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Continuous motion joint angle estimation plays an important role in human–machine interaction (HMI). However, it is still a challenge to estimate continuous motion finger joint angles (CMFJA) accurately. To improve CMFJA estimation accuracy, we used hybrid surface electromyography-force myography (sEMG-FMG) modality as the decoding scheme since combining two sensing modalities could potentially compensate and correct for single sensing modality, and proposed a biosignals driven convolution neural networks (CNN) and Transformer model (BioCNN-T) to estimate CMFJA motivated by the potential of the Transformer architecture, which extracts local features through CNN, and captures dependencies between global features through Transformer encoder. The metacarpophalangeal joint angles of 6 hand movements commonly used in daily life were selected as estimation objects. The experimental …