocol: models are trained and evaluated per subject, and each skill is paired with a unique object and a fixed three-finger grip. The central claim that the system estimates manipulation skill in a way that generalizes across subjects and objects is therefore not yet supported.

major comments (5)
  1. [§2.6.1 and §2.8.2] The paper describes two incompatible evaluation protocols. Section 2.6.1 states that training and evaluation were performed across five folds and reports 105 experiments (7 subjects × 3 iterations × 5 folds), while Section 2.8.2 states that, per skill, the first 1600 samples were used for training and the remaining 400 samples for testing. A fixed chronological split is not a five-fold cross-validation. The reported accuracy of 94.9% ± 10.2% cannot be interpreted until the actual split and fold construction are specified, including how the three iterations differ.
  2. [§2.6.1, Table 3, §4.1] The skill classifier is trained and tested separately for each subject, so the subject-wise results do not test whether skill representations generalize across individuals. The abstract and Section 4.1 claim generalization 'across subjects' and 'diverse manipulation tasks,' but the experiment never evaluates a model on a subject whose data were not seen in training. A leave-one-subject-out evaluation, or a model trained on multiple subjects and tested on a held-out subject, is required to support the generalization claim.
  3. [§2.3.2, Figure 2, §4.5.1] The experimental design confounds manipulation skill with object identity and hand posture: each of the five skills is paired with a distinct object (ball, can, plate, cuboid, mug) and subjects are instructed to hold all objects with the thumb, index, and middle fingers. The authors acknowledge in Section 4.5.1 that 'there may be a strong correlation between finger postures and skill classification.' Because of this confound, the 94.9% accuracy may reflect recognition of object-specific hand configurations rather than a transferable manipulation skill. The authors should decouple skill from object, for example by using multiple objects per skill or the same object with multiple skills, and report the resulting classification accuracy.
  4. [Equation (11), §2.7.1] Equation (11) defines accuracy as (TP + TN)/N, which is a binary-classification formula and is not a valid definition for the five-class skill classification task. In the multiclass setting, true negatives are not uniquely defined, and a naive computation would make accuracy appear spuriously high. The authors should replace this with the standard multiclass accuracy, defined as the number of correctly classified samples divided by the total number of samples.
  5. [§2.1, §2.6.2, §4.4] The force-estimation results are obtained from models trained per subject and per skill, and it is not stated whether the force regressor at test time receives the predicted skill label from the classifier or the ground-truth skill label. The pipeline in Section 2.1 says the force model is selected after skill identification, so the 'simultaneous' claim requires an end-to-end evaluation in which the force RMSE is computed under classifier-selected skills and compared with the oracle-skill RMSE. Without this, the reported 0.51 N RMSE does not characterize the actual joint system.
minor comments (5)
  1. [§2.5, Equation (1)] Equation (1) uses the filter f_l before defining it; please specify that f_l denotes the l-th convolution kernel and give its dimensions.
  2. [§2.5] The sentence 'as shown in 4(b)' appears without a clear antecedent; the CNN architecture is described in the text, so either add a proper figure reference or remove the dangling parenthetical.
  3. [§2.6.3] The Grad-CAM description first says it is applied to continuous force estimation, then says it visualizes regions contributing to 'a particular manipulation skill.' Please clarify the target of the heatmaps and provide the layer-weighting scheme (weights 1.0, 0.75, 0.35, 0.15) as a formal or algorithmic definition, since the current description is not reproducible.
  4. [§3.3, Tables 7 and 8] The interpretability analysis relies on a single physician's qualitative labels, and artifact cases are excluded from the quantified muscle-activation frequencies. Please state the labeling instructions and the number of videos reviewed, and consider reporting inter-rater reliability if additional raters are available.
  5. [§4.4] The inference times of 7.1 ms and 7.5 ms are reported without specifying the hardware, batch size, or whether they include ultrasound image preprocessing. Please add these details so the real-time claim can be assessed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported accuracy and RMSE come from held-out test folds after disjoint training, and the object-skill confound acknowledged in Section 4.5.1 is an external-validity limitation, not a circular derivation.

full rationale

The paper's central performance claims are evaluated on held-out test folds, not reconstructed from fitted parameters: Section 2.6.1 and Section 2.6.2 describe per-subject five-fold cross-validation with 20% test splits, and Section 3 reports test accuracy and test RMSE from those folds. The skill-classification labels are the five task labels defined in Section 2.2, and the model is scored against those labels on unseen frames, which is standard supervised evaluation rather than a self-definitional reduction. The most relevant limitation is external validity, not circularity: Section 4.5.1 states that 'since each distinct object is involved in a distinct manipulation skill, there may be a strong correlation between finger postures and skill classification,' and Section 2.6.1 trains and tests per subject. That confound means the high accuracy may reflect object-specific posture discrimination rather than transferable manipulation-skill recognition, but it does not make the held-out numbers logically depend on the claims being tested. The architecture is adapted from the authors' prior work [30], and other self-citations are used as background or prior sensing demonstrations, but no load-bearing conclusion is justified solely by those citations; the current results are measured independently. The Grad-CAM interpretability analysis in Sections 2.6.3 and 3.3 is qualitative and expert-labeled, and Section 4.5.3 notes that no quantitative artifact assessment was conducted, so the interpretability evidence is weak but it does not feed back into the derivation of the accuracy or RMSE. No hidden ansatz, uniqueness theorem, or fitted-input-renamed-as-prediction is present, so the derivation chain is self-contained apart from the acknowledged generalization limitations.

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

The central claim rests on measured model performance, not on a derived formula with fitted constants. The CNN weights (67,525 or 67,457 trainable parameters) are learned by standard training and are not treated as ad hoc fitted parameters. Hand-chosen items (auditory cue timing, Grad-CAM layer weights) affect protocol or visualization but not the reported accuracy/RMSE.

assumptions (4)
  • domain assumption The averaged readings of three FlexiForce sensors attached to the object represent valid ground-truth grasp force.
    Section 2.3.2 and 2.8.2: force labels are computed as the average of three sensor readings; sensor range is 4 N and the study does not validate sensor accuracy against a reference.
  • domain assumption Ultrasound frames and force samples are temporally aligned well enough for frame-level supervision.
    Section 2.3.1 describes an Arduino and custom Python script for synchronization but reports no latency or jitter quantification.
  • domain assumption Each manipulation skill is uniquely identified with a distinct object, making skill labels unambiguous.
    Section 2.2 assigns each skill to one object; Section 4.5.1 acknowledges this creates a finger-posture/object confound.
  • domain assumption Grad-CAM heatmaps reflect muscle activation rather than imaging artifacts.
    Section 3.3 uses physician review of Grad-CAM videos; the paper states artifact prevalence was not quantitatively assessed (Section 4.5.3).

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Cite this review

Pith. "Pith review of Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images." pith.science (2026). https://pith.science/paper/HYDIUDXN

@misc{pith2026250200275,
  author       = {Pith},
  title        = {Pith review of: Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HYDIUDXN}},
  note         = {Machine review of arXiv:2502.00275}
}
read the original abstract

Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic manipulation. A deeper understanding of human interactions with objects can further enhance teleoperation performance. To address this need, researchers have explored methods to capture and translate human manipulation skills and applied forces to robotic systems. Among these, biosignal-based approaches, particularly those using forearm ultrasound data, have shown significant potential for estimating hand movements and finger forces. In this study, we present a method for simultaneously estimating manipulation skills and applied hand force using forearm ultrasound data. Data collected from seven participants were used to train deep learning models for classifying manipulation skills and estimating grasp force. Our models achieved an average classification accuracy of 94.87 percent plus or minus 10.16 percent for manipulation skills and an average root mean square error (RMSE) of 0.51 plus or minus 0.19 Newtons for force estimation, as evaluated using five-fold cross-validation. These results highlight the effectiveness of forearm ultrasound in advancing human-machine interfacing and robotic teleoperation for complex manipulation tasks. This work enables new and effective possibilities for human-robot skill transfer and tele-manipulation, bridging the gap between human dexterity and robotic control.

Figures

Figures reproduced from arXiv: 2502.00275 by the authors.

Figure 1
Figure 1. The pipeline for simultaneous manipulation skill classification and continuous force esti [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. (Top row) Manipulation skill prototypes and objects: (a) Push to horizontal - ball, (b) [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Different muscles and their activation: (a) Flexor digitorum superficialis (FDS), flexor carpi [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: (a) Hardware setup for data acquisition: System for acquiring ultrasound and force data. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Comparison of fold-wise and subject-wise classification results. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Comparison of fold-wise and subject-wise RMSE results. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Skill-wise RMSE results [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Skill-wise Grad-CAM based muscle activations [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Subject-wise Grad-CAM based muscle activations [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Ultrasound-Based Force Estimation and Skill Classification in Manipulation Tasks: Real [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

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Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

  1. [1]

    Dzedzickis et al., ”Advanced applications of industrial robotics: New trends and possibili- ties,” Appl

    A. Dzedzickis et al., ”Advanced applications of industrial robotics: New trends and possibili- ties,” Appl. Sci., vol. 12, no. 1, p. 135, 2021

    work page 2021

  2. [2]

    Surati, S

    S. Surati, S. Hedaoo, T. Rotti, V. Ahuja, and N. Patel, ”Pick and place robotic arm: a review paper,” Int. Res. J. Eng. Technol., vol. 8, no. 2, pp. 2121–2129, 2021

    work page 2021

  3. [3]

    Suomalainen, Y

    M. Suomalainen, Y. Karayiannidis, and V. Kyrki, ”A survey of robot manipulation in contact,” Robot. Auton. Syst., vol. 156, p. 104224, 2022

    work page 2022

  4. [4]

    Bramhe and S

    S. Bramhe and S. S. Pathak, ”Robotic surgery: a narrative review,” Cureus, vol. 14, no. 9, 2022

    work page 2022

  5. [5]

    Zhang, Y

    B. Zhang, Y. Xie, J. Zhou, K. Wang, and Z. Zhang, ”State-of-the-art robotic grippers, grasping and control strategies, as well as their applications in agricultural robots: A review,” Comput. Electron. Agric., vol. 177, p. 105694, 2020. 26

    work page 2020

  6. [6]

    Papadopoulos, F

    E. Papadopoulos, F. Aghili, O. Ma, and R. Lampariello, ”Robotic manipulation and capture in space: A survey,” Front. Robot. AI, vol. 8, p. 686723, 2021

    work page 2021

  7. [7]

    Thibaut and L

    J.-P. Thibaut and L. Toussaint, ”Developing motor planning over ages,” J. Exp. Child Psychol., vol. 105, no. 1-2, pp. 116–129, 2010

    work page 2010

  8. [8]

    M. R. Cutkosky and R. D. Howe, ”Human grasp choice and robotic grasp analysis,” Dextrous Robot Hands, pp. 5–31, 1990

    work page 1990

Show all 52 references
  1. [9]

    F. Gao, M. L. Latash, and V. M. Zatsiorsky, ”Internal forces during object manipulation,” Exp. Brain Res., vol. 165, pp. 69–83, 2005

    2005

  2. [10]

    Z. Liu, Q. Liu, W. Xu, L. Wang, and Z. Zhou, ”Robot learning towards smart robotic manu- facturing: A review,” Robot. Comput.-Integr. Manuf., vol. 77, p. 102360, 2022

    2022

  3. [11]

    Song and S.-Y

    K.-T. Song and S.-Y. Jiang, ”Force-cooperative guidance design of an omni-directional walking assistive robot,” in Proc. IEEE Int. Conf. Mechatron. Autom., 2011, pp. 1258–1263

    2011

  4. [12]

    R. V. Patel, S. F. Atashzar, and M. Tavakoli, ”Haptic feedback and force-based teleoperation in surgical robotics,” Proc. IEEE, vol. 110, no. 7, pp. 1012–1027, 2022

    2022

  5. [13]

    Haidegger, J

    T. Haidegger, J. S´ andor, and Z. Beny´ o, ”Surgery in space: the future of robotic telesurgery,” Surg. Endosc., vol. 25, pp. 681–690, 2011

    2011

  6. [14]

    Trevelyan, W

    J. Trevelyan, W. R. Hamel, and S.-C. Kang, ”Robotics in hazardous applications,” Springer Handbook of Robotics, pp. 1521–1548, 2016

    2016

  7. [15]

    Lichiardopol, ”A survey on teleoperation,” Technische Universiteit Eindhoven, 2007

    S. Lichiardopol, ”A survey on teleoperation,” Technische Universiteit Eindhoven, 2007

    2007

  8. [16]

    Dipietro, A

    L. Dipietro, A. M. Sabatini, and P. Dario, ”A survey of glove-based systems and their applica- tions,” IEEE Trans. Syst. Man Cybern. C, vol. 38, no. 4, pp. 461–482, 2008

    2008

  9. [17]

    P. K. Pisharady and M. Saerbeck, ”Recent methods and databases in vision-based hand gesture recognition: A review,” Comput. Vis. Image Underst., vol. 141, pp. 152–165, 2015

    2015

  10. [18]

    Zheng, M

    M. Zheng, M. S. Crouch, and M. S. Eggleston, ”Surface electromyography as a natural human– machine interface: a review,” IEEE Sens. J., vol. 22, no. 10, pp. 9198–9214, 2022

    2022

  11. [19]

    Bimbraw and M

    K. Bimbraw and M. Zheng, ”Towards the development of a low-latency, biosignal-controlled human-machine interaction system,” in Proc. IEEE/SICE Int. Symp. Syst. Integr. (SII), 2023, pp. 1–7

    2023

  12. [20]

    Cho et al., ”Force myography to control robotic upper extremity prostheses: a feasibility study,” Front

    E. Cho et al., ”Force myography to control robotic upper extremity prostheses: a feasibility study,” Front. Bioeng. Biotechnol., vol. 4, p. 18, 2016

    2016

  13. [21]

    Bimbraw, J

    K. Bimbraw, J. Liu, Y. Wang, and T. Koike-Akino, ”Random channel ablation for robust hand gesture classification with multimodal biosignals,” arXiv preprint arXiv:2407.10874, 2024. 27

    2024 arXiv

  14. [22]

    Tsubone, T

    T. Tsubone, T. Muroga, and Y. Wada, ”Application to robot control using brain function measurement by near-infrared spectroscopy,” in Proc. IEEE Eng. Med. Biol. Soc. (EMBC), 2007, pp. 5342–5345

    2007

  15. [23]

    Zheng, Y

    E. Zheng, Y. Li, Z. Zhao, Q. Wang, and H. Qiao, ”An electrical impedance tomography based interface for human–robot collaboration,” IEEE/ASME Trans. Mechatronics, vol. 26, no. 5, pp. 2373–2384, 2020

    2020

  16. [24]

    X. Yang, C. Castellini, D. Farina, and H. Liu, ”Ultrasound as a neurorobotic interface: A review,” IEEE Trans. Syst. Man Cybern. Syst., 2024

    2024

  17. [25]

    He, ”Ultrasound-based human machine interfaces for hand gesture recognition: A scoping review and future direction,” IEEE Trans

    K. He, ”Ultrasound-based human machine interfaces for hand gesture recognition: A scoping review and future direction,” IEEE Trans. Med. Robot. Bionics, 2024

    2024

  18. [26]

    Z. G. Xiao and C. Menon, ”A review of force myography research and development,” Sensors, vol. 19, no. 20, p. 4557, 2019

    2019

  19. [27]

    Tchantchane, H

    R. Tchantchane, H. Zhou, S. Zhang, and G. Alici, ”A review of hand gesture recognition systems based on noninvasive wearable sensors,” Adv. Intell. Syst., vol. 5, no. 10, p. 2300207, 2023

    2023

  20. [28]

    Z. Cui, Y. Yu, and H. Wang, ”Recent developments in impedance-based tactile sensors: A review,” IEEE Sens. J., 2023

    2023

  21. [29]

    Bimbraw, E

    K. Bimbraw, E. Fox, G. Weinberg, and F. L. Hammond, ”Towards sonomyography-based real-time control of powered prosthesis grasp synergies,” in Proc. IEEE Eng. Med. Biol. Soc. (EMBC), 2020, pp. 4753–4757

    2020

  22. [30]

    Bimbraw, C

    K. Bimbraw, C. J. Nycz, M. Schueler, Z. Zhang, and H. K. Zhang, ”Simultaneous estimation of hand configurations and finger joint angles using forearm ultrasound,” IEEE Trans. Med. Robot. Bionics, vol. 5, no. 1, pp. 120–132, 2023

    2023

  23. [31]

    Bimbraw and H

    K. Bimbraw and H. K. Zhang, ”Estimating force exerted by the fingers based on forearm ultrasound,” in Proc. IEEE Int. Ultrason. Symp. (IUS), 2023, pp. 1–4

    2023

  24. [32]

    Spacone, S

    G. Spacone, S. Vostrikov, V. Kartsch, S. Benatti, L. Benini, and A. Cossettini, ”Tracking of wrist and hand kinematics with ultra low power wearable A-mode ultrasound,” IEEE Trans. Biomed. Circuits Syst., 2024

    2024

  25. [33]

    Bimbraw and H

    K. Bimbraw and H. K. Zhang, ”Mirror-based ultrasound system for hand gesture classification through convolutional neural network and vision transformer,” inMed. Imaging 2024: Ultrason. Imaging Tomogr., vol. 12932, pp. 218–222, 2024

    2024

  26. [34]

    Schaal, ”Learning from demonstration,” Adv

    S. Schaal, ”Learning from demonstration,” Adv. Neural Inf. Process. Syst., vol. 9, 1996

    1996

  27. [35]

    Ravichandar, A

    H. Ravichandar, A. S. Polydoros, S. Chernova, and A. Billard, ”Recent advances in robot learning from demonstration,” Annu. Rev. Control Robot. Auton. Syst., vol. 3, no. 1, pp. 297– 330, 2020. 28

    2020

  28. [36]

    Liang, V

    C.-J. Liang, V. R. Kamat, and C. C. Menassa, ”Teaching robots to perform quasi-repetitive construction tasks through human demonstration,” Autom. Constr., vol. 120, p. 103370, 2020

    2020

  29. [37]

    Su et al., ”Toward teaching by demonstration for robot-assisted minimally invasive surgery,” IEEE Trans

    H. Su et al., ”Toward teaching by demonstration for robot-assisted minimally invasive surgery,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 2, pp. 484–494, 2021

    2021

  30. [38]

    C. Zeng, C. Yang, J. Zhong, and J. Zhang, ”Encoding multiple sensor data for robotic learning skills from multimodal demonstration,” IEEE Access, vol. 7, pp. 145604–145613, 2019

    2019

  31. [39]

    Vasan and P

    G. Vasan and P. M. Pilarski, ”Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning,” in Proc. Int. Conf. Rehabil. Robot. (ICORR), 2017, pp. 1457–1464

    2017

  32. [40]

    Zongxing et al., ”Human-machine interaction technology for simultaneous gesture recognition and force assessment: A review,” IEEE Sens

    L. Zongxing et al., ”Human-machine interaction technology for simultaneous gesture recognition and force assessment: A review,” IEEE Sens. J., 2023

    2023

  33. [41]

    R. Hu, X. Chen, H. Zhang, X. Zhang, and X. Chen, ”A novel myoelectric control scheme supporting synchronous gesture recognition and muscle force estimation,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 30, pp. 1127–1137, 2022

    2022

  34. [42]

    Zengyu, L

    Q. Zengyu, L. Zongxing, Z. Liu, Y. Cai, S. Cai, B. He, and L. Yao, ”A simultaneous gesture classification and force estimation strategy based on wearable A-mode ultrasound and cascade model,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 30, pp. 2301–2311, 2022

    2022

  35. [43]

    Peng et al., ”A novel transformer-based approach for simultaneous recognition of hand movements and force levels in amputees using flexible ultrasound transducers,” IEEE Trans

    X. Peng et al., ”A novel transformer-based approach for simultaneous recognition of hand movements and force levels in amputees using flexible ultrasound transducers,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 4580–4590, 2023

    2023

  36. [44]

    Kroemer, S

    O. Kroemer, S. Niekum, and G. Konidaris, ”A review of robot learning for manipulation: Challenges, representations, and algorithms,” J. Mach. Learn. Res., vol. 22, no. 30, pp. 1–82, 2021

    2021

  37. [45]

    Della Santina et al., ”Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands,” IEEE Robot

    C. Della Santina et al., ”Learning from humans how to grasp: a data-driven architecture for autonomous grasping with anthropomorphic soft hands,” IEEE Robot. Autom. Lett., vol. 4, no. 2, pp. 1533–1540, 2019

    2019

  38. [46]

    Eppner, R

    C. Eppner, R. Deimel, J. Alvarez-Ruiz, M. Maertens, and O. Brock, ”Exploitation of envi- ronmental constraints in human and robotic grasping,” Int. J. Robot. Res., vol. 34, no. 7, pp. 1021–1038, 2015

    2015

  39. [47]

    L. U. Odhner, R. R. Ma, and A. M. Dollar, ”Precision grasping and manipulation of small objects from flat surfaces using underactuated fingers,” in Proc. IEEE Int. Conf. Robot. Autom., 2012, pp. 2830–2835

    2012

  40. [48]

    L. U. Odhner, R. R. Ma, and A. M. Dollar, ”Open-loop precision grasping with underactuated hands inspired by a human manipulation strategy,” IEEE Trans. Autom. Sci. Eng., vol. 10, no. 3, pp. 625–633, 2013. 29

    2013

  41. [49]

    Manschitz, M

    S. Manschitz, M. Gienger, J. Kober, and J. Peters, ”Learning sequential force interaction skills,” Robotics, vol. 9, no. 2, p. 45, 2020

    2020

  42. [50]

    X. Gao, J. Ling, X. Xiao, and M. Li, ”Learning Force-Relevant Skills from Human Demonstra- tion,” Complexity, vol. 2019, no. 1, p. 5262859, 2019

    2019

  43. [51]

    R. R. Selvaraju et al., ”Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 618–626

    2017

  44. [52]

    Abadi et al., ”TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” Available: https://www.tensorflow.org/, 2015

    M. Abadi et al., ”TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems,” Available: https://www.tensorflow.org/, 2015. 30

    2015

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.