Fig 1.
Proposed attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition.
Table 1.
The layers configuration of proposed attention-based hybrid CNN-RNN.
Fig 2.
Six raw signal based sEMG image representation methods.
Table 2.
Comparison of gesture recognition accuracy with various image representation methods on NinaProDB1.
Here, we employ the GengNet [26], and the sliding window length is fixed at 200ms for all experiments.
Table 3.
Details of five sEMG benchmark databases.
Table 4.
Classification accuracy of the proposed method and previous works.
Table 5.
Recognition time of each sample on five benchmark databases with attention-based hybrid CNN-RNN architecture.
The recognition window length is 200ms for NinaProDB1 and NinaProDB2, 150ms for BioPatRec26MOV, CapgMyo-DBa and csl-hdemg.
Fig 3.
Classification accuracy of RNN module with raw-signal, CNN module hybrid CNN-RNN and attention-based hybrid CNN-RNN architectures with raw-image1 on five benchmark databases.
Fig 4.
Classification accuracy of attention-based hybrid CNN-RNN architecture with with different numbers of subsegments on NinaProDB1.
Fig 5.
Classification accuracy of CNN module, hybrid CNN-RNN and attention-based hybrid CNN-RNN architectures with three sEMG image representation methods on three sparse multi-channel benchmark databases.
Table 6.
Classification accuracy of different image representation methods on NinaProDB1.
We use the same sliding window length (200ms) for all experiments mentioned bellow.