The cybertwin-driven 6G that can obtain static and dynamic data stream of users provide an exciting potential for a novel muscular human cybertwin beyond traditonally used artificial neural networks (ANNs) and musculoskeletal models (MSMs). In this article, we propose the conceptual design of the muscular human cybertwin and construct a baseline model with an improved generalization ability over ANN and an easier adaptation to new data distributions over MSMs. In particular, we for the first time propose to combine ANN and MSM, which benefits from the combination of learning-based approaches and analytical approaches. We then experimentally compare different manners of the combination and demonstrate the better combining manner on our testing case. Finally, we evaluate our method on an open-sourced dataset and on data from wearable sensors from the aspects of joint moment prediction accuracy, data efficiency, generalization ability, and time efficiency of personalization. Our proposed method achieves accuracy similar with ANN and over 30<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> better than MSM with sufficient training data. Compared with ANN, the improved data efficiency is presented by the better accuracies with a small amount of training data, and the generalization ability to unseen walking conditions and new subjects are demonstrated by the over 70<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> accuracy improvements. Moreover, when fine-tuning the model, our algorithm is demonstrated by the time 75<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> shorter than calibrated MSM and the accuracy improvements.