Prosthetics and orthotics are undergoing a major digital transformation. Three-dimensional scanning, CAD/CAM design, additive manufacturing, wearable sensors, advanced prosthetic control, virtual reality and artificial intelligence are changing how devices are designed, manufactured, fitted, learned, monitored and integrated into rehabilitation.
But the most technologically sophisticated prosthesis or orthosis is not automatically the best device for a person.
The central question remains: Does this technology improve comfort, function, participation, safety and long-term use in the person’s everyday life?
What are prosthetics and orthotics?
Prosthetics and orthotics are closely related rehabilitation disciplines.
Prosthetics concerns artificial devices designed to replace, in whole or in part, an absent limb or body segment.
Orthotics concerns external devices designed to support, protect, align, restrict or assist the function of an existing body segment.
Professionals may work with people affected by amputation, limb difference, neurological conditions, cerebral palsy, stroke, spinal cord injury, musculoskeletal injuries, fractures, scoliosis, joint instability, peripheral nerve injury, diabetic foot problems and other mobility or positioning needs.
Device selection and design need to consider not only anatomy, but also functional goals, skin integrity, strength, range of motion, sensation, activity level, environment, work and leisure, cognition, tolerance and personal preferences.
From plaster casting to digital workflows
Traditional fabrication often relies on plaster casting, manual measurements, foam impressions, physical modification of positive models and hand fabrication.
These methods remain clinically valuable.
Digital workflows can complement or replace selected steps.
A modern process may involve: 3D scan → digital model → CAD modification → simulation → manufacturing → fitting → training → functional evaluation → adjustment.
This changes more than fabrication. It creates a digital record that can potentially be stored, reproduced, compared over time, modified remotely and integrated with functional data.
Eight technologies transforming prosthetics and orthotics
1. 3D scanning
Three-dimensional scanning can capture the external geometry of a body segment.
Depending on the application, this may reduce the need for some conventional casting procedures.
Potential advantages include faster digital capture, less messy procedures, easier storage, reproducibility, remote collaboration and comparison between scans over time.
For a patient requiring a wrist orthosis, for example, the hand and forearm may be scanned and converted into a digital model.
The clinician can then modify borders, relief areas, thickness, openings and pressure zones.
But a scan only captures geometry. It does not automatically reveal pain, tissue tolerance, tone, movement quality, skin sensitivity or functional requirements.
The professional still needs to interpret the body, not merely scan it.
2. CAD/CAM design
Computer-aided design and manufacturing allow prosthetic and orthotic components to be modified digitally.
The clinician or technician may adjust thickness, contour, alignment, trim lines, ventilation, stiffness, reinforcement and joint positioning.
Digital models can also facilitate version control.
For example, instead of completely recreating a device, a previous design can be modified after growth, swelling changes, functional improvement, discomfort or new therapeutic goals.
Digital design therefore creates the possibility of a more iterative fitting process.
3. Additive manufacturing and 3D printing
Additive manufacturing makes it possible to transform digital models into physical devices.
Potential applications include upper-limb orthoses, hand splints, ankle-foot orthoses, prosthetic sockets, cosmetic covers, customized components and pediatric devices.
Recent evidence on 3D-printed upper-extremity immobilization devices suggests high patient satisfaction in many studies and generally favorable clinical outcomes, although the evidence remains heterogeneous.
This means that 3D printing should not be presented as automatically superior to conventional fabrication.
Its value depends on indication, design, material, manufacturing process, clinician expertise and patient characteristics.
4. Smart prostheses and myoelectric control
Advanced upper-limb prostheses may use electrical signals generated by residual muscles.
Surface electromyography can detect muscle activation and translate it into prosthetic commands.
Newer research explores high-density EMG, pattern recognition, machine learning, multimodal sensing and more intuitive control.
A prosthesis that works well during a laboratory test may still be difficult to use when the user is tired, sweating, changing arm position, carrying objects or working outdoors.
Real-world robustness matters as much as algorithmic accuracy.
5. Sensors inside prosthetic and orthotic devices
Sensors can potentially measure pressure, load, acceleration, orientation, gait cycles, activity, temperature and movement frequency.
For prosthetic users, socket pressure monitoring may help identify areas of excessive load.
For orthotic users, smart insoles or wearable sensors may provide information about gait, weight distribution, step symmetry and activity outside the clinic.
However, collecting pressure or movement continuously does not automatically tell clinicians what to change.
A clinically useful sensor system needs to produce information that is reliable, interpretable, relevant and actionable.
6. Artificial intelligence
AI can potentially contribute to prosthetic control, gait classification, fall-risk estimation, movement recognition, device adaptation and prediction of functional patterns.
Machine-learning algorithms may help transform complex sensor data into simpler clinical information.
Future systems could potentially adapt assistance according to speed, terrain, fatigue, user intention and activity.
But the more autonomous a system becomes, the greater the need for external validation, confidence estimation, safe fallback behaviour and clinician oversight.
7. Digital gait analysis
Prosthetic and orthotic fitting frequently involves observing gait.
Digital tools may add information about cadence, step length, symmetry, stance time, movement patterns and loading.
This can support questions such as: Is the alignment appropriate? Is the orthosis restricting too much movement? Is the patient compensating? Has gait changed after an adjustment?
But gait is complex. A more symmetrical gait is not automatically a better gait if achieving it increases pain, energy expenditure, instability or fatigue.
The patient’s functional priorities remain essential.
8. Virtual reality and digital rehabilitation around the device
The success of a prosthesis or orthosis does not depend only on fabrication.
The person must often learn how to use the device, trust it, integrate it into movement and progressively incorporate it into everyday activities.
This is where virtual reality can add a particularly interesting dimension.
VR can provide a safe and controlled environment in which the user learns to interact with the prosthesis or orthosis before facing more complex real-world situations.
Learning how to use a prosthesis in VR
A person using a new upper-limb prosthesis may need to learn how to activate different grips, coordinate residual-limb movement with prosthetic control, position the arm, apply the right amount of force, approach an object, release it and switch between functions.
A virtual environment can allow repeated practice with objects of different sizes, shapes, positions, simulated resistance and functional purposes.
The user might practice virtually picking up a cup, moving an object from one shelf to another, opening a container, carrying groceries, placing objects on a table or performing a sequence of household tasks.
The difficulty can increase progressively without the risk of breaking objects or creating unsafe situations.
Learning to use an orthosis
VR and interactive rehabilitation may also support the use of orthotic devices.
A patient wearing an ankle-foot orthosis may practice stepping, obstacle crossing, weight transfer, changing direction and walking in complex environments.
A person with an upper-limb orthosis may practice reaching, grasp preparation, bilateral tasks and functional positioning.
The therapist can then observe not simply whether the orthosis is worn, but how the person adapts their movement while using it.
Facilitating assimilation and embodiment
A new prosthesis or orthosis changes the relationship between the person and their body.
The user must often develop a new internal representation of where the device is, how it moves, what it can do, how much space it occupies and how to coordinate it with the rest of the body.
Repeated interaction inside immersive environments may help support this process of sensorimotor assimilation and embodiment.
For a prosthetic user, virtual feedback may help connect: intention → muscle activation → prosthetic movement → visual consequence.
Repeated exposure to this loop can help the person learn how their actions control the device.
Before the final device is available
Another interesting possibility is using virtual environments before the final prosthesis or orthosis has been fully delivered.
A patient may potentially begin learning movement strategies, device-control principles, functional sequences and environmental interaction.
This could help prepare the person for the physical device and reduce part of the initial learning burden after fitting.
Safe progression toward real-world rehabilitation
A possible progression is: simulation → supervised VR practice → controlled physical task → real-world activity.
For example, a lower-limb prosthetic user could first practice responding to obstacles in a controlled virtual scenario, then perform similar tasks in the rehabilitation gym, and later transfer them to community environments.
VR can therefore become a bridge between device fitting and functional participation.
The fit is digital—but the patient remains physical
One of the biggest risks of digital fabrication is becoming overly focused on the digital model.
A scan may appear perfect on screen.
But the real device interacts with skin, soft tissue, sweat, pressure, movement, clothing, fatigue and body-volume changes.
The fitting process therefore still requires physical examination, patient feedback, functional observation and repeated adjustment.
Digital precision does not eliminate biological variability.
Comfort is a clinical outcome
Prosthetic and orthotic technology is often evaluated through alignment, accuracy, mechanical properties and control performance.
But users may judge the device through different questions: Is it comfortable? Can I wear it for several hours? Can I put it on independently? Does it fit under clothing? Is it too heavy? Does it cause sweating? Does it look acceptable? Does it help me do what matters to me?
This is particularly important because even a technically advanced device has little value if it is not used.
Device abandonment matters
Advanced prosthetic systems sometimes show excellent technical capabilities but remain underused or abandoned.
Reasons can include discomfort, complexity, maintenance, weight, unreliable control, limited functional benefit, cosmetic preferences and charging requirements.
The goal of innovation should therefore not be simply more functions.
It should be more useful functions that the person actually wants and learns to use.
VR-based training may contribute here by allowing repeated practice, progressive familiarization and greater confidence before the user is exposed to more demanding real-world tasks.
Four realistic clinical scenarios
Custom wrist orthosis
A patient requires wrist stabilization after injury.
A digital workflow may include 3D scanning, digital modeling, modification of pressure areas, 3D printing, fitting, interactive or VR-assisted movement practice and functional evaluation.
The clinician still needs to verify comfort, joint position, pressure, skin response and function.
Lower-limb prosthesis
A person with lower-limb amputation receives a prosthesis.
Digital gait analysis and sensors may help assess symmetry, loading, activity and gait pattern.
VR can then add a controlled training phase in which the person practices weight shifting, stepping, obstacle avoidance, changes in direction and community-like environments.
The prosthetist and rehabilitation team can combine this information with pain, socket comfort, confidence, walking goals and community mobility.
Upper-limb myoelectric prosthesis
A person receiving a myoelectric prosthesis must learn to associate specific muscle activations with prosthetic actions.
A virtual representation of the prosthetic hand could provide immediate feedback.
For example: muscle activation → virtual grip → visual feedback → correction.
The patient can repeat the task many times before using the physical prosthesis for increasingly complex real-world activities.
The objective is to reduce cognitive effort and progressively make control more automatic.
Smart ankle-foot orthosis
A person with a neurological condition may use an orthotic device designed to influence foot clearance or gait.
Virtual scenarios could be used to practice stepping over obstacles, walking at different speeds, navigating crowded environments and responding to unexpected changes.
Future systems may adapt support dynamically, but intelligent adaptation should only be introduced when the sensing system is sufficiently reliable and failures can be managed safely.
Pediatric orthotics and growth
Children create an additional challenge: the body changes.
A digital model can potentially make it easier to store previous geometry, compare growth, modify a design and reproduce components.
But growth also means repeated clinical reassessment.
A child may need changes not simply because dimensions increased, but because movement, tone, activities or participation goals changed.
Interactive rehabilitation can also make adaptation to a new orthosis more engaging, especially when movement practice is integrated into age-appropriate games.
Rehabilitation after upper-limb prosthetic fitting
A sophisticated prosthetic hand may provide multiple grip patterns.
But the user must still learn when to select each grip, how to position the arm, how much force is needed and how to coordinate vision and prosthetic control.
Occupational therapy may involve tasks such as holding a cup, opening containers, dressing, carrying objects and manipulating tools.
VR can add a progression before and alongside these real tasks.
The therapist could first introduce simplified virtual objects, then increase object variety, task speed, sequence complexity, cognitive load and environmental distractions.
The goal is not to master the prosthesis as a machine.
It is to assimilate the prosthesis into meaningful occupation and everyday body use.
From device metrics to participation
| Metric | Possible value | Important limitation |
|---|---|---|
| Socket pressure | Interface loading | Pressure tolerance varies |
| Step symmetry | Gait comparison | Symmetry is not always optimal |
| Steps per day | Real-world activity | Does not explain quality |
| EMG classification accuracy | Prosthetic control | Lab accuracy may not transfer |
| Device wear time | Acceptance/use | Long use does not prove benefit |
| 3D fit accuracy | Manufacturing precision | Does not guarantee comfort |
| Battery duration | Practical usability | Depends on activity |
| Grip success | Task performance | Does not equal participation |
| VR task success | Device-learning performance | Must transfer to physical use |
The important progression is: device performance → device learning → functional activity → participation → quality of life.
Remote fitting and telerehabilitation
Digital models create opportunities for remote collaboration.
A local clinician could potentially acquire a scan while a specialized team reviews the design remotely.
Remote rehabilitation can also support device education, home practice, skin checks, gait review, functional follow-up and review of VR or digital practice data.
But remote systems should not eliminate necessary physical examination.
AI should support—not hide—clinical decisions
Future systems may automatically recommend socket adjustments, alignment changes, gait parameters and prosthetic control settings.
Such systems could be useful, but recommendations need to be understandable.
A clinician should be able to ask: What data produced this recommendation? How confident is the model? Was this population represented during training? What happens if the sensor is wrong?
The more a digital system influences physical movement or device behaviour, the more important transparency and safety become.
What does current evidence tell us?
Digital fabrication and smart devices are promising, but the evidence is not uniform.
VR and digital simulation add another promising area: device learning and functional integration.
However, improvement in a virtual task should not automatically be interpreted as successful prosthetic or orthotic integration.
The key question is whether training improves physical device control, real-world task performance, confidence, participation and sustained use.
The appropriate evidence pathway is: technical accuracy → fit and safety → device learning → functional performance → daily use → participation → long-term outcomes.
Ten questions before adopting a digital prosthetic or orthotic technology
- What functional problem are we trying to solve?
- Does digital technology improve the current workflow?
- Is the device comfortable and acceptable?
- Is the material appropriate for the clinical use?
- Are sensor measurements sufficiently reliable?
- Does the technology change a clinical decision?
- Can the device be easily adjusted?
- Can the person learn to use the device safely and efficiently?
- Does digital or VR training transfer to real-world use?
- Does the person actually want to use the device?
The future: from passive devices to adaptive rehabilitation systems
Prosthetic and orthotic technology is gradually moving from passive mechanical devices toward systems that can sense, interpret, respond and adapt.
A future prosthetic system may combine EMG, movement sensors, pressure, AI and real-time control.
A future orthosis may potentially change assistance according to gait phase or activity.
But the most important innovation may not be a single smart component.
It may be the integration of: digital design + smart device + VR-assisted learning + rehabilitation + longitudinal monitoring.
At Remotion, this broader rehabilitation perspective is particularly relevant.
A person using a prosthesis or orthosis may benefit from interactive and immersive activities targeting balance, reaching, gait, bilateral coordination, device-control learning, functional tasks, cognitive-motor challenges and confidence in complex environments.
Virtual environments can also allow therapists to create graded situations in which the patient learns how to integrate the device into movement before progressing toward more complex physical environments.
The prosthetic or orthotic device should therefore not be isolated from rehabilitation.
The future is not simply smarter devices.
It is smarter integration between the person, the device, the clinician and the rehabilitation environment.
Frequently asked questions
Is a 3D-printed orthosis better than a traditional orthosis?
Not automatically. 3D printing can improve customization and reproducibility, but clinical benefit depends on the indication, material, design and fitting.
Can a phone scan replace plaster casting?
In some workflows it may provide a useful digital model, but accuracy and suitability depend on the body segment, scanner and clinical requirements.
What is a myoelectric prosthesis?
It is a prosthesis controlled partly through electrical activity generated by the user’s muscles.
Can VR help someone learn to use a prosthesis?
Potentially, yes. VR can provide a controlled environment for learning device-control strategies, practicing functional tasks, increasing complexity progressively and building confidence before or alongside real-world practice.
Can VR help with orthotic rehabilitation?
Yes. Virtual environments can support gait, balance, reaching, obstacle negotiation and functional movement while the patient adapts to an orthotic device.
Can VR improve prosthesis assimilation or embodiment?
Potentially. Repeated visual and motor feedback may support the user’s learning of how the prosthesis responds to intention and movement. However, successful virtual performance must ultimately transfer to real device use and everyday function.
Can AI control a prosthetic hand?
Machine-learning systems can help interpret muscle or sensor signals and translate them into prosthetic commands, but reliability and real-world robustness remain important issues.
Can prosthetic and orthotic rehabilitation be done remotely?
Some follow-up, education and therapeutic activities can be supported remotely, but fitting, skin assessment or complex mechanical problems may still require in-person evaluation.
Selected references and further reading
- Clinical and functional outcomes of upper extremity 3D-printed immobilization devices compared to traditional methods: a systematic review with limited meta-analysis. 2026.
- Clinical Efficacy and Accuracy of 3D-Printed Medical Devices: A Systematic Review and Meta-Analysis. 2026.
- What is known from the existing literature about the treatment of mallet injury using 3D-printed splints? A scoping review. 2025.
- Quadrelli A et al. Advances in HD-EMG interfaces and spatial algorithms for upper limb prosthetic control. 2025.
- Wearable Technologies for Gait Instability Rehabilitation: Mechanisms, Clinical Evidence, and Future Directions. 2026.
- Advancements in Wearable Sensor Technologies for Health Monitoring in Terms of Clinical Applications, Rehabilitation, and Disease Risk Assessment: Systematic Review. 2026.
- Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention. 2026.
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review. 2026.