Introduction: Biomechanics Today Biomechanics — from the Greek βίος (life) and μηχανή (machine) — is the branch of biophysics that studies how living systems move, deform, and bear load. It treats organisms, organs, cells, and even cell organelles as mechanical systems with measurable properties. The field is old, but the tooling around it has changed faster in the last decade than in the previous century. AI is now embedded in the scan-to-orthotic pipeline, in prosthetic control loops, in athlete monitoring, and increasingly in the brain–machine interfaces that let people move things with thought. We work with several of these pipelines in our engagements, and the pattern is consistent: the interesting engineering problem is rarely the model itself — it is the data path around it. A short history, and why it matters The first recorded study of biomechanics is attributed to Aristotle, whose De Motu Animalium (On the Movement of Animals) treated animal bodies as mechanical systems and subjected muscle action to geometric analysis. Archimedes, Leonardo da Vinci, Galileo, Newton, and Borelli each added to the picture. The lineage matters because it explains the field’s current shape: biomechanics inherits a strong tradition of measurement-first reasoning. That tradition is what makes it a natural fit for AI — the data is dense, the ground truth is physical, and the failure modes are observable. Figure 1 – Illustration of a male runner with visualisation of the angles, acceleration, and basic forces vectors applied to his body (istology, 2018). Today the field is a multibillion-dollar industry spanning orthopaedics, gait analysis, hardware–software monitoring for athletes, prosthetic limbs, and brain implants for restoring motor control. The rehabilitation equipment market alone was valued at USD 14.5 billion in 2022 with an anticipated annual growth rate of 4.9% to 2030 (Grand View Research, market-direction estimate, not an operational benchmark). The real question for engineering teams is narrower: where does AI move the needle, and where is it cosmetic? In my shoes: custom orthotics as a data pipeline One of the most fundamental applications of biomechanics is rehabilitation. Orthopaedic cases are the most common type of inherited or acquired skeletal problem, but rehabilitation does not always mean fixing a broken bone or attending physical therapy. The off-the-shelf insoles on store shelves do something, but custom orthotics — made from a scan of your actual foot — are a different category of intervention. The pipeline looks like this: Stage Sensor / process Where AI sits Capture Depth cameras, pressure sensors Noise filtering, surface reconstruction Gait analysis Video + IMU streams Pose estimation, anomaly detection Mesh generation GPU-accelerated reconstruction Smoothing, hole-filling, symmetry checks Adjustment Operator-in-the-loop edits Suggesting correction zones Print 3D printer Slice optimisation A gait analysis surfaces musculoskeletal problems — many of which are caused by the way someone walks, not by an anatomical defect. The 3D scan feeds a GPU-accelerated reconstruction step, the operator tunes the mesh, and the final geometry is printed. Computer vision is the load-bearing piece here; without reliable depth and pose estimation, the rest of the pipeline produces garbage. Figure 2 – The Albert 2 3D foot scanner from Aetrex features depth cameras and gold-plated sensors. This allows for capturing key foot measurements, such as length, width, girth, instep, and arch height (Taylor, 2020). Creating metahumans My thought, my command Neuralink has been in the headlines because it is the first company to implant a chip directly into the human brain with the explicit goal of restoring motor control to people who have lost it. Early candidates are individuals who have lost use of their upper extremities; the demonstrated capability so far is operating a cursor or a phone by intent. How does this connect to rehabilitation? Through the prosthetic side of the pipeline. Prosthetics are artificial devices that replace a body part missing due to trauma, disease, or a congenital condition. Some are cosmetic; others are high-end devices built from titanium and carbon fibre with active control. The fabrication path mirrors the orthotic pipeline: the residual limb is scanned, a custom socket is generated, and the socket is fitted to the prosthetic. Control comes from electrodes that pick up myoelectric signals — the residual electrical activity of nearby muscles — which a logic board on the prosthetic translates into actuator commands. Depending on which muscles fire, the hand can wave, shake, pinch a key, or grip. This is not telepathy. It is signal classification on a tight latency budget. The interesting question is what happens when the input source changes — when the control signal comes from a cortical implant rather than from residual muscle. The ceiling stops being the quality of myoelectric decoding and starts being the degrees of freedom of the prosthetic itself. Figure 3 – Image of an adult and a child holding hands. The adult has a prosthetic hand (Modular Prosthetic Limb - ROBOTS: Your Guide to the World of Robotics, no date). How does AI improve athletic performance through biomechanics? Professional athletes sit at the demanding end of biomechanics because they need repeatable peak output and they tolerate instrumentation that ordinary patients would not. Performance breaks down into two physical levers: diet and training. We are what we eat It does not matter how hard you train if you do not fuel properly. Muscles cramp, immune function drops, stamina decreases, recovery stretches, and reaction time degrades. The widely cited 80–20 framing — roughly 80% of performance from nutrition, 20% from training — is a coaching heuristic, not a measured constant, but it points at something real: nutrition is the dominant variable for most athletes most of the time. Generative AI can support nutritionists by drafting custom plans against an athlete’s targets and constraints. Combined with IoT-connected supply chains, those plans can trigger automatic re-ordering of ingredients and supplements. The model is not replacing the nutritionist; it is removing the boring half of the workflow. Going the distance Training is not just lifting weights and building stamina. Each sport has its own techniques, but the underlying loop is the same: monitor performance, identify deviations from intended technique, adjust, recover, repeat. Modern training programs run that loop through computer vision: pose estimation on training footage, real-time musculoskeletal monitoring, muscle strain inference, posture tracking, and physiological signals like heart-rate variability and blood pressure. Frameworks like PyTorch and runtimes like TensorRT sit underneath; OpenCV handles the pre-processing; ONNX is the format that lets a model trained in one environment ship to a different inference target. Recovery matters as much as the training stimulus. Stopping an intense session abruptly instead of gradually leads to soreness and, over time, joint injuries. Many athletes train in remote locations to limit distractions, which raises a practical question: how do trainers monitor them when the network is poor or absent? The answer is edge inference. A portable, localised stack — typically a small GPU or accelerator, an inference runtime, and a model compiled for that target — can analyse video and sensor streams on-device without a round-trip to a data centre. Figure 4 – The MOVE+ Pro NIR enhanced light therapy device by the company KINEON (Red Light Therapy Science, no date). Recovery hardware is catching up. Kineon’s MOVE+ Pro is a non-invasive Near Infrared (NIR) light therapy device aimed at reducing inflammation and supporting joint recovery. The point is not the specific device — it is that the rehabilitation stack now extends beyond the clinic. Where biomechanics AI tends to fail Most failures we see in biomechanics pipelines have nothing to do with the model architecture. They cluster around: Sensor drift. Depth cameras and IMUs need calibration. A model trained on clean data degrades silently when the sensor stack changes. Label scarcity. Ground-truth biomechanical labels are expensive — they often require a motion-capture lab. Teams overfit to whatever small dataset they have. Latency budgets. Prosthetic control and real-time coaching have hard latency ceilings. A model that is accurate but slow is not deployable. Edge deployment. A model that runs fine on a workstation may not fit on the target accelerator without quantisation or graph compilation, and that step is where accuracy quietly drops. These are engineering problems, not research problems. They are also where most of the value sits. Summing up AI is a useful ally in biomechanics and rehabilitation. It makes custom orthotics tractable at scale, sharpens prosthetic control, monitors athletes in detail, and — paired with brain–machine interfaces — opens a path toward intent-driven movement for people who have lost it. The interesting work is in the data path: capture, calibration, latency, and edge deployment. The model is the easy part. What we offer At TechnoLynx we build custom AI and computer-vision systems for biomechanics, rehabilitation, and adjacent domains. Our work covers sensor integration, model development, edge deployment, and the operational concerns — safety, data management, and ethics — that surround human-machine interaction. We are happy to talk through a specific problem; contact us and we will respond. List of references Istology (2018) Laboratory of Biomechanics, Department of Physical Education & Sport Science, Aristotle University of Thessaloniki. Modular Prosthetic Limb — ROBOTS: Your Guide to the World of Robotics (no date) (Accessed: 3 February 2024). Red Light Therapy Science — Kineon (no date) (Accessed: 25 January 2024). Rehabilitation Equipment Market Size & Share Report, 2030 (no date) (Accessed: 3 February 2024). Taylor, G. (2020) ‘How Aetrex’s New In-Store 3D Foot Scanner Aims to Bridge Shopping Channels’, Sourcing Journal, 28 October (Accessed: 3 February 2024).
