SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/shands-a-multi-view-dataset-and-benchmark-for-surgical-hand-gesture-and-error-recognition-toward-medical-training

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 73
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID shands-a-multi-view-dataset-and-benchmark-for-surgical-hand-gesture-and-error-recognition-toward-medical-training | Route /signal-canvas/shands-a-multi-view-dataset-and-benchmark-for-surgical-hand-gesture-and-error-recognition-toward-medical-training

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MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: 73

Proof: Verification pending

Freshness state: computing

Source paper: SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

PDF: https://arxiv.org/pdf/2603.26400v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:52:57.774Z

Signal Canvas receipt window

Watch and verify: SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

/buildability/shands-a-multi-view-dataset-and-benchmark-for-surgical-hand-gesture-and-error-recognition-toward-medical-training

Watchwatch

Subject: SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
Our dataset is the first to provide synchronized multi-view open hand surgery videos with comprehensive gesture and error annotations.
Implicationpartial
The abstract and Table 1 explicitly state this novelty.
Verificationpartial
partial
Evidencepartial
SHands captures linear incision and suturing using five RGB cameras from complementary viewpoints
Implicationpartial
The abstract and Figure 1 describe the multi-view camera setup.
Verificationpartial
partial
Evidencepartial
The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types
Implicationpartial
The abstract and Section 3 detail the annotation taxonomy.
Verificationpartial
partial
Evidencepartial
performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure.
Implicationpartial
The abstract and Table 1 specify the participant numbers and trial structure.
Verificationpartial
partial
Evidencepartial
JIGSAWS [8] provide synchronized video and kinematic streams but fail to capture the manual hand–tool coordination central to open surgical training
Implicationpartial
The paper contrasts SHands with JIGSAWS, highlighting this limitation.
Verificationpartial
partial
Evidencepartial
Endoscopic phase datasets, such as Cholec80 [9] and M2CAI16 [10], offer single-view laparoscopic recordings but lack the fine-grained gesture boundaries and clinically validated error labels needed for targeted feedback.
Implicationpartial
The paper contrasts SHands with Cholec80, highlighting these limitations.
Verificationpartial
partial
Evidencepartial
We further define standardized evaluation protocols for single-view, multi-view, and cross-view generalization
Implicationpartial
The abstract explicitly mentions the defined evaluation protocols.
Verificationpartial
partial
Evidencepartial
The videos are annotated at the frame level with 15 gesture primitives and include a validated taxonomy of 8 trainee error types
Implicationpartial
The abstract and Section 3 detail the annotation taxonomy.
Verificationpartial
partial
Evidencepartial
Our dataset is the first to provide synchronized multi-view open hand surgery videos with comprehensive gesture and error annotations.
Implicationpartial
The abstract and Table 1 explicitly state this novelty.
Verificationpartial
partial
Evidencepartial
SHands captures linear incision and suturing using five RGB cameras from complementary viewpoints
Implicationpartial
The abstract and Figure 1 describe the multi-view camera setup.
Verificationpartial
partial
Evidencepartial
performed by 52 participants (20 experts and 32 trainees), each completing three standardized trials per procedure.
Implicationpartial
The abstract provides the participant numbers and trial details.
Verificationpartial
partial
Evidencepartial
enabling both gesture recognition and error detection.
Implicationpartial
The abstract states the purpose of the annotations.
Verificationpartial
partial

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SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training

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