Feedforward control, or why movement is less reactive than you think
The brain commits to a movement before it happens. That has quiet but large implications for anyone building wearables, rehab tools, or coaching software.
The first time I saw a person trip over a cable and catch themselves mid-step, I remember thinking the nervous system was extraordinarily fast. Stimulus, sensor, response, all in a few hundred milliseconds. It was the kind of observation that makes you respect reflexes. What I did not understand yet is that most of what I was watching was not a reflex at all. It was a plan.
Classical models of movement lean on a reactive picture. A stimulus arrives, a sensor notices it, a signal runs up the spinal cord, the cortex deliberates, and a muscle responds. The latency alone rules that picture out for most of what bodies do. A tennis serve takes about 120 milliseconds to arrive; a visual cortical response needs more than that just to register the ball's trajectory. If the return were reactive, it would always be late. And yet it is not.
Prediction is the main job
The thing the brain is actually doing, most of the time, is predicting. It builds an internal model of the body and the environment, forecasts the next slice of time, and sends motor commands that are already tuned to what it expects. Sensory feedback comes back late, but the prediction has given the muscles a head start. If the prediction is wrong, feedback corrects it. If it is right, the movement looks effortless and the feedback is almost unused.
This is feedforward control. It is not a replacement for feedback. It is the scaffold that feedback hangs on.
Movement is less like a thermostat reacting to temperature and more like a forecaster who has already packed a raincoat.
Why biomechanics researchers should care
If you only study the muscle activations that follow a perturbation, you miss the part of the nervous system that decided what to do before the perturbation arrived. Pre-activation patterns in the lower limb before heel strike, co-contraction patterns the moment before a ball is caught, postural adjustments that precede a voluntary arm lift. These are the signature of a system that is predicting, not reacting. They are small, they are early, and they are easy to miss if your analysis starts at the event instead of before it.
Most of my favorite findings in this area come from running and hopping experiments where muscle activity is measured during the swing phase, well before the foot hits the ground. The timing of that activation tells you how stiff the leg is going to be at impact. That stiffness, in turn, shapes ground reaction forces, injury risk, and energetic cost. All of it is decided before contact.
Why this matters for wearables
The practical consequence is awkward for the wearables field. Most devices that promise to coach movement or prevent injury are reactive by design. They measure what already happened, classify it, and feed something back to the user a few seconds later. By the time the notification lands, the brain has moved on to planning the next three steps.
A more honest version of the problem is this: if we want a wearable to actually change movement, it has to engage with the planning layer, not just the output layer. That means either surfacing signals a person can internalize over many repetitions (so the prediction itself changes), or catching the earliest hint of a motor command before it executes. Electromyography, eye-gaze, and posture shifts are candidates for the latter. None of them are easy.
What I am circling
I keep coming back to this question because it touches almost everything I work on. My three research themes (video to biomechanics, lab to field, and the fundamentals of force control) all sit on the same substrate. You cannot build good measurement tools without knowing what a body is doing; and a body is rarely doing what the last millisecond of data implies.
