Advancing Digital Approaches to Prosthetic Care
Augmene is an independent research and development initiative developing a smartphone-based multi-view capture and reconstruction system designed to generate geometric models of residual limb anatomy. The project applies established computer vision and photogrammetry techniques using widely available smartphone hardware. The capture and reconstruction pipeline is currently operational in controlled development environments and is undergoing ongoing refinement and evaluation. The system has not been clinically validated and no regulatory approval has been obtained.
Access Barriers in Prosthetic Rehabilitation
Prosthetic socket fabrication relies on an established clinical workflow that has been refined over many decades. It is a process that demands specialist expertise and repeated in-person contact — and for many patients, particularly those in geographically remote or under-resourced settings, these demands create significant barriers to care.
A prosthetist must physically assess the residual limb, take a cast or manual measurements, and apply clinical judgement informed by direct patient contact. This process cannot be trivially digitised or automated. However, some aspects of the pathway — such as early geometric assessment, referral preparation, and documentation — may benefit from digital support, provided the methods are sufficiently validated.
Augmene develops digital capture and reconstruction tools designed to contribute to certain stages of this pathway, without seeking to replace specialist clinical assessment. The focus is on geometric documentation and early referral preparation — areas where digital support may complement, rather than replicate, clinical expertise.
Geographic Distance from Services
In many regions, specialist prosthetic clinics are concentrated in urban centres. For patients in rural or remote areas, repeated travel for assessment, fitting, and adjustment imposes a significant practical burden.
Shortage of Qualified Prosthetists
Qualified prosthetists are unevenly distributed globally, with pronounced shortfalls in low- and middle-income countries. Training capacity and workforce development remain long-term challenges in the field.
Iterative and Time-Intensive Fitting
Socket fabrication and adjustment typically involves multiple appointments over weeks or months. Each visit requires clinical attendance, placing demands on both patient time and clinical resource.
Financial and Systemic Access Barriers
Beyond distance, financial cost, insurance coverage, and healthcare system capacity limit access to prosthetic rehabilitation, particularly in lower-income settings where provision may be inconsistent or absent.
Specialist Fabrication Requirements
Socket fabrication requires not only clinical assessment but skilled manual craftsmanship. The combination of technical expertise, equipment, and materials needed constrains the number of centres able to offer comprehensive prosthetic services.
Smartphone-Based Geometric Capture & Reconstruction
The Augmene system applies multi-view image capture using a standard smartphone, combined with photogrammetric reconstruction techniques, to generate geometric data about residual limb morphology. The following describes the implemented pipeline currently in prototype operation.
Multi-View Image Capture
A structured sequence of overlapping images is captured by moving a smartphone in a controlled arc around the subject. Adequate frame overlap, consistent lighting, and full surface coverage are each critical to downstream reconstruction quality.
Feature Matching & Pose Estimation
Feature detection algorithms identify and match corresponding points across overlapping frames. Camera pose estimation — recovering the position and orientation of each captured view — provides the geometric foundation for three-dimensional reconstruction.
3D Surface Reconstruction
Using multi-view geometry, the system generates a dense representation of the captured surface. Reconstruction quality and dimensional accuracy are being refined through systematic evaluation across varying capture conditions.
Geometric Assessment
The reconstructed geometry is analysed for dimensional characteristics relevant to prosthetic assessment workflows, including surface contour and spatial extent. Accuracy and repeatability are being characterised through controlled testing, with clinical applicability subject to further validation.
Capture quality is sensitive to lighting conditions, camera motion, surface texture, and coverage completeness. Ongoing development work is focused on improving robustness and repeatability across these variables.
Technical Foundation
The Augmene prototype applies established methods from computer vision and photogrammetry, implemented for use with consumer smartphone hardware. The following describes the key technical components of the system and the areas of active development and evaluation.
Feature Detection & Pose Estimation
Local feature detection algorithms identify distinctive points in each image frame. Matching these features across overlapping views allows the recovery of relative camera positions — a process known as camera pose estimation — which underpins the entire reconstruction pipeline.
Smartphone Optical Sensors
Consumer smartphone cameras provide the optical hardware for the Augmene prototype. The system uses standard smartphone optics — without depth sensors, structured light, or physical calibration targets — and is evaluated for geometric reliability under controlled capture conditions.
Multi-View Photogrammetry
The prototype implements structure-from-motion and dense multi-view stereo principles — established methods used in surveying, heritage documentation, and medical imaging. The system adapts these techniques to the specific challenges of soft tissue surfaces, handheld capture, and scale estimation without physical reference objects.
Surface Reconstruction Pipelines
Captured image data is processed to generate dense point clouds and polygon mesh representations. Output quality is highly sensitive to input image consistency, coverage completeness, and the photometric properties of the target surface. Occlusion, soft tissue variability, and motion blur represent known degradation factors under active investigation.
Dimensional Assessment & Accuracy
Reconstructed geometry is assessed for dimensional characteristics relevant to prosthetic assessment, including surface contour and approximate volume estimates. Establishing the repeatability and accuracy of these measurements relative to established clinical reference methods is an active and central research objective.
Controlled Evaluation
The prototype is evaluated against reference measurements using controlled test objects and geometric benchmarks. Known performance factors — including sensitivity to lighting conditions, incomplete surface coverage, motion blur, and scale estimation — are being systematically characterised through structured testing. No clinical testing has been conducted.
Prototype Capture & Reconstruction
The demonstration below shows the Augmene prototype system performing a multi-view image capture sequence on a controlled, non-clinical test model. The purpose is to communicate the general capture methodology and reconstruction concept — not to demonstrate a clinically validated workflow.
Prototype Demonstration
Smartphone image acquisition and multi-view reconstruction on a controlled non-clinical test model
Prototype demonstration: multi-view smartphone capture and surface reconstruction on a controlled test object. This footage is illustrative of the research methodology. Capture accuracy, reconstruction quality, and output reliability remain under investigation.
Interactive Reconstruction Example
The viewer below displays a three-dimensional reconstruction generated from a prototype multi-view smartphone capture sequence. This model illustrates the type of geometric output produced by the Augmene reconstruction pipeline.
Example reconstruction output generated from a prototype capture sequence. Model shown for demonstration purposes using a controlled non-clinical test object.
Future Development & Evaluation
Augmene is designed to complement — not replace — established clinical assessment. The following describes areas targeted for future development and evaluation. If validated through further testing and clinical collaboration, the system may support specific aspects of prosthetic consultation workflows. These are development objectives, not established or clinically validated outcomes.
Supporting Early Assessment
Digital limb geometry is being evaluated for its utility in preliminary documentation and early referral preparation — providing a clinical team with contextual geometric information ahead of in-person consultation. This is a targeted development objective and would not constitute clinical assessment in itself.
Remote Consultation Preparation
In contexts where travel to a specialist clinic is difficult, the system is being developed to provide geometric data that could help a prosthetist review anatomical information remotely before a scheduled in-person visit. Clinical utility in this context is an active area of evaluation, subject to validation through appropriate clinical collaboration.
Digital Fabrication Workflow Input
Some prosthetic services already use computer-aided design and digital fabrication tools. The Augmene system is being developed to produce geometry in formats compatible with such workflows — subject to achieving the required dimensional fidelity and appropriate clinical and regulatory requirements.
Research & Development Progress
Transparent communication about the current stage of the project is important. The following is an honest account of where the work stands.
March 2026
Augmene is currently in Stage 2: prototype development and controlled testing. The image capture and reconstruction pipeline is operational in a controlled laboratory setting using non-clinical test models. No clinical environments, human subjects, or patient data have been involved in testing to date, and no clinical trials or validation studies have been conducted.
Current research activity is focused on: assessing the geometric accuracy and repeatability of smartphone-derived 3D reconstructions against calibrated reference measurements; characterising the conditions under which reliable captures can be obtained; and identifying the factors — including lighting variability, surface texture, motion, and incomplete coverage — that degrade reconstruction quality.
This work is being conducted openly, and engagement from clinicians, prosthetists, researchers, and engineers with relevant expertise is actively welcomed. The project does not claim to have solved the technical or clinical challenges involved. Constructive critique is considered a contribution to the research process.
An Independent Research & Development Initiative
Augmene was initiated as an independent research and development effort applying widely available smartphone technology to capture geometrically useful data about residual limbs, and developing the tools to make that data useful in supporting prosthetic assessment workflows.
The project does not position itself as a finished product, a replacement for clinical expertise, or a challenge to established prosthetic practice. It is an active engineering and development programme — one that recognises the complexity of the domain it operates in and the expertise required to work responsibly within it.
Augmene operates without institutional affiliation. The work is guided by a commitment to scientific honesty, clear acknowledgement of limitations, and constructive engagement with the clinical and technical communities whose scrutiny the project actively invites.
Get In Touch
Enquiries are welcome from clinicians, prosthetists, researchers, rehabilitation engineers, healthcare organisations, and anyone with a genuine interest in the project. Whether you have technical questions, clinical perspectives to share, or wish to discuss collaboration, we are glad to hear from you.
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