OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection

Chen Hu; Letizia Gionfrida; Shan Luo

REVIEW 3 major objections 6 minor 53 references

OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A soft hand exoskeleton can read a scene and listen for commands, achieving an 87% grasps-ability score without buttons or touch.

desk verdict A real integration paper whose own tables and methods undercut its headline: the ablation is internally reversed, the GAS comparison loses on pinch, and the finger-ROM analysis is built on an invalid pose-estimation pipeline. read the letter →

arxiv 2509.04324 v1 pith:EFNKTZLN submitted 2025-09-04 cs.RO cs.CV

Chen Hu , Shan Luo , Letizia Gionfrida This is my paper · ORCID

classification cs.ROcs.CV

keywords GraspingAssistanceWearablehandexoskeletonContextualAwarenessOpen-VocabularyDetectionMultimodalIntentionEgocentricVisionSoftrobotics

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 proposes OVGrasp, a hierarchical control pipeline for a soft hand exoskeleton that decides when to grip and release by combining egocentric RGB-D vision, depth, and voice. The central aim is to show that a wearable assistive glove can be driven by open-vocabulary object detection and speech instead of push buttons or force contact, making it more generalizable and more natural for users with motor impairments. Ten healthy participants grasped 15 objects with pinch, spherical, and cylindrical grips, and the system reached a Grasping Ability Score of 87.00 ± 2.46%, outperforming push-button and force-sensing baselines while preserving finger-joint motion more faithfully, especially when the camera is head-mounted rather than worn under the hand. If correct, this points toward hands-free, adaptive grasp assistance that can handle objects unseen during training.

What carries the argument

The load-bearing mechanism is the three-level hierarchy: a YOLO-World vision-language detector that matches region embeddings to open-vocabulary text prompts; a middle-level decision-maker that registers depth with RGB, builds a Grasp-Point Graph whose nodes are object centers, selects the nearest node as the target, and maps voice keywords through the FunASR speech recognizer into symbolic commands G, R, and S; and a low-level PID controller with velocity feedback and indirect torque limiting that drives the cable-driven glove. The Grasp-Point Graph is what converts raw detections into a stable, multi-object decision about which object to grasp.

What would settle it

Run the same grasping protocol with marker-based optical motion capture on the index and middle fingers under the three conditions—exoskeleton off, eye-in-hand exoskeleton on, and egocentric exoskeleton on. If the measured MCP, PIP, and DIP range-of-motion differences between camera views disappear or reverse, the paper's kinematic alignment claim would be refuted.

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Extended reading notes

Core claim

The paper's central claim is that a hierarchical controller—high-level open-vocabulary detection, mid-level multimodal intent fusion, and low-level PID actuation—can make a cable-driven soft exoskeleton trigger grasps and releases in a natural, context-aware way. The high-level detector uses a vision-language model to recognize both pre-trained and previously unseen objects from plain text prompts. The middle-level decision-maker builds a grasp-point graph from detected objects and depth data, selects the nearest object as the target, and confirms grip commands while voice keywords trigger release and stop. Deployed on a custom egocentric-view exoskeleton, this arrangement scores higher on t

Load-bearing premise

The finger-joint motion results assume the pose-tracking model measures joint angles accurately and treats both camera views equally, but no motion-capture validation was done and the two exoskeleton-on conditions use different camera mountings.

Editorial extensions

If this is right

Users can add new object categories by simply typing a text prompt, so the glove can adapt to personal, open-ended environments without retraining.
Voice release and stop commands let the system handle the full grasp-release cycle, not just the initial grip trigger, which is a noted gap in vision-based assistive gloves.
An egocentric, head-mounted camera avoids the physical collision of an under-hand camera with the table, preserving more natural finger joint motion during low-profile and cylindrical grasps.
The multimodal intent fusion is designed for multi-object scenes, allowing the nearest object to be selected automatically while speech provides an extra safety valve.
Comparing the hierarchy against push-button and force-sensing controllers on the same glove suggests that context-aware visual triggering frees the user's attention for hand placement rather than for pressing or sensing contact.

Reading between the lines

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

Inference: because the open-vocabulary detector is prompt-driven, the same architecture could be extended beyond grasping to other assistive hand actions—pointing, pinching, or tool use—by replacing the symbolic command set.
Inference: the speech-based release channel presumes intelligible vocal commands; for users with dysarthria or limited speech, the same mid-level decision layer could instead fuse eye gaze or residual muscle signals without changing the rest of the hierarchy.
Inference: the kinematic results are presented as biomechanical evidence, but the claim that egocentric views preserve natural motion would be stronger if the pose-estimation joint angles were validated against motion capture; that validation is not in the paper.
Inference: the nearest-object rule works well in single-target trials, but in denser scenes a more explicit disambiguation step—e.g., combining gaze or pointing with voice—would likely be needed before clinical deployment.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: the new thing here is real—YOLO-World plus depth and speech as a grasp/release controller on a soft exoskeleton, with a systematic egocentric vs eye-in-hand comparison on the same hardware. That comparison hasn't been done before, and the GAS protocol with 10 participants, 15 objects, and three grasp types is concrete and reproducible. The hardware documentation is careful, and the release-intent via voice plus the queue-based grasp trigger are sensible engineering choices.

The problems are in the evidence. First, Table 3 has the rows reversed relative to the text. The text says the OVD-enabled detector reaches AP 93.3% and APunseen 0.86, but those numbers sit in the "YOLO without OVD" row; the "with OVD" row shows APunseen 0.03. Either the table labels or the text is wrong, and the section's argument depends on the text's reading. That's not a minor typo—the ablation as printed contradicts its own conclusion. Second, the abstract claims OVGrasp "outperforming state-of-the-art baselines," but Table 1 shows it below both push-button and force-sensing on pinch GAS (87.65 vs 92.00 and 93.00). The body is more careful, crediting only spherical and cylindrical, but the abstract overclaims. Third, and most serious: the finger-ROM kinematic analysis is not valid. The paper says YOLO11 was fine-tuned on 11k Hands to obtain MCP/PIP/DIP angles, but 11k Hands is a detection/classification dataset, not a hand-pose keypoint dataset. There is no way to get joint angles from it, and there is no motion-capture validation. On top of that, the eye-in-hand and egocentric conditions mount the camera in physically different locations, so any ROM difference is confounded with viewpoint and possibly hand loading. Figure 4 cannot be attributed to the controller.

So my verdict: the integration and the GAS comparison are worth a serious look, but the paper as written is not self-consistent. The kinematic-alignment claim should be removed or redone with a proper pose estimator and controlled camera placement; the ablation table must be fixed; the abstract must be brought in line with Table 1. This deserves peer review, not desk rejection, because the system is real and the core experiments are re-runnable. I'd send it back for major revision. For a reading group, it's a useful cautionary example of checking tables against claims.

Referee Report

3 major / 6 minor

Summary. OVGrasp is a hierarchical control framework for a cable-driven soft hand exoskeleton that combines YOLO-World open-vocabulary detection with depth- and speech-based intent fusion and PID low-level control. The paper reports hardware integration, a 15-object, 10-participant evaluation using the Grasping Ability Score (GAS), zero-shot detection results, and finger joint ROM comparisons between eye-in-hand and egocentric camera configurations. The central claims are that OVGrasp outperforms push-button and force-sensing baselines and improves kinematic alignment with natural hand motion.

Significance. If the results held, the paper would make a useful contribution to wearable assistive robotics by demonstrating an open-vocabulary multimodal controller and a quantitative comparison of egocentric vs eye-in-hand sensing for grasp assistance. It also presents a complete system architecture and uses a standardized GAS protocol. However, the experimental evidence contains major internal inconsistencies and a kinematics measurement pipeline that is not supported by the cited dataset, so the headline claims are not currently established.

major comments (3)

[§5.1, Table 1] The abstract claims OVGrasp 'outperforms state-of-the-art baselines,' but Table 1 shows that for pinch grasps the proposed method (GAS 87.65±0.42) is below both push-button (92.00±0.95) and force-sensing (93.00±1.17). Since pinch is one of the three evaluated grasp types, the blanket claim is unsupported. The authors should either qualify the claim to the overall average or to specific grasp types, and report pairwise significance tests; as presented, the standard deviations overlap in a way that makes the claimed advantage unclear.
[§5.3, Table 3 and surrounding text] The table and text directly contradict each other. Table 3 reports YOLO with OVD has APunseen=0.03, mAP=0.23 while YOLO without OVD has APunseen=0.86, mAP=0.93; the text states the exact opposite ('rises sharply from 0.03 to 0.86'). Additionally, the text mentions '93.3% compared to 33.3%' which appears in no table entry. As printed, the ablation either refutes the zero-shot generalization claim or is mislabeled; in either case the conclusion cannot be assessed. The authors must correct the table/text and reconcile the numbers before the open-vocabulary contribution can be evaluated.
[§4.3, §5.2, Fig. 4] The finger-ROM analysis is not valid. The 11k Hands dataset is a hand segmentation and detection dataset; it does not contain MCP/PIP/DIP joint keypoint annotations, so fine-tuning YOLO11 on it cannot produce joint angles as described. No validation against motion capture or goniometry is provided. Moreover, the two exoskeleton 'on' conditions place the camera in different physical locations (under-hand vs head-mounted), so viewpoint-dependent pose-estimation bias is confounded with any real kinematic effect. Figure 4 therefore cannot support the claimed 'improved kinematic alignment with natural hand motion.'

minor comments (6)

[§3.2.1] The phrase 'Weight YOLO-World-X' appears to have a typo; it should state 'with weights of YOLO-World-X' or similar.
[§3.2.2] The grasp-point graph G=(V,E) introduces edges E that are never defined or used in the algorithm; either remove the edge set or describe how edges contribute to decision-making.
[§5.1] The abstract's overall GAS 87.00±2.46 does not correspond to any single row in Table 1; specify how this aggregate is computed across the three grasp types and ten participants.
[§4.1] The distinction between 'seen' and 'unseen' objects is tied to YOLO-World's pretraining data. Clarify that 'seen' means categories present in that pretraining set, and confirm that the 'unseen' objects are genuinely absent from the pretraining vocabulary.
[Table 3] Use consistent units for AP/mAP (fractions vs percentages). Mixing values like 0.86 and 93.3 makes the table hard to read and can hide inconsistencies.
[Algorithm 1] The DepthRegister function is not defined; state the spatial registration method used to align depth and RGB frames.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: GAS, zero-shot evaluations, and kinematic comparisons are measured outcomes with no fitted parameters or self-citation chain forcing the conclusions.

full rationale

The paper's central claims rest on measured experimental outcomes rather than on parameters fitted to the outcomes. The 87.00% GAS is computed from manual grasping/maintaining scores across 10 participants and 15 objects, with no model constant fit to produce that number. The zero-shot detection evaluation uses YOLO-World pretrained on external datasets (O365, GoldG, CC3M) and tests objects labeled 'seen' or 'unseen' relative to that public pretraining; this is an external benchmark, not a self-defined success criterion. The eye-in-hand comparison and push-button/force-sensing baselines come from the authors' prior work [22], but those data are externally reported and falsifiable, and they are used as competitors rather than as inputs that mathematically force the proposed method's score. The finger-ROM analysis is a potential measurement-validity concern (fine-tuning YOLO11 on 11k Hands, viewpoint confounds between eye-in-hand and egocentric cameras), but that is an empirical correctness risk, not circularity: the ROM differences are not constructed from the claimed conclusion and no fitted parameter is renamed as a prediction. The paper's small amount of self-citation concerns hardware reuse and baseline selection, not the logical derivation of the headline results, so it does not constitute load-bearing circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The system is assembled from known components; the only ad hoc elements are hand-set controller parameters and the unvalidated pose-estimation assumption. No new physical entities (particles, forces, dimensions) are introduced.

free parameters (3)

Queue length threshold τ
Number of consecutive frames the same target must appear before a grip command is issued (Algorithm 1). Value is not reported; it directly affects trigger timing and success.
PID gains Kp, Ki, Kd
Velocity control gains in Eq. (3). Hand-tuned, not reported; central to smooth grip closure.
Target velocity setpoint Δv
User-defined velocity setpoint for grip/release in §3.2.3; not specified.

assumptions (5)

domain assumption YOLO-World pretrained on O365+GoldG+CC3M can zero-shot detect the 15 object prompts at deployment without retraining.
The entire OVD contribution rests on this; Section 3.2.1 and Section 4.1.
domain assumption FunASR accurately transcribes the spoken keywords 'release' and 'stop' in the test setting.
Speech branch in Algorithm 1 and Section 3.2.2; no accuracy data reported.
domain assumption The GAS protocol from Maldonado et al. [44] and AHAP [45] is a valid, repeatable measure of grasp assistance quality.
Used for all quantitative grasping comparisons in Section 4.3 and Table 1.
domain assumption A YOLO11 model fine-tuned on 11k Hands provides unbiased joint-angle (ROM) measurements across the three experimental conditions.
Kinematics analysis in Section 4.3 and Section 5.2; no ground-truth validation against motion capture is provided.
domain assumption Ten healthy participants approximate the target stroke-survivor population for evaluating assistive grasping.
All participants had normal hand function (Section 4.2); the motivation is stroke survivors (Section 1), so transferability is assumed.

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Cite this review

Pith. "Pith review of OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection." pith.science (2026). https://pith.science/paper/EFNKTZLN

@misc{pith2026250904324,
  author       = {Pith},
  title        = {Pith review of: OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EFNKTZLN}},
  note         = {Machine review of arXiv:2509.04324}
}

read the original abstract

Grasping assistance is essential for restoring autonomy in individuals with motor impairments, particularly in unstructured environments where object categories and user intentions are diverse and unpredictable. We present OVGrasp, a hierarchical control framework for soft exoskeleton-based grasp assistance that integrates RGB-D vision, open-vocabulary prompts, and voice commands to enable robust multimodal interaction. To enhance generalization in open environments, OVGrasp incorporates a vision-language foundation model with an open-vocabulary mechanism, allowing zero-shot detection of previously unseen objects without retraining. A multimodal decision-maker further fuses spatial and linguistic cues to infer user intent, such as grasp or release, in multi-object scenarios. We deploy the complete framework on a custom egocentric-view wearable exoskeleton and conduct systematic evaluations on 15 objects across three grasp types. Experimental results with ten participants demonstrate that OVGrasp achieves a grasping ability score (GAS) of 87.00%, outperforming state-of-the-art baselines and achieving improved kinematic alignment with natural hand motion.

Figures

Figures reproduced from arXiv: 2509.04324 by the authors.

**Figure 1.** An Overview of OVGrasp Architecture. This hierarchical control framework consists of three primary components. 1) High-Level Detector: This module, based on an existing design [17], employs a YOLO-based visual backbone and a text encoder to generate multi-scale visual and textual embeddings. These embeddings are then integrated using a Vision-Language Path Aggregation Network (PAN). A Box Head and a Text Contrasti… view at source ↗

**Figure 2.** Hardware setup of the proposed soft wearable exoskeleton system. (A) [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Overview of the 15 objects used in the grasping experiments, along with their cor [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Range of motion (ROM) analysis for DIP, PIP, and MCP joints of the index and [PITH_FULL_IMAGE:figures/full_fig_p020_4.png]

**Figure 5.** Figure 5: Visualization results on zero-shot inference on 7 objects and non-zero-shot In [PITH_FULL_IMAGE:figures/full_fig_p022_5.png]

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

53 extracted references · 50 canonical work pages

[1]

Stroke Association, State of the nation: Stroke statistics, accessed: 2024- 03-19 (2023)

work page 2024
[2]

Public Health England, Stroke statistics in the uk, accessed: 2024-03-19 (2021)

work page 2024
[3]

C. D. Takahashi, L. Der-Yeghiaian, V. Le, S. C. Cramer, A robotic device for hand motor therapy after stroke, Proceedings of the IEEE 96 (7) (2008) 1083–1090. doi:10.1109/JPROC.2008.925391. 21

work page arXiv 2008
[4]

J. Bae, S. Kang, J. Lee, A lightweight and soft wearable robotic hand with compact actuation for stroke patients, IEEE Transactions on Neu- ral Systems and Rehabilitation Engineering 23 (3) (2015) 353–362. doi:10.1109/TNSRE.2015.2403274

work page arXiv 2015
[5]

Y. W. Hsieh, C. Y. Wu, K. C. Lin, Y. F. Chang, C. L. Chen, J. S. Liu, Comparison of robotic-assisted hand therapy and conventional ther- apy for stroke patients: A randomized controlled trial, American Jour- nal of Physical Medicine and Rehabilitation 97 (4) (2018) 279–286. doi:10.1097/PHM.0000000000000863

work page doi:10.1097/phm.0000000000000863 2018
[6]

W. S. Kim, S. Cho, D. Baek, H. Bang, N. J. Paik, Effects of vir- tual reality-based rehabilitation on distal upper extremity function and health-related quality of life: A single-blinded, randomized controlled trial, Journal of NeuroEngineering and Rehabilitation 13 (1) (2016) 17. doi:10.1186/s12984-016-0125-x

work page doi:10.1186/s12984-016-0125-x 2016
[7]

M. D. Bland, A. Sturmoski, M. Whitson, M. L. Harris-Love, C. E. Lang, Restricted active range of motion at the elbow, forearm, wrist, or fingers decreases hand function, Journal of Hand Therapy 21 (3) (2008) 268–

work page 2008
[8]

Gionfrida, et al., Wearable robots for the real world need vision, Science Robotics 9 (90) (2024) eadj8812

L. Gionfrida, et al., Wearable robots for the real world need vision, Science Robotics 9 (90) (2024) eadj8812

work page 2024

Show all 53 references

[9]

Tanczak, A

N. Tanczak, A. Yurkewich, F. Missiroli, S. K. Wee, S. Kager, H. Choi, K.-J. Cho, H. K. Yap, C. Piazza, L. Masia, et al., Soft robotics in upper limb neurorehabilitation and assistance: Current clinical evidence and recommendations, Soft Robotics (2024)

2024
[10]

Kim, et al., Eyes are faster than hands: A soft wearable robot learns user intention from the egocentric view, Science Robotics 4 (26) (2019) eaav2949

D. Kim, et al., Eyes are faster than hands: A soft wearable robot learns user intention from the egocentric view, Science Robotics 4 (26) (2019) eaav2949

2019
[11]

Tricomi, et al., Environment-based assistance modulation for a hip exosuit via computer vision, IEEE Robotics and others 8 (5) (2023) 2550–2557

E. Tricomi, et al., Environment-based assistance modulation for a hip exosuit via computer vision, IEEE Robotics and others 8 (5) (2023) 2550–2557

2023
[12]

Diaz Garcia, Strong geometric context for scene understanding, Ph.D

R. Diaz Garcia, Strong geometric context for scene understanding, Ph.D. thesis, UC Irvine (2016). 22

2016
[13]

A. P´ erez-Yus, et al., Detection and modelling of staircases using a wear- able depth sensor, in: Computer Vision-ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part III 13, Springer, 2015, pp. 449–463

2014
[14]

Rho, et al., Multiple hand posture rehabilitation system using vision- based intention detection and soft-robotic glove, IEEE Transactions on Industrial Informatics (2024)

E. Rho, et al., Multiple hand posture rehabilitation system using vision- based intention detection and soft-robotic glove, IEEE Transactions on Industrial Informatics (2024)

2024
[15]

Rho, et al., Learning fingertip force to grasp deformable objects for soft wearable robotic glove with tsm, IEEE Robotics and Automation Letters 6 (4) (2021) 8126–8133

E. Rho, et al., Learning fingertip force to grasp deformable objects for soft wearable robotic glove with tsm, IEEE Robotics and Automation Letters 6 (4) (2021) 8126–8133

2021
[16]

T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ ar, C. L. Zitnick, Microsoft coco: Common objects in context, in: European Conference on Computer Vision (ECCV), Springer, 2014, pp. 740–755

2014
[17]

Cheng, L

T. Cheng, L. Song, Y. Ge, W. Liu, X. Wang, Y. Shan, Yolo-world: Real-time open-vocabulary object detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

2024
[18]

Z. Gao, Z. Li, J. Wang, H. Luo, X. Shi, M. Chen, Y. Li, L. Zuo, Z. Du, Z. Xiao, et al., Funasr: A fundamental end-to-end speech recognition toolkit, arXiv preprint arXiv:2305.11013 (2023)

2023 arXiv
[19]

Y. Du, F. Wei, Z. Zhang, M. Shi, Y. Gao, G. Li, Learning to prompt for open-vocabulary object detection with vision-language model, in: Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 14084–14093

2022
[20]

C. Shi, S. Yang, Edadet: Open-vocabulary object detection using early dense alignment, in: Proceedings of the IEEE/CVF international con- ference on computer vision, 2023, pp. 15724–15734

2023
[21]

S. Wu, W. Zhang, S. Jin, W. Liu, C. C. Loy, Aligning bag of regions for open-vocabulary object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 15254– 15264. 23

2023
[22]

C. Hu, E. Tricomi, E. Rho, D. Kim, L. Masia, S. Luo, L. Gion- frida, Point cloud-based grasping for soft hand exoskeleton (2025). arXiv:2504.03369. URL https://arxiv.org/abs/2504.03369

2025 arXiv
[23]

C. Hu, T. Neate, S. Luo, L. Gionfrida, Multiclear: Multimodal soft exoskeleton glove for transparent object grasping assistance (2025). arXiv:2504.03379. URL https://arxiv.org/abs/2504.03379

2025 arXiv
[24]

Missiroli, et al., Integrating computer vision in exosuits for adaptive support and reduced muscle strain in industrial environments, IEEE Robotics and Automation Letters (2023)

F. Missiroli, et al., Integrating computer vision in exosuits for adaptive support and reduced muscle strain in industrial environments, IEEE Robotics and Automation Letters (2023)

2023
[25]

Du Plessis, K

T. Du Plessis, K. Djouani, C. Oosthuizen, A review of active hand ex- oskeletons for rehabilitation and assistance, Robotics 10 (1) (2021) 40

2021
[26]

P. W. Ferguson, , et al., On the other hand: A bilateral, reconfigurable hand exoskeleton with opposable thumbs for use with upper limb ex- oskeletons, IEEE Transactions on Medical Robotics and Bionics 6 (3) (2024) 1158–1169. doi:10.1109/TMRB.2024.3421513

2024
[27]

Alicea, M

R. Alicea, M. Xiloyannis, D. Chiaradia, M. Barsotti, A. Frisoli, L. Masia, A soft, synergy-based robotic glove for grasping assistance, Wearable Technologies 2 (2021) e4

2021
[28]

Ge, et al., Design, modeling, and evaluation of fabric-based pneumatic actuators for soft wearable assistive gloves, Soft robotics 7 (5) (2020) 583–596

L. Ge, et al., Design, modeling, and evaluation of fabric-based pneumatic actuators for soft wearable assistive gloves, Soft robotics 7 (5) (2020) 583–596

2020
[29]

Triwiyanto, E

T. Triwiyanto, E. Yulianto, M. R. Mak’ruf, D. Titisari, T. Rahmawati, S. Luthfiyah, T. Hamzah, S. Syaifudin, I. D. G. H. Wisana, A review on robotic hand exoskeleton devices: State-of-the-art method, in: Pro- ceedings of the 1st International Conference on Electronics, Biomedi...

2020
[30]

Sierotowicz, N

M. Sierotowicz, N. Lotti, L. Nell, F. Missiroli, R. Alicea, X. Zhang, M. Xiloyannis, R. Rupp, E. Papp, J. Krzywinski, et al., Emg-driven 24 machine learning control of a soft glove for grasping assistance and re- habilitation, IEEE Robotics and Automation Letters 7 (2) (2022) ...

2022
[31]

Baselli, F

C. Baselli, F. e. a. Missiroli, Tendon-driven haptic glove for force feed- back telemanipulation, IEEE RAS/EMBS Biomedical Robotics and Biomechatronics 10 (2024) 1043–1050

2024
[32]

N. Lotti, et al., Intention-detection strategies for upper limb exo- suits: Model-based myoelectric vs dynamic-based control, IEEE In- ternational Conference on Biomedical Robotics and Biomechatronics (BioRob) (2020) 410–417

2020
[33]

Zhang, et al., Deep learning in human activity recognition with wear- able sensors: A review on advances, Sensors 22 (4) (2022) 1476

S. Zhang, et al., Deep learning in human activity recognition with wear- able sensors: A review on advances, Sensors 22 (4) (2022) 1476

2022
[34]

Zareian, K

A. Zareian, K. Dela Rosa, D. H. Hu, S.-F. Chang, Open-vocabulary object detection using captions, in: CVPR, 2021, pp. 14393–14402

2021
[35]

Gu, T.-Y

X. Gu, T.-Y. Lin, W. Kuo, Y. Cui, Open-vocabulary object detection via vision and language knowledge distillation, in: ICLR, 2022

2022
[36]

Radford, J

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, I. Sutskever, Learning transferable visual models from natural language supervision, in: ICML, 2021, pp. 8748–8763

2021
[37]

X. Zhou, R. Girdhar, A. Joulin, P. Kr¨ ahenb¨ uhl, I. Misra, Detecting twenty-thousand classes using image-level supervision, in: ECCV, 2022, pp. 350–368

2022
[38]

Minderer, A

M. Minderer, A. A. Gritsenko, A. Stone, M. Neumann, D. Weissenborn, A. Dosovitskiy, A. Mahendran, A. Arnab, M. Dehghani, Z. Shen, X. Wang, X. Zhai, T. Kipf, N. Houlsby, Simple open-vocabulary ob- ject detection with vision transformers, in: ECCV, 2022

2022
[39]

L. H. Li, P. Zhang, H. Zhang, J. Yang, C. Li, Y. Zhong, L. Wang, L. Yuan, L. Zhang, J.-N. Hwang, K.-W. Chang, J. Gao, Grounded language-image pre-training, in: CVPR, 2022, pp. 10955–10965. 25

2022
[40]

S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu, L. Zhang, Grounding dino: Marrying dino with grounded pre-training for open-set object detection, in: arXiv preprint arXiv:2303.05499, 2023

2023 arXiv
[41]

L. Yao, J. Han, Y. Wen, X. Liang, D. Xu, W. Zhang, Z. Li, C. Xu, H. Xu, Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection, in: NeurIPS, 2022

2022
[42]

Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin transformer: Hierarchical vision transformer using shifted windows, in: ICCV, 2021, pp. 9992–10002

2021
[43]

Calli, Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set, IEEE Robotics & Automation Magazine 22 (3) (2015) 36–52

B. Calli, Benchmarking in manipulation research: Using the yale-cmu- berkeley object and model set, IEEE Robotics & Automation Magazine 22 (3) (2015) 36–52

2015
[44]

Maldonado-Mej ´ ıa, et al., A fabric-based soft hand exoskeleton for as- sistance: the exhand exoskeleton, Frontiers in Neurorobotics 17 (2023) 1091827

2023
[45]

Llop-Harillo, et al., The anthropomorphic hand assessment protocol (ahap), Robotics and Autonomous Systems 121 (2019) 103259

I. Llop-Harillo, et al., The anthropomorphic hand assessment protocol (ahap), Robotics and Autonomous Systems 121 (2019) 103259

2019
[46]

Everingham, L

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman, The pascal visual object classes (voc) challenge, International Journal of Computer Vision 88 (2) (2010) 303–338

2010
[47]

Jocher, J

G. Jocher, J. Qiu, Ultralytics yolo11 (2024). URL https://github.com/ultralytics/ultralytics

2024
[48]

Afifi, 11k hands: gender recognition and biometric identification using a large dataset of hand images, Multimedia Tools and Applications (2019)

M. Afifi, 11k hands: gender recognition and biometric identification using a large dataset of hand images, Multimedia Tools and Applications (2019). doi:10.1007/s11042-019-7424-8. URL https://doi.org/10.1007/s11042-019-7424-8

2019 doi
[49]

Jocher, A

G. Jocher, A. Chaurasia, J. Qiu, Ultralytics yolov8 (2023). URL https://github.com/ultralytics/ultralytics 26

2023
[50]

S. Shao, Z. Li, T. Zhang, C. Peng, G. Yu, X. Zhang, J. Sun, Objects365: A large-scale, high-quality dataset for object detection, in: Proceed- ings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 8430–8439

2019
[51]

X. Zhai, A. Kolesnikov, N. Houlsby, L. Beyer, Lit: Zero-shot transfer with locked-image text tuning, in: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 18123–18133

2022
[52]

Sharma, N

P. Sharma, N. Ding, S. Goodman, R. Soricut, Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- tioning, in: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), 2018, pp. 2556–2565. 27

2018
[274]

doi:10.1197/j.jht.2008.02.003

2008 doi

Pith tools

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

[1] [1]

Stroke Association, State of the nation: Stroke statistics, accessed: 2024- 03-19 (2023)

work page 2024

[2] [2]

Public Health England, Stroke statistics in the uk, accessed: 2024-03-19 (2021)

work page 2024

[3] [3]

C. D. Takahashi, L. Der-Yeghiaian, V. Le, S. C. Cramer, A robotic device for hand motor therapy after stroke, Proceedings of the IEEE 96 (7) (2008) 1083–1090. doi:10.1109/JPROC.2008.925391. 21

work page arXiv 2008

[4] [4]

J. Bae, S. Kang, J. Lee, A lightweight and soft wearable robotic hand with compact actuation for stroke patients, IEEE Transactions on Neu- ral Systems and Rehabilitation Engineering 23 (3) (2015) 353–362. doi:10.1109/TNSRE.2015.2403274

work page arXiv 2015

[5] [5]

Y. W. Hsieh, C. Y. Wu, K. C. Lin, Y. F. Chang, C. L. Chen, J. S. Liu, Comparison of robotic-assisted hand therapy and conventional ther- apy for stroke patients: A randomized controlled trial, American Jour- nal of Physical Medicine and Rehabilitation 97 (4) (2018) 279–286. doi:10.1097/PHM.0000000000000863

work page doi:10.1097/phm.0000000000000863 2018

[6] [6]

W. S. Kim, S. Cho, D. Baek, H. Bang, N. J. Paik, Effects of vir- tual reality-based rehabilitation on distal upper extremity function and health-related quality of life: A single-blinded, randomized controlled trial, Journal of NeuroEngineering and Rehabilitation 13 (1) (2016) 17. doi:10.1186/s12984-016-0125-x

work page doi:10.1186/s12984-016-0125-x 2016

[7] [7]

M. D. Bland, A. Sturmoski, M. Whitson, M. L. Harris-Love, C. E. Lang, Restricted active range of motion at the elbow, forearm, wrist, or fingers decreases hand function, Journal of Hand Therapy 21 (3) (2008) 268–

work page 2008

[8] [8]

Gionfrida, et al., Wearable robots for the real world need vision, Science Robotics 9 (90) (2024) eadj8812

L. Gionfrida, et al., Wearable robots for the real world need vision, Science Robotics 9 (90) (2024) eadj8812

work page 2024