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Reconstruction and recognition accuracy for different muscles.

Figshare2015-12-02 更新2026-04-29 收录

肌电信号识别

肌肉活动重建

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Reconstruction and recognition accuracy for different muscles.

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创建时间：

2015-12-02

肌电信号识别

自适应学习

In order to provide the guideline for designing and evaluating the long-term myoelectric signal recognition methods based on the adaptive learning, we proposed theoretical models to describe how the b

Mendeley Data2024-03-27 更新80

Data for: The upper bound of the misclassification risk for the long-term myoelectric signal recognition based on the adaptive learning

肌电信号识别

自适应学习

NIAID Data Ecosystem40

Performance comparison of different models on the Ninapro DB5 and MUSED-I datasets.

肌电信号识别

身体部位运动分类

Performance comparison of different models on the Ninapro DB5 and MUSED-I datasets.

Figshare2026-03-06 更新30

Ablation study: Performance comparison of different model configurations on Ninapro DB5 and MUSed-I datasets.

肌电信号识别

模型消融分析

Ablation study: Performance comparison of different model configurations on Ninapro DB5 and MUSed-I datasets.

Figshare2026-03-06 更新60

Recognition time of each sample on five benchmark databases with attention-based hybrid CNN-RNN architecture.

肌电信号识别

手势动作分类

The recognition window length is 200ms for NinaProDB1 and NinaProDB2, 150ms for BioPatRec26MOV, CapgMyo-DBa and csl-hdemg.

Figshare2018-10-30 更新40

Reconstruction and recognition accuracy for different muscles.

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