Falls Prevention, Gait and Mobility assessment
Ready, steady, go! It only takes a few second to perform a TUG test with QTUG™. Data are analyzed in real-time and results provided immedidately.
QTUG™ is very simple to use and uses sophisticated evidence-based analytics. The patented wearable sensor algorithm behind the QTUG™ technology provides immediate results, based on nine years of peer reviewed research. The technology has been extensively clinically validated through publication in top-tier international scientific journals.


Kinesis QTUG is an objective tool for identifying older adults at risk of falling. QTUG™ (Quantitative Timed Up and Go) is based on the Timed Up and Go test and is instrumented with wireless sensors placed on each leg. This technology provides a method for objective assessment of mobility, frailty and falls risk. It provides automatic analysis of patient data against average values for patients’ age and gender with colour coding to indicate deviations from normality.
QTUG™ is intended to assist those assessing falls risk, by providing a falls risk score (known as the Falls Risk Estimate (FRE)) and a frailty score (known as the Frailty Estimate) along with fast, accurate and objective data. QTUG™ also incorporates a questionnaire based on the American Geriatric Society (AGS) and British Geriatric Society (BGS) guidelines recording standard falls risk-factors.
Our products, QTUG™ (Quantitative Timed Up and Go) and Kinesis Gait™, falls risk and gait assessment technologies have been developed through nine years of research and extensively clinically validated through publication in top-tier international scientific journals.
Kinesis products have been shown to be Valid, Reliable and Accurate in measuring Gait and Mobility, while as well as assessing Falls in older adults.
The technology is based on nine years of peer reviewed research. Our technology uses advanced wearable sensors suitable for objective assessment of gait and mobility, measurement of response to rehabilitation and treatment and well as screening for falls risk, mobility impairment and frailty.
Kinesis Health Technologies Ltd are a spin-out from University College Dublin and the TRIL centre, a large ageing research project funded by Intel, GE Healthcare and the Irish government.
NICE, The UK National Institute of Health and Care Excellence have recently issued a briefing note on the QTUG™ technology and its utility in assessing falls risk and frailty. Click here to read the briefing note.

Erin Smith, Lorcan Walsh, Julie Doyle, Barry Greene, Catherine Blake
Geriatrics & Gerontology International 2016. In press. DOI: 10.1111/ggi.12845

Barry R. Greene, Stephen J. Redmond, Brian Caulfield
IEEE J. Biomed Health Inform 2016. 21(3). DOI: 10.1109/JBHI.2016.2539098

Barry R. Greene, Stephanie Rutledge, Iain McGurgan, Christopher McGuigan, Karen O’Connell, Brian Caulfield, Niall Tubridy
IEEE J. Biomed Health Inform 2015. Jul;19(4):1356-61.

Erin Smith,Lorcan Walsh, Julie Doyle, Barry Greene, Catherine Blake
Gait & Posture 2016, (43): 239-244

Barry R. Greene, Emer P Doheny, Rose A. Kenny and Brian Caulfield
Physiological Measurement, 2014, 35 (10), 2053

Barry R. Greene, Emer P Doheny, Aisling O’Halloran, Rose A. Kenny
Age and Ageing, 2014, 43(3): 406-411

B. R. Greene, E. P. Doheny, C. W. Walsh, C. Cunningham, L. Crosby, and R. A. Kenny
Gerontol. 58(5), 2012

Barry R. Greene, Alan O’Donovan, Roman Romero-Ortuno, Lisa Cogan, Cliodhna Ni Scanaill, Rose A. Kenny
IEEE Trans. Biomed. Eng. 2010. 57(12): p. 2918-26

B. R. Greene, R. A. Kenny
IEEE Trans. Biomed. Eng. 59(4) p988-995, 2012
Denise McGrath, B. R. Greene, K.J. Sheehan, L. Walsh, R. A. Kenny, B. Caulfield
Eur. J. Appl. Physiol. 2015 Feb;115(2):437-49
Emer P. Doheny, Barry R. Greene, Cathal Walsh, Timothy Foran, Clodagh Cunningham, Chie Wei Fan and Rose Anne Kenny
Gait and Posture, 2013, I38(4): 1021-1025
Greene BR, Foran T, McGrath D, E.P. Doheny, Burns A. Caulfield, B.
Journal of Applied Biomechanics; 28(3):349-55, 2012
KJ Sheehan, BR Greene, C Cunningham, L Crosby, RA Kenny
Gait & Posture, 2014, 39(4): 1034-1039
Emer P. Doheny, Denise McGrath, Massimiliano Ditroilo; Jacqueline Mair, Barry R. Greene, Brian Caulfield, Giuseppe De Vito, Madeleine Lowery
Annals of Biomedical Engineering, 2013, 41(8): 1748-1757
Barry R. Greene, Denise McGrath, Lorcan Walsh, Emer P. Doheny, David McKeown, Chiara Garattini, Clodagh Cunningham, Lisa Crosby, Brian Caulfield, Rose A. Kenny
Phys Meas. 33 (2012) 2049–2063
B. R. Greene, D. McGrath, R. O’Neill, K. J. O’Donovan, A. Burns, and B. Caulfield
Medical & Biological Engineering & Computing, vol. 48, Issue 12 (2010), p. 1251
Denise McGrath, Barry R. Greene, Cathal Walsh, Brian Caulfield
J. Biomech. 44, 1083-1088 (2011)
Emer P. Doheny, Barry R. Greene, Timothy Foran, Clodagh Cunningham, Chie Wei Fan and Rose Anne Kenny
Phys Meas, 33(3), p361, 2012
Denise McGrath, Barry R. Greene, Brian Caulfield
Journal of Sports Biomechanics July 2012, p1-7
Ni Scanaill, C., Garattini, C., Greene, B. R., & McGrath, M. J.
Ageing International, vol 35, No. 4. 2010
Adrian Burns, Barry R. Greene, Michael J. McGrath, Terrance J. O’Shea, Benjamin Kuris, Steven M. Ayer, Florin Stroiescu, Victor Cionca
IEEE Sensors, Volume: 10, Issue: 9 (2010), 1527-1534
Keep in mind the following usage while performing the test.
To determine why the sensor does not connect to the tablet, you should do the following:
To identify why the test keeps failing, you should do the following:
If you are getting strange behaviour from QTUG™, you should do the following steps:
Check if the tablet battery is fully charged by plugging into a wall socket.
If you are getting strange behaviour from QTUG™, you should do the following steps:
The messages, ‘Cannot find Left sensor’ and ‘Cannot find Right sensor’ usually arise when one of the sensors (LEFT sensor or RIGHT sensor) is switched off, or the battery of a given sensor is flat.
The message, ‘Too many or not enough sensors connected’ usually arises when one or both of the sensors (LEFT sensor or RIGHT sensor) are not paired to the tablet.
If sensors from multiple QTUG™ kits have been swapped and need to be paired again, follow the instructions below (also on page 16 of QTUG™ user guide)
QTUG sensor data are anonymised and can be back-up automatically via Wi-Fi. More information can found in the QTUG™ user guide
Yes. Both Kinesis Gait™ and QTUG™ products can use the same sensors and tablet device.
Assessment of mobility and falls risk and frailty using QTUG™ should take no longer than 10 minutes.
Yes they can. However, we recommend that patients don’t use a mobility aid when doing the test as QTUG™ is more accurate when used without a mobility aid. QTUG™ will still work with a mobility aid but may underestimate falls risk and mobility impairment.
The Falls risk estimate produced by QTUG™ is a statistical estimate of the patients’ current risk of having a fall and is based on a model of community dwelling older adults.
The Frailty estimate produced by QTUG™ is a statistical estimate of the patients’ current level of Frailty based on Fried’s frailty phenotype derived from a statistical model of community dwelling older adults.
The QTUG™ comparison to reference data is a statistical comparison the patients’ mobility against average values for their age-group and gender based on a large reference population containing a variety of patients.
QTUG™ can be used to assess gait and mobility in patients suffering from Multiple Sclerosis, Parkinson’s and other patients with gait disorders.
QTUG™ can be used to assess patients in the home under supervised conditions. Take care to ensure the 3m walking distance is accurately measured and that the underfoot conditions are suitable for the test (e.g. free of clutter, no loose carpeting, at least 3.5 metres of uninterupted linear space).
To identify the QTUG™ or Kinesis Gait version on your device, you should do the following:
Accurate assessment of falls riskPrevent falls through more accurate assessment and referral!
Quantitative Timed Up and Go (QTUG™) provides Falls risk score (a validated risk profile of a patients’ future risk of having a fall). QTUG™ also provides an estimate of Frailty. The Falls risk and Frailty scores have been extensively validated by nine years of research.
Comparison to reference populationQTUG™ can identify specific gait and mobility problems by automatic comparison of patient’s mobility (e.g. standing, walking turning) against average values for age and gender. The results are colour coded to highlight gait and mobility impairment.
Trend TUG time, falls risk and frailty over timeQTUG™ can be used to determine how a patient responds to intervention ,
therapy or medication by analyzing falls risk assessment and frailty scores as well as gait and mobility data across multiple assessments. Trend Falls risk, Frailty and TUG test data over time as well as view all historical tests.
Gait & Mobility assessmentQuantitative assessment of each phase of the TUG (Timed Up and Go) test including, standing, sitting, walking and turning. Customise results by pinning most relevant data to the summary results screen. Data are colour-coded to highlight any statistical deviations in mobility from reference values.