<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-03T15:52:43Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/0DCB6114-EC97-4802-B5CC-DF5D95549119" ns1:id="0DCB6114-EC97-4802-B5CC-DF5D95549119"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/persons/584DF82E-D99D-4DE7-9E82-52437789FF81" ns1:rel="PM_PER"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7933056D-4CE0-4C5D-94CC-2772B4662F3A" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/7933056D-4CE0-4C5D-94CC-2772B4662F3A" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/EC9326B5-50D8-459F-9932-1D39B5055FC9" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2026-03-30T23:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/01432353-CD0C-44D2-82E0-42402097B6A5" ns1:rel="FUND" ns1:start="2022-12-01T00:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">10038058</ns2:identifier></ns2:identifiers><ns2:title>Assessing the feasibility of a sensor-rich gait wearable and advanced behavioural science platform to improve care and rehabilitation outcomes for knee arthroplasty patients</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Collaborative R&amp;D</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Over 125,400 total KAs (TKAs) are carried out in the UK each years, costing the NHS ~&amp;pound;812M pa. Driven in part by an aging population and increase in conditions such as Rheumatoid arthritis (RA), Parkinson's Disease (PD) and Osteoarthritis (OA), the last 10 years have seen a 39% increase in cost, and a 33% increase in surgery delays, dramatically impacting patient's mobility, ability to stay active and quality of life.

Path Insight (PIn), is an advanced at-home gait monitoring system that combines a unique and patented, sensor-rich insoles and patient-adapting machine learning to accurately map, metricate and baseline a persons gait symmetry, pronation, supination, pressure distribution, balance, falls risk and significant changes in activity, amongst a range of other clinically accurate metrics.

From this, our platform can infer to high-degree of accuracy (validated) a number of clinically-interesting health indicators and biomarkers such as mobility, activity, falls risk assessment and measuring disease progression or therapeutic impact.

From this, patients can be stratified and prioritised for surgery or physiotherapy much more accurately, rather than it being on 'first-come, first-serve' basis currently, which leads to delays, creates regional and social inequalities and damages QoL for patients. Target NHS saving = &amp;pound;177m pa (~23% KA-NHS spend;2021).</ns2:abstractText></ns2:project>