NUMWRS™
Giving knee stability a language surgeons can read.
The n-u Motion Waveform Rating Scale describes how the knee moves side to side while bending and straightening — turning a movement trace into a small set of named patterns that reflect how ligaments control stability during motion.
This proposed study asks whether those patterns, derived from MAI Motion™, correspond to clinically meaningful differences in knee function and alignment behaviour.
n → s
Six Waveform Types
500
Recordings Analysed
12mo
Non-Interventional Study
The Clinical Challenge
Why do some patients never regain confident movement?
Total knee replacement provides effective pain relief, yet up to one in five patients remain dissatisfied — frequently because of ongoing instability or difficulty returning to normal activity. The unmet need is understanding why, after a technically successful operation, stable and confident movement does not follow.
134,562
Knee replacements recorded in the NHS in 2024 (NJR), costing over £1.1 billion annually
1 in 5
Patients remain dissatisfied after knee replacement, often due to instability
£300m
Added NHS cost each year from revision surgery, at roughly £15,000 per case
A snapshot, not a story
Traditional orthopaedic assessment relies on static imaging — X-rays or CT — which shows the joint in a fixed position and cannot reveal how it behaves during movement. Yet functional performance is increasingly recognised as the true measure of surgical success.
Alignment and stability are linked
Even minor malalignment can alter ligament balance, affect load distribution and compromise long-term implant performance. What is missing is a rapid, objective way to assess both motion and alignment in real-life contexts.
The Scale
Six letters for six ways a knee can move
Each letter names a distinct pattern seen in patients. The waveform shapes reflect how knee ligaments control stability during movement, providing an indirect but clinically meaningful marker of dynamic function.
n
Narrow, steady motion
Good control and stability
u
Slightly broader motion
Mild imbalance
m / w / s / r
Progressively more irregular patterns
Increasing instability or compensation
How the two fit together
MAI Motion™ captures the movement; NUMWRS™ classifies it into interpretable patterns. Together they are intended to translate complex biomechanical data into clear, objective measures of knee function.
Method & Aim
No new recordings.
Only new questions of old ones.
A non-interventional study using pre-existing motion video. A subset of 500 recordings, drawn from 3,500 sit-to-stand and squat tasks captured on standard cameras, will be reconstructed in three dimensions by MAI Motion™ and classified by NUMWRS™ into stability patterns from n through s. Motion–alignment correlation mapping will explore whether specific waveform types correspond to characteristic alignment profiles.
Correlations will be tested using ordinal and trend-based statistical methods, and a brief PPI review will ensure findings are communicated clearly and remain clinically meaningful.
WP1
Months 1–3
Protocol and dataset
Project initiation and coordination; selection of anonymised motion videos.
Deliverable — Standardised protocol and baseline dataset confirmed for analysis.
WP2
Months 4–6
Systematic analysis
Systematic video analysis using MAI Motion™; preliminary NUMWRS™ classification and pre-operative comparison.
Deliverable — Feasibility metrics — data quality, processing time, usability — demonstrating dataset suitability.
WP3
Months 7–9
Correlation testing
NUMWRS™ tested against MAI Motion analyses; classification workflow and algorithm parameters refined.
Deliverable — Dataset correlated with NUMWRS™ metrics.
WP4
Months 10–12
Dissemination and PPI
Preparation of RCSEd abstract, final report and journal manuscript; dissemination planning and a PPI session to review clarity.
Deliverable — Reproducible workflow and PPI summary report for future studies.
Success criteria
- Demonstrated correlation between NUMWRS™ patterns and functional alignment indicators.
- Defined reliability benchmarks for motion–alignment analysis.
- RCSEd report and peer-review manuscript completed.
Alignment with the College
Surgeons leading the responsible use of AI.
The project aligns with RCSEd’s commitment to advancing surgical science, innovation and patient-centred care — demonstrating how surgeons can lead the integration of artificial intelligence into clinical practice while maintaining ethical and educational standards.
Innovation in orthopaedic assessment
Translating motion science into practical surgical tools.
Surgeon-led research capacity
Empowering Fellows to bridge engineering and clinical domains.
Patient-centred improvement
Assessing function as patients actually experience it, not only as imaging depicts it.
Evidence-based digital health
Demonstrating responsible integration of AI into clinical practice.
Key Technical Risks
Named up front, with mitigations.
Analytical validity
NUMWRS™ patterns may not align precisely with measured joint stability.
Mitigation: Refine category thresholds through orthopaedic review.
Task variability
NUMWRS™ consistency may vary between movement types — sit-to-stand versus squat.
Mitigation: Test across both tasks and report variance.
Clinical interpretation
Surgeons may find waveform terminology unfamiliar.
Mitigation: Develop a visual reference and short interpretive guide.
Proposal-Stage Evidence Boundary
What the proposal does — and does not — establish.
This page describes a project proposal. It does not confirm that the study was funded, commenced or completed, and NUMWRS™ should not be read as a validated clinical scale.
Supported by the proposal
- A 12-month, £9,928 non-interventional study proposal using pre-existing, anonymised motion recordings.
- A subset of 500 recordings drawn from 3,500 sit-to-stand and squat tasks captured on standard cameras.
- MAI Motion™ is UKCA Class 1 and MHRA registered, with patents filed (PCT/GB2025/050722; UK 2404991.8) and trade mark UK00004072701.
- Three peer-reviewed publications underpin the motion-analysis method the scale is built on.
Not established by the proposal
- That NUMWRS™ waveform types have been shown to correspond to measured joint stability — this is the question the study asks.
- That the scale is validated, adopted, or in routine orthopaedic use.
- That motion-informed alignment strategies improve surgical outcomes or reduce revision rates.
- That the proposal was funded or the study has commenced.
Source provenance
- Funder
- Royal College of Surgeons of Edinburgh (RCSEd)
- Study type
- Non-interventional analysis of pre-existing recordings
- Proposed duration
- 12 months
- Proposed value
- £9,928
- Dataset
- 500 recordings selected from 3,500 sit-to-stand and squat tasks
- Platform
- MAI Motion™ — UKCA Class 1, MHRA registered
A structured language for motion
By establishing NUMWRS™ as a structured way to interpret movement, surgeons and researchers would gain a quantifiable way to describe knee function in real movement — improving consistency in assessment, strengthening training, and supporting earlier intervention.
On completion, results would be disseminated through RCSEd and peer-reviewed publication, providing the foundation for larger multi-centre validation.
Academic & Clinical Collaboration
We welcome orthopaedic surgeons, Fellows and research groups interested in motion-based assessment and multi-centre validation.