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\author[1]{Yun Chen}%
\author[1]{Xinyu Zhang}%
\author[1]{Hui Li}%
\author[1]{Hongsheng He}%
\author[1]{Wan Shou}%
\author[1]{Qiang Zhang}%
\affil[1]{Affiliation not available}%


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  \date{April 24, 2026}


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\begin{abstract}
To maximize the autonomy of individuals with upper limb amputations in
daily activities, leveraging forearm muscle information to infer
movement intent is a promising research direction. While current
prosthetic hand technologies can utilize forearm muscle data to achieve
basic movements such as grasping, accurately estimating finger joint
angles remains a significant challenge. Therefore, we propose a
Multi-Stage Cascade Convolutional Neural Network with Long Short-Term
Memory Network, where an upsampling module is introduced before the
downsampling module to enhance model generalization. Additionally, we
designed a transfer learning framework based on parameter freezing,
where the pre-trained downsampling module is fixed, and only the
upsampling module is updated with a small amount of out-of-distribution
data to achieve transfer learning. Furthermore, we compared the
performance of unimodal and multimodal models, collecting surface
electromyography (sEMG) signals, brightness mode ultrasound images
(B-mode US images), and motion capture data simultaneously. The results
show that, on the validation set, the US image had the lowest error,
while on the prediction set, the four-channel sEMG achieved the lowest
error. The performance of the multimodal model in both datasets was
intermediate between the unimodal models. On the prediction set, the
average normalized root mean square error values for the four-channel
sEMG, US images, and sensor fusion models across three subjects were
0.170, 0.203, and 0.186, respectively. By utilizing advanced sensor
fusion techniques and transfer learning, our approach can reduce the
need for extensive data collection and training for new users, making
prosthetic control more accessible and adaptable to individual needs.%
\end{abstract}%



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