AI in Biomechanics: From Creating Cosmetic Prosthetics to Making Metahumans

Surely, the term ‘Biomechanics’ sounds familiar. If you are like us, there is no way you have not seen movies in which the hero has parts of his body replaced with mechanical or electronic parts. Let’s see if this is just a sci-fi concept or if there is any truth to that.

AI in Biomechanics: From Creating Cosmetic Prosthetics to Making Metahumans
Written by TechnoLynx Published on 08 Apr 2024

Introduction: Biomechanics Today

The term ‘biomechanics’ is a complex word composed of the Greek words ‘βίος’, meaning ‘life’ and ‘μηχανή’, meaning machine/apparatus. It is the branch of biophysics studying the structure, function, and motion of the mechanical aspects of biological systems at any level, from whole organisms to organs, cells, and cell organelles, using the mechanics methods. There are so many commercial and hobbyist items out there, it is no wonder that biomechanics has been studied for centuries!

The History of Biomechanics

Early Days

The first known study of biomechanics was made by the man himself: Aristotle. In his book “De Motu Animalium” (On the Movement of Animals), he defined the bodies of animals as mechanical systems describing the actions of the muscles and subjecting them to geometric analysis for the first time. It is believed that Aristotle’s work led to the birth of kinesiology, establishing the foundations for spinal biomechanics. Followed by Archimedes, Leonardo da Vinci, Galileo, Newton, and Borelli, it is no wonder that biomechanics has evolved so much over time.

Figure 1 – Illustration of a male runner with visualisation of the angles, acceleration, and basic forces vectors applied to his body (istology, 2018).
Figure 1 – Illustration of a male runner with visualisation of the angles, acceleration, and basic forces vectors applied to his body (istology, 2018).

Today, biomechanics has evolved into a multibillion-dollar industry, with applications ranging from orthopaedics and gait analysis systems to hardware-software monitoring complexes for athletes, prosthetic limbs, and brain implants for lost motor control. The question is, ‘How can such systems be further improved?’ Keep scrolling to learn more about biomechanics’ fascinating applications, to check your knowledge, and to learn how AI is implemented in such systems to take them to a whole new level!

In my shoes

One of the most fundamental applications of biomechanics is the rehabilitation market. In fact, the data show that worldwide, the size of the rehabilitation equipment market was valued at 14.5 billion USD in 2022, with an anticipated annual growth rate of 4.9% until 2030 (Rehabilitation Equipment Market Size & Share Report, 2030, no date).

Orthopaedic cases are the most common type of inherited or acquired skeletal problem, but rehabilitation does not necessarily mean fixing a broken back, nor does it refer to physical therapy sessions. You have seen those anatomical insoles on the store shelves, right? Yes, they are good and all, but did you know that you can order custom-made ones tailored to your shoe and foot? A gait analysis can show all kinds of musculoskeletal problems, most of which are caused by improper walking. Through accurate 3D scanning, measurements of your foot are taken. After the foot is scanned, a GPU-accelerated algorithm is run to visualise the scan and make adjustments to achieve optimal results. The scan is then sent to a 3D printer, and a mould of your foot is created. Finally, the insole is made in this exact mould, and voila! You have your first orthotic!

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).
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

If you have watched the latest news, you have surely seen that Neuralink is in many headlines, and there is a reason behind that! Neuralink is the first company to implant a chip implant directly into the human brain. With individuals who have lost motor control in their upper extremities being the first candidates, the founder and visioner Elon Musk wanted to state they can use their mobile phones just by thought. How is this connected to rehabilitation, though? Well, we have already discussed orthotics. Let us now move on to the prosthetics!

Prosthetics are any artificial device that replaces a body part that is missing due to trauma, disease, or a condition present at birth. Some companies have developed all kinds of prosthetic limbs, some purely cosmetic. In contrast, others have pioneered the field by making high-end devices with titanium and carbon fibres. Focusing on the latter, their development is very similar to that of the insoles we mentioned earlier. The missing end part of the limb is scanned and a special socket is made. This socket is attached to the prosthetic limb, and with the use of electrodes, electric signals are reaching the prosthetic’s logic board, where they are translated into movements. Depending on the muscles we use, a prosthetic hand can perform a wave motion, a handshake, a pinch so that we can use a pair of keys to open the door, you name it! This tricks us into believing that we can control what the hand does just by thought. Can we, though? Of course not! There is no such thing as telepathy between man and machine. But wait… see where I am going? What would happen if the brain implant mentioned above could be used with such prosthetics? The answer is simple: It would unlock a new spectrum of possibilities, with the only limitation 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).
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).

Fair play

Professional athletes are probably the professionals who receive the most pampering. Don’t get me wrong. Athletes, especially those who compete in the first category, need to perform to their full potential each day, always surpassing themselves and beating their personal records. In general, the performance of an athlete depends on two physical factors: Diet and training.

We are What we Eat

Ask anyone. It doesn’t matter how hard you train. If you do not feed properly, your body will betray you. Your muscles will feel cramps, your immune system will start to weaken, your stamina will decrease, recovery time will stretch, and most importantly, your brain relays will shut off. The 80-20 rule is not just a myth. Approximately 80% of athletic performance has been observed to be due to a proper diet, while only 20% is due to fitness. The world’s best athletes have entire teams of nutritionists who ensure that the necessary calories, fibres, carbohydrates, proteins and minerals are consumed each day and at the appropriate time. Generative AI can definitely help nutritionists by suggesting custom diet plans to meet the needs of athletes. Furthermore, combined with the Internet of Things (IoT), it can automatically source ingredients and supplements at the best price or the fastest delivery time and place an order. Now that is a win!

Going the Distance

Moving onto the second part, training is not just lifting weights, jumping higher, and building stamina. Surely, each sport has its tricks and techniques, but some things always stay the same: performance improvement through proper monitoring and recovery. An athlete can only do this much without his team of trainers. Modern training programs include performance analysis through Computer Vision (CV), which consists of time performance, real-time musculoskeletal monitoring, muscle strain, posture, and blood pressure. This way, they can know exactly what their champion needs to change in their technique for maximum performance. And, of course, no exercise is complete without recovery. An intense workout that has been stopped suddenly instead of gradually will surely lead to soreness and decreased performance, which in turn might lead to possible injuries, especially in the joints. It is known that many athletes prefer to train in remote areas to increase their focus by limiting distractions. You will think now, ‘but how can their trainers monitor them in remote areas?’ The answer is ‘edge computing’. A portable, localised AI infrastructure that can analyse massive amounts of data to derive the most is not so difficult to create. Technology does not need to be our ally only in urban areas!

Figure 4 – The MOVE+ Pro NIR enhanced light therapy device by the company KINEON (Red Light Therapy Science, no date).
Figure 4 – The MOVE+ Pro NIR enhanced light therapy device by the company KINEON (Red Light Therapy Science, no date).

Fortunately, some companies can take care of joint pain with the help of technology. Kineon is a technology company that innovates light therapy devices and provides clinical-level treatments in the comfort of your home. Their device, the MOVE+ Pro is a safe, non-invasive, and enhanced light therapy device. The MOVE+ Pro uses Near Infrared (NIR) red light to reduce inflammation, boost healing and recovery, and even boost collagen production in your joints. It comes in a travel case to carry it wherever you need, 3 LED laser modules, 1 adjustable strap, and a charging cable. You can click here to visit their shop and order yours now. The best part is that you have a 30-day trial to evaluate whether it suits your needs. And if you live in a remote area, don’t worry. Kineon is so proud of its products that they ship worldwide!

Summing Up

AI is a powerful ally in the field of biomechanics and rehabilitation. It can make you orthotics, monitor your performance, create a diet plan, it can even help you move things with your mind! There is no doubt that AI can do wonders. It is time to embrace a powerful ally.

What We Offer

At TechnoLynx, we specialise in delivering custom, innovative tech solutions tailored to any challenge because we understand the benefits of integrating AI into biomechanis and rehabilitation applications. Our expertise covers improving AI capabilities, ensuring safety in human-machine interactions, managing and analysing extensive data sets, and addressing ethical considerations.

We offer precise software solutions designed to empower AI-driven algorithms in various industries. Our commitment to innovation drives us to adapt to the ever-evolving AI landscape. We provide cutting-edge solutions that increase efficiency, accuracy, and productivity. Feel free to contact us. We will be more than happy to answer any questions!

List of references

Cost, Efficiency, and Value Are Not the Same Metric

Cost, Efficiency, and Value Are Not the Same Metric

17/04/2026

Performance per dollar. Tokens per watt. Cost per request. These sound like the same thing said differently, but they measure genuinely different dimensions of AI infrastructure economics. Conflating them leads to infrastructure decisions that optimize for the wrong objective.
Precision Is an Economic Lever in Inference Systems

Precision Is an Economic Lever in Inference Systems

17/04/2026

Precision isn't just a numerical setting — it's an economic one. Choosing FP8 over BF16, or INT8 over FP16, changes throughput, latency, memory footprint, and power draw simultaneously. For inference at scale, these changes compound into significant cost differences.
Precision Choices Are Constrained by Hardware Architecture

Precision Choices Are Constrained by Hardware Architecture

17/04/2026

You can't run FP8 inference on hardware that doesn't have FP8 tensor cores. Precision format decisions are conditional on the accelerator's architecture — its tensor core generation, native format support, and the efficiency penalties for unsupported formats.
Steady-State Performance, Cost, and Capacity Planning

Steady-State Performance, Cost, and Capacity Planning

17/04/2026

Capacity planning built on peak performance numbers over-provisions or under-delivers. Real infrastructure sizing requires steady-state throughput — the predictable, sustained output the system actually delivers over hours and days, not the number it hit in the first five minutes.
How Benchmark Context Gets Lost in Procurement

How Benchmark Context Gets Lost in Procurement

16/04/2026

A benchmark result starts with full context — workload, software stack, measurement conditions. By the time it reaches a procurement deck, all that context is gone. The failure mode is not wrong benchmarks but context loss during propagation.
Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

16/04/2026

High-value AI hardware decisions need traceable evidence, not slide-deck bullet points. When benchmarks are documented with methodology, assumptions, and limitations, they become auditable institutional evidence — defensible under scrutiny and revisitable when conditions change.
The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

16/04/2026

Two benchmark scores can only be compared if they share a declared methodology — the same workload, precision, measurement protocol, and reporting conditions. Without that contract, the comparison is arithmetic on numbers of unknown provenance.
A Decision Framework for Choosing AI Hardware

A Decision Framework for Choosing AI Hardware

16/04/2026

Hardware selection is a multivariate decision under uncertainty — not a score comparison. This framework walks through the steps: defining the decision, matching evaluation to deployment, measuring what predicts production, preserving tradeoffs, and building a repeatable process.
How Benchmarks Shape Organizations Before Anyone Reads the Score

How Benchmarks Shape Organizations Before Anyone Reads the Score

16/04/2026

Before a benchmark score informs a purchase, it has already shaped what gets optimized, what gets reported, and what the organization considers important. Benchmarks function as decision infrastructure — and that influence deserves more scrutiny than the number itself.
Accuracy Loss from Lower Precision Is Task‑Dependent

Accuracy Loss from Lower Precision Is Task‑Dependent

16/04/2026

Reduced precision does not produce a uniform accuracy penalty. Sensitivity depends on the task, the metric, and the evaluation setup — and accuracy impact cannot be assumed without measurement.
Precision Is a Design Parameter, Not a Quality Compromise

Precision Is a Design Parameter, Not a Quality Compromise

16/04/2026

Numerical precision is an explicit design parameter in AI systems, not a moral downgrade in quality. This article reframes precision as a representation choice with intentional trade-offs, not a concession made reluctantly.
Mixed Precision Works by Exploiting Numerical Tolerance

Mixed Precision Works by Exploiting Numerical Tolerance

16/04/2026

Not every multiplication deserves 32 bits. Mixed precision works because neural network computations have uneven numerical sensitivity — some operations tolerate aggressive precision reduction, others don't — and the performance gains come from telling them apart.

Throughput vs Latency: Choosing the Wrong Optimization Target

16/04/2026

Throughput and latency are different objectives that often compete for the same resources. This article explains the trade-off, why batch size reshapes behavior, and why percentiles matter more than averages in latency-sensitive systems.

Quantization Is Controlled Approximation, Not Model Damage

16/04/2026

When someone says 'quantize the model,' the instinct is to hear 'degrade the model.' That framing is wrong. Quantization is controlled numerical approximation — a deliberate engineering trade-off with bounded, measurable error characteristics — not an act of destruction.

GPU Utilization Is Not Performance

15/04/2026

The utilization percentage in nvidia-smi reports kernel scheduling activity, not efficiency or throughput. This article explains the metric's exact definition, why it routinely misleads in both directions, and what to pair it with for accurate performance reads.

FP8, FP16, and BF16 Represent Different Operating Regimes

15/04/2026

FP8 is not just 'half of FP16.' Each numerical format encodes a different set of assumptions about range, precision, and risk tolerance. Choosing between them means choosing operating regimes — different trade-offs between throughput, numerical stability, and what the hardware can actually accelerate.

Peak Performance vs Steady‑State Performance in AI

15/04/2026

AI systems rarely operate at peak. This article defines the peak vs. steady-state distinction, explains when each regime applies, and shows why evaluations that capture only peak conditions mischaracterize real-world throughput.

The Software Stack Is a First‑Class Performance Component

15/04/2026

Drivers, runtimes, frameworks, and libraries define the execution path that determines GPU throughput. This article traces how each software layer introduces real performance ceilings and why version-level detail must be explicit in any credible comparison.

The Mythology of 100% GPU Utilization

15/04/2026

Is 100% GPU utilization bad? Will it damage the hardware? Should you be worried? For datacenter AI workloads, sustained high utilization is normal — and the anxiety around it usually reflects gaming-era intuitions that don't apply.

Why Benchmarks Fail to Match Real AI Workloads

15/04/2026

The word 'realistic' gets attached to benchmarks freely, but real AI workloads have properties that synthetic benchmarks structurally omit: variable request patterns, queuing dynamics, mixed operations, and workload shapes that change the hardware's operating regime.

Why Identical GPUs Often Perform Differently

15/04/2026

'Same GPU' does not imply the same performance. This article explains why system configuration, software versions, and execution context routinely outweigh nominal hardware identity.

Training and Inference Are Fundamentally Different Workloads

15/04/2026

A GPU that excels at training may disappoint at inference, and vice versa. Training and inference stress different system components, follow different scaling rules, and demand different optimization strategies. Treating them as interchangeable is a design error.

Performance Ownership Spans Hardware and Software Teams

15/04/2026

When an AI workload underperforms, attribution is the first casualty. Hardware blames software. Software blames hardware. The actual problem lives in the gap between them — and no single team owns that gap.

Performance Emerges from the Hardware × Software Stack

15/04/2026

AI performance is an emergent property of hardware, software, and workload operating together. This article explains why outcomes cannot be attributed to hardware alone and why the stack is the true unit of performance.

Power, Thermals, and the Hidden Governors of Performance

14/04/2026

Every GPU has a physical ceiling that sits below its theoretical peak. Power limits, thermal throttling, and transient boost clocks mean that the performance you read on the spec sheet is not the performance the hardware sustains. The physics always wins.

Why AI Performance Changes Over Time

14/04/2026

That impressive throughput number from the first five minutes of a training run? It probably won't hold. AI workload performance shifts over time due to warmup effects, thermal dynamics, scheduling changes, and memory pressure. Understanding why is the first step toward trustworthy measurement.

CUDA, Frameworks, and Ecosystem Lock-In

14/04/2026

Why is it so hard to switch away from CUDA? Because the lock-in isn't in the API — it's in the ecosystem. Libraries, tooling, community knowledge, and years of optimization create switching costs that no hardware swap alone can overcome.

GPUs Are Part of a Larger System

14/04/2026

CPU overhead, memory bandwidth, PCIe topology, and host-side scheduling routinely limit what a GPU can deliver — even when the accelerator itself has headroom. This article maps the non-GPU bottlenecks that determine real AI throughput.

Why AI Performance Must Be Measured Under Representative Workloads

14/04/2026

Spec sheets, leaderboards, and vendor numbers cannot substitute for empirical measurement under your own workload and stack. Defensible performance conclusions require representative execution — not estimates, not extrapolations.

Low GPU Utilization: Where the Real Bottlenecks Hide

14/04/2026

When GPU utilization drops below expectations, the cause usually isn't the GPU itself. This article traces common bottleneck patterns — host-side stalls, memory-bandwidth limits, pipeline bubbles — that create the illusion of idle hardware.

Why GPU Performance Is Not a Single Number

14/04/2026

AI GPU performance is multi-dimensional and workload-dependent. This article explains why scalar rankings collapse incompatible objectives and why 'best GPU' questions are structurally underspecified.

What a GPU Benchmark Actually Measures

14/04/2026

A benchmark result is not a hardware measurement — it is an execution measurement. The GPU, the software stack, and the workload all contribute to the number. Reading it correctly requires knowing which parts of the system shaped the outcome.

Why Spec‑Sheet Benchmarking Fails for AI

14/04/2026

GPU spec sheets describe theoretical limits. This article explains why real AI performance is an execution property shaped by workload, software, and sustained system behavior.

Cracking the Mystery of AI’s Black Box

4/02/2026

A guide to the AI black box problem, why it matters, how it affects real-world systems, and what organisations can do to manage it.

Inside Augmented Reality: A 2026 Guide

3/02/2026

A 2026 guide explaining how augmented reality works, how AR systems blend digital elements with the real world, and how users interact with digital content through modern AR technology.

Smarter Checks for AI Detection Accuracy

2/02/2026

A clear guide to AI detectors, why they matter, how they relate to generative AI and modern writing, and how TechnoLynx supports responsible and high‑quality content practices.

Choosing Vulkan, OpenCL, SYCL or CUDA for GPU Compute

28/01/2026

A practical comparison of Vulkan, OpenCL, SYCL and CUDA, covering portability, performance, tooling, and how to pick the right path for GPU compute across different hardware vendors.

Deep Learning Models for Accurate Object Size Classification

27/01/2026

A clear and practical guide to deep learning models for object size classification, covering feature extraction, model architectures, detection pipelines, and real‑world considerations.

TPU vs GPU: Which Is Better for Deep Learning?

26/01/2026

A practical comparison of TPUs and GPUs for deep learning workloads, covering performance, architecture, cost, scalability, and real‑world training and inference considerations.

CUDA vs ROCm: Choosing for Modern AI

20/01/2026

A practical comparison of CUDA vs ROCm for GPU compute in modern AI, covering performance, developer experience, software stack maturity, cost savings, and data‑centre deployment.

Best Practices for Training Deep Learning Models

19/01/2026

A clear and practical guide to the best practices for training deep learning models, covering data preparation, architecture choices, optimisation, and strategies to prevent overfitting.

Measuring GPU Benchmarks for AI

15/01/2026

A practical guide to GPU benchmarks for AI; what to measure, how to run fair tests, and how to turn results into decisions for real‑world projects.

GPU‑Accelerated Computing for Modern Data Science

14/01/2026

Learn how GPU‑accelerated computing boosts data science workflows, improves training speed, and supports real‑time AI applications with high‑performance parallel processing.

CUDA vs OpenCL: Picking the Right GPU Path

13/01/2026

A clear, practical guide to cuda vs opencl for GPU programming, covering portability, performance, tooling, ecosystem fit, and how to choose for your team and workload.

Performance Engineering for Scalable Deep Learning Systems

12/01/2026

Learn how performance engineering optimises deep learning frameworks for large-scale distributed AI workloads using advanced compute architectures and state-of-the-art techniques.

Choosing TPUs or GPUs for Modern AI Workloads

10/01/2026

A clear, practical guide to TPU vs GPU for training and inference, covering architecture, energy efficiency, cost, and deployment at large scale across on‑prem and Google Cloud.

GPU vs TPU vs CPU: Performance and Efficiency Explained

10/01/2026

Understand GPU vs TPU vs CPU for accelerating machine learning workloads—covering architecture, energy efficiency, and performance for large-scale neural networks.

Energy-Efficient GPU for Machine Learning

9/01/2026

Learn how energy-efficient GPUs optimise AI workloads, reduce power consumption, and deliver cost-effective performance for training and inference in deep learning models.
Back See Blogs
arrow icon