One of the biggest challenges faced by neuroscience is to have robust neuro sensing methods that translate neural activity into stable movement dynamics and with minimally invasive methods. The spinal motor neurons, the muscles, and the tendons constitute a fundamental interface that allows the nervous system to communicate with the environment in a stable and reliable way. Here I will present and discuss recent data and new decoding algorithms that identify the latent embeddings controlling all of the functional degrees of freedom of the human hand through a feedforward neural network that maps the activity of hundreds of electromyographic sensors placed on the extrinsic hand muscles. This will be followed with a view on the neural control of the muscles from the firings of populations of individual human motor neurons during a vast range of hand movements and lower limb motor actions.