<p>Most humanoid robots use high-performance CPUs to process huge amounts of numerical calculations at high speed and control joint angles with servomotors. Humans, on the other hand, use neural networks to generate signals to contract and relax multiple muscles for efficient joint movement. We have created a musculoskeletal humanoid model that mimics the human musculoskeletal structure and have conducted dynamics analysis of walking. In the analysis, we obtained the time-specific generated force and contraction displacement of 12 different muscles during one walking cycle. In this study, based on dynamics analysis, artificial muscles were fabricated using elastomers and shape memory alloys for a total of 12 muscles: gluteus maximus, iliopsoas, rectus femoris, long head of biceps femoris, short head, vastus medialis and lateralis, gastrocnemius medialis and lateralis, tibialis anterior, and soleus muscles, and attached to the legs of a musculoskeletal humanoid. Using electrical signals from an external power source, the artificial muscles were driven based on the timing of muscle contraction during one cycle of walking, and the motion of the hip, knee, and ankle joints was reproduced in a stationary system. To validate the parameters of the artificial muscles obtained from the dynamics analysis of walking, the leg movements of the musculoskeletal humanoid were compared with the simulation results from the forward dynamics analysis.</p>