Towards Integration of Domain Knowledge-Guided Feature Engineering and Deep Feature Learning in Surface Electromyography-Based Hand Movement Recognition

<div>Average hand movement recognition accuracies achieved by decision-level fusion, stage 1 feature-level fusion, stage 2 feature-level fusion, and our proposed PFNet on NinaProDB1–NinaProDB5, when the sliding window length was set to 200 ms.</div>

Computational Intelligence and Neuroscience

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Figure 6

Figure 6: Towards Integration of Domain Knowledge-Guided Feature Engineering and Deep Feature Learning in Surface Electromyography-Based Hand Movement Recognition 

Figure 6 | Towards Integration of Domain Knowledge-Guided Feature Engineering and Deep Feature Learning in Surface Electromyography-Based Hand Movement Recognition 